././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011029.0024385 emmet_core-0.87.2/0000755000175100017510000000000015240717725013407 5ustar00runnerrunner././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011029.0012214 emmet_core-0.87.2/PKG-INFO0000644000175100017510000000401615240717725014505 0ustar00runnerrunnerMetadata-Version: 2.4 Name: emmet-core Version: 0.87.2 Summary: Core Emmet Library Author-email: The Materials Project License-Expression: BSD-3-Clause-LBNL Project-URL: Homepage, https://github.com/materialsproject/emmet Project-URL: Documentation, https://materialsproject.github.io/emmet/ Requires-Python: >=3.11 Requires-Dist: pymatgen>=2024.6.10 Requires-Dist: monty>=2024.2.2 Requires-Dist: pydantic>=2.12 Requires-Dist: pydantic-settings>=2.0 Requires-Dist: pymatgen-io-validation>=0.1.1 Requires-Dist: pybtex~=0.24 Requires-Dist: typing-extensions>=3.7 Requires-Dist: blake3 Requires-Dist: inflect Requires-Dist: pubchempy Provides-Extra: all Requires-Dist: seekpath>=2.0.1; extra == "all" Requires-Dist: pymatgen-analysis-defects>=2024.7.18; extra == "all" Requires-Dist: pymatgen-analysis-diffusion>=2024.7.15; extra == "all" Requires-Dist: pymatgen-core<2026.7.27; extra == "all" Requires-Dist: pymatgen-analysis-alloys>=0.0.6; extra == "all" Requires-Dist: pyarrow; extra == "all" Requires-Dist: pymongo; extra == "all" Requires-Dist: pycodcif; extra == "all" Requires-Dist: lobsterpy; extra == "all" Requires-Dist: phonopy>=3.0.0; extra == "all" Provides-Extra: test Requires-Dist: pre-commit; extra == "test" Requires-Dist: pytest; extra == "test" Requires-Dist: pytest-cov; extra == "test" Requires-Dist: pytest-xdist; extra == "test" Requires-Dist: pycodestyle; extra == "test" Requires-Dist: pydocstyle; extra == "test" Requires-Dist: flake8; extra == "test" Requires-Dist: mypy; extra == "test" Requires-Dist: mypy-extensions; extra == "test" Requires-Dist: types-setuptools; extra == "test" Requires-Dist: types-requests; extra == "test" Requires-Dist: wincertstore; extra == "test" Requires-Dist: custodian; extra == "test" Provides-Extra: docs Requires-Dist: sphinx<9.0.0; extra == "docs" Requires-Dist: sphinx-rtd-theme>=3.0.2; extra == "docs" Requires-Dist: sphinx-markdown-builder>=0.6.8; extra == "docs" Requires-Dist: sphinx-autodoc-typehints; extra == "docs" Requires-Dist: myst-parser; extra == "docs" ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011028.9519863 emmet_core-0.87.2/dev_scripts/0000755000175100017510000000000015240717725015734 5ustar00runnerrunner././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8148534 emmet_core-0.87.2/dev_scripts/gen_api_field_test.py0000644000175100017510000000217315240717720022110 0ustar00runnerrunner"""Generate test for consistency of document models used in API.""" from mp_api.client import MPRester import json from importlib_resources import files from pathlib import Path test_file = Path( files("emmet.core") / ".." / ".." / "tests" / "test_model_fields.py" ).resolve() document_models = {rester.document_model for rester in MPRester()._all_resters} model_fields = {} py_str = '"""Ensure that document models used in API do not change fields."""' py_str += """ import pytest from importlib import import_module """ for doc_model in sorted(document_models, key=lambda x: f"{x.__module__}.{x.__name__}"): model_fields[f"{doc_model.__module__}.{doc_model.__name__}"] = list( doc_model.model_fields ) py_str += f"ref_model_fields = {json.dumps(model_fields,indent=2)}\n" py_str += """ @pytest.mark.parametrize('import_str,ref_fields',ref_model_fields.items()) def test_model_field_drift(import_str, ref_fields): module, class_name = import_str.rsplit(".",1) model_class = getattr(import_module(module),class_name) assert set(model_class.model_fields) == set(ref_fields) """ test_file.write_text(py_str) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8149128 emmet_core-0.87.2/dev_scripts/generate_enums.py0000644000175100017510000001556315240717720021314 0ustar00runnerrunner"""Module to define various calculation types as Enums for VASP.""" from __future__ import annotations from importlib.resources import files as import_resource_files from itertools import product from ruamel.yaml import YAML _BASE_ENUM_PATH = { "vasp": import_resource_files("emmet.core.vasp"), "qchem": import_resource_files("emmet.core.qchem"), } for code_base in _BASE_ENUM_PATH: _BASE_ENUM_PATH[code_base] /= "calc_types" def get_enum_source( enum_name: str, doc: str, members: dict, enum_class: str = "ValueEnum" ) -> str: """Write python-format enum from a dict of members and metadata. Parameters ----------- enum_name : str Name of the enum. doc : str Enum docstr members : dict The key-value pair indexed members of the enum. enum_class : str = "ValueEnum" The name of the enum class. Returns -------- str """ items = [f"class {enum_name}({enum_class}):", f' """ {doc} """\n'] items += [f' {const} = "{val}"' for const, val in members.items()] return "\n".join(items) def string_bulk_replace(string: str, rules: dict[str, str]) -> str: """Perform multiple string replacements subject to a set of rules. Parameters ----------- string : str input string rules : dict[str,str] A dict of string replacements, with the characters to replace as keys, and their replacements as values. Returns -------- str """ for targ_char, rep_char in rules.items(): string = string.replace(targ_char, rep_char) return string def generate_vasp_enums_file(enum_file_name: str | None = None) -> None: """ Generate VASP enum members from reference yaml data. Parameters ----------- enum_file_name : str Name of the file to write the enums to. Defaults to _BASE_ENUM_PATH / vasp_enums.json.gz """ with open(_BASE_ENUM_PATH["vasp"] / "calc_types.yaml", "r") as config: _RUN_TASK_TYPE_DATA = YAML().load(config) _TASK_TYPES = _RUN_TASK_TYPE_DATA.get("TASK_TYPES") _RUN_TYPES = set( rt for functionals in _RUN_TASK_TYPE_DATA.get("RUN_TYPES", {}).values() for rt in functionals ) _RUN_TYPES.update(set(f"{rt}+U" for rt in _RUN_TYPES)) _ENUMS = { "RunType": { "_".join(rt.split()).replace("+", "_").replace("-", "_"): rt for rt in _RUN_TYPES }, "TaskType": {"_".join(tt.split()): tt for tt in _TASK_TYPES}, "CalcType": { f"{'_'.join(rt.split()).replace('+','_').replace('-','_')}" f"_{'_'.join(tt.split())}": f"{rt} {tt}" for rt, tt in product(_RUN_TYPES, _TASK_TYPES) }, } docstr = {} for enum_name in _ENUMS: rtc_type = enum_name.split("Calc")[-1].split("Type")[0].lower() if len(rtc_type) > 0: rtc_type += " " docstr[enum_name] = f"VASP calculation {rtc_type}types." enum_file_name = enum_file_name or str(_BASE_ENUM_PATH["vasp"] / "enums.py") with open(enum_file_name, "w+") as f: f.write( """\"\"\" Autogenerated Enums for VASP RunType, TaskType, and CalcType. Do not edit this by hand to add or remove enums. Instead, edit dev_scripts/generate_enums.py and/or emmet/core/vasp/calc_types/calc_types.yaml \"\"\" from emmet.core.types.enums import ValueEnum, IgnoreCaseEnum """ ) enum_order = ( "RunType", "TaskType", "CalcType", ) for ienum, enum_name in enumerate(enum_order): sorted_enums = {k: _ENUMS[enum_name][k] for k in sorted(_ENUMS[enum_name])} f.write( get_enum_source( enum_name, docstr[enum_name], sorted_enums, enum_class=( "IgnoreCase" if enum_name in ["RunType", "CalcType"] else "Value" ) + "Enum", ) ) f.write("\n\n" if ienum < (len(enum_order) - 1) else "\n") def generate_qchem_enum_file(enum_file_name: str | None = None) -> None: """ Generate QChem enum members from reference yaml data. Original author, Evan Spotte-Smith Parameters ----------- enum_file_name : str Name of the file to write the enums to. Defaults to _BASE_ENUM_PATH / qchem_enums.json.gz """ with open(_BASE_ENUM_PATH["qchem"] / "calc_types.yaml", "r") as config: _calc_type_meta = YAML().load(config) _calc_type_meta["FUNCTIONALS"] = [ rt for functionals in _calc_type_meta["FUNCTIONAL_CLASSES"].values() for rt in functionals ] _LOTS = list() for funct in _calc_type_meta["FUNCTIONALS"]: for basis in _calc_type_meta["BASIS_SETS"]: for solv_model in _calc_type_meta["SOLVENT_MODELS"]: _LOTS.append(f"{funct}/{basis}/{solv_model}") _lot_str_replacements = { "+": "_", "-": "_", "(": "_", ")": "_", "/": "_", "*": "_d", } _ENUMS = { "LevelOfTheory": { "_".join(string_bulk_replace(lot, _lot_str_replacements).split()): lot for lot in _LOTS }, "TaskType": { "_".join(tt.split()).replace("-", "_"): tt for tt in _calc_type_meta["TASK_TYPES"] }, "CalcType": { ( "_".join(string_bulk_replace(lot, _lot_str_replacements).split()) + f"_{'_'.join(tt.split()).replace('-', '_')}" ): f"{lot} {tt}" for lot, tt in product(_LOTS, _calc_type_meta["TASK_TYPES"]) }, } docstr = { "LevelOfTheory": "Levels of theory for calculations in Q-Chem.", "TaskType": "Calculation task types for Q-Chem.", "CalcType": "Calculation types (LOT + task type) for Q-Chem.", } enum_file_name = enum_file_name or str(_BASE_ENUM_PATH["qchem"] / "enums.py") with open(enum_file_name, "w+") as f: f.write( """\"\"\" Autogenerated Enums for Q-Chem LevelOfTheory, TaskType, and CalcType. Do not edit this by hand to add or remove enums. Instead, edit dev_scripts/generate_enums.py and/or emmet/core/qchem/calc_types/calc_types.yaml \"\"\" from emmet.core.types.enums import ValueEnum """ ) enum_order = ( "LevelOfTheory", "TaskType", "CalcType", ) for ienum, enum_name in enumerate(enum_order): sorted_enums = {k: _ENUMS[enum_name][k] for k in sorted(_ENUMS[enum_name])} f.write(get_enum_source(enum_name, docstr[enum_name], sorted_enums)) f.write("\n\n" if ienum < (len(enum_order) - 1) else "\n") if __name__ == "__main__": generate_vasp_enums_file() generate_qchem_enum_file() ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8149128 emmet_core-0.87.2/dev_scripts/generate_identifier_exclude_list.py0000644000175100017510000000545415240717720025051 0ustar00runnerrunnerimport os from pathlib import Path import requests from emmet.core.mpid import AlphaID, VALID_ALPHA_SEPARATORS GH_OBSCENITY = "https://raw.githubusercontent.com/LDNOOBW/List-of-Dirty-Naughty-Obscene-and-Otherwise-Bad-Words/master/" LANGUAGES = [ "ar", "cs", "da", "de", "en", "eo", "es", "fa", "fi", "fil", "fr", "fr-CA-u-sd-caqc", "hi", "hu", "it", "ja", "kab", "ko", "nl", "no", "pl", "pt", "ru", "sv", "th", "tlh", "tr", "zh", ] DEFAULT_FILE = ( Path(__file__).parent / ".." / "emmet" / "core" / "_forbidden_alpha_id.py" ) def generate_exclude_list( github_url: str = GH_OBSCENITY, languages: list[str] = LANGUAGES, output_file: str | Path = DEFAULT_FILE, ) -> list[int]: """Generate a list of integers corresponding to multi-lingual obscene words. Parameters ----------- github_url: str = GH_OBSCENITY The github repo URL to fetch obscene word lists from languages: list[str] = LANGUAGES The languages in the repo to select word lists for output_file: str | Path = DEFAULT_FILE The file to write the integer list to Returns ----------- list of int AlphaID integer values of those words/phrases """ obscene_alpha_ids = set() for lang in languages: for word in ( requests.get(f"{os.path.join(github_url,lang)}") .content.decode() .splitlines() ): for sep in VALID_ALPHA_SEPARATORS | {" "}: word = word.replace(sep, "") alpha_id = None try: alpha_id = AlphaID(word) except Exception: continue # Only retain "obscene AlphaIDs" that # are longer than two characters, with leading "a"'s removed if len(AlphaID(int(alpha_id))) > 2: obscene_alpha_ids.update({alpha_id}) if any(alpha_id._prefix for alpha_id in obscene_alpha_ids): badly_parsed = "\n".join( alpha_id._identifier for alpha_id in obscene_alpha_ids if alpha_id._prefix ) raise ValueError( "None of the obscene AlphaIDs should have prefixes or separators. " "Please check the following strings, which have been parsed incorrectly:\n" f"{badly_parsed}" ) obscene_alpha_id = sorted(obscene_alpha_ids) output_str = """\"\"\"Define AlphaID integer values which cannot be used.\"\"\" FORBIDDEN_ALPHA_ID_VALUES : set[int] = { """ output_str += ( ",\n".join(f" {int(alpha_id)}" for alpha_id in obscene_alpha_id) + "\n}" ) with open(output_file, "w+") as f: f.write(output_str) return obscene_alpha_id if __name__ == "__main__": _ = generate_exclude_list() ././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1787011028.944221 emmet_core-0.87.2/emmet/0000755000175100017510000000000015240717725014516 5ustar00runnerrunner././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1787011028.963619 emmet_core-0.87.2/emmet/core/0000755000175100017510000000000015240717725015446 5ustar00runnerrunner././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8150523 emmet_core-0.87.2/emmet/core/__init__.py0000644000175100017510000000062515240717720017555 0ustar00runnerrunner""" Core module exposes the document interfaces These will be ingested via Drones, built by Builders, and served via the API. """ from importlib.metadata import PackageNotFoundError, version from importlib.util import find_spec ARROW_COMPATIBLE = find_spec("pyarrow") try: __version__ = version("emmet-core") except PackageNotFoundError: # pragma: no cover # package is not installed pass ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8151078 emmet_core-0.87.2/emmet/core/_forbidden_alpha_id.py0000644000175100017510000006243615240717720021742 0ustar00runnerrunner"""Define AlphaID integer values which cannot be used.""" FORBIDDEN_ALPHA_ID_VALUES: set[int] = { 724, 1310, 1729, 1883, 1884, 1978, 2143, 3386, 3393, 3494, 3598, 4075, 4431, 5107, 5261, 7290, 7299, 7967, 8481, 8799, 8886, 9326, 10160, 10358, 10366, 10523, 11252, 12295, 12386, 12520, 12797, 13071, 16169, 18774, 20084, 23140, 23482, 27080, 27405, 31609, 35204, 40251, 40413, 40423, 40950, 42815, 44678, 44954, 44993, 48972, 49029, 58198, 64595, 66638, 67002, 87997, 88373, 88426, 93340, 93350, 93444, 93556, 93568, 93632, 93827, 97422, 101462, 108220, 119432, 132617, 136998, 164267, 175826, 181432, 184929, 185581, 189756, 202878, 202898, 203261, 207197, 210923, 211224, 214141, 216611, 218365, 220676, 220714, 220720, 224770, 224944, 228778, 229146, 234390, 240808, 242090, 253786, 257736, 258734, 263651, 263908, 263992, 266436, 266634, 269318, 269386, 269446, 269510, 269561, 273223, 273234, 273473, 273481, 273487, 273559, 276668, 277654, 277658, 292045, 294956, 299186, 301619, 302042, 311019, 311250, 316434, 317054, 317739, 319228, 319234, 319684, 319694, 321327, 322040, 322044, 323927, 324343, 325019, 325531, 326718, 329905, 329944, 329950, 330148, 333285, 336757, 339864, 343798, 345658, 348835, 352584, 359331, 362349, 387020, 392319, 404586, 422196, 445466, 447957, 464529, 464702, 468844, 473206, 541261, 599118, 605736, 610487, 610926, 711949, 712482, 712534, 712548, 715040, 715702, 813002, 813014, 820007, 820444, 821182, 825734, 915356, 918025, 918346, 926180, 931516, 996906, 1042513, 1046546, 1046831, 1050586, 1064518, 1113204, 1161646, 1165932, 1168249, 1169146, 1169836, 1171860, 1177566, 1218638, 1442127, 1519064, 1625144, 1675641, 1732702, 1845780, 1864598, 1975387, 2297717, 2356547, 2362061, 2362679, 2438128, 2438826, 2438830, 2533093, 2538670, 2544442, 2584012, 2742197, 2750742, 2754526, 2813730, 2813739, 2823682, 3004837, 3046179, 3050506, 3068747, 3205577, 3213722, 3345602, 3345966, 3352792, 3456750, 3459444, 3973714, 4118934, 4119142, 4119919, 4183783, 4236173, 4363942, 4372593, 4468534, 4477428, 4571229, 4571376, 4578665, 4718796, 4719440, 4772808, 4822967, 4928161, 4929010, 4930146, 4933318, 4933344, 4933368, 4933942, 4934142, 4934618, 5276310, 5380405, 5391360, 5486121, 5491557, 5492269, 5495386, 5496794, 5565586, 5565590, 5739058, 5739578, 5784551, 5784792, 5839114, 5847868, 5848570, 5848577, 5940967, 5942319, 5946174, 6015169, 6015504, 6018214, 6082849, 6085508, 6200614, 6305550, 6702202, 6856968, 6862193, 6862366, 6862370, 6863766, 6863946, 6867068, 6927190, 6929950, 6933958, 6936908, 6937168, 6938315, 6996822, 6997068, 7002136, 7002990, 7004544, 7005778, 7007026, 7012226, 7106893, 7109206, 7110959, 7112248, 7112262, 7112548, 7112638, 7114042, 7154803, 7155575, 7167465, 7206958, 7215008, 7218820, 7593189, 7665845, 7665892, 7665949, 7768933, 7777466, 7777562, 7909552, 8081372, 8081386, 8129160, 8225854, 8238425, 8304101, 8358558, 8373157, 8373434, 8404155, 8407230, 8407249, 8483176, 8503076, 8559776, 8577790, 8578718, 8665429, 8691488, 8695886, 8695890, 8698430, 8766190, 8766427, 8836514, 8938540, 8991008, 9046245, 9046438, 9046720, 9074972, 9597900, 9598732, 9666892, 9678448, 9750462, 9750481, 9955998, 10066554, 10186414, 10978926, 11111566, 11574138, 12034273, 12112013, 12188654, 12188663, 12214862, 12328998, 13718189, 13800657, 13891917, 13908376, 15082782, 15082786, 15089160, 15338443, 15749154, 18317390, 18372628, 18420142, 18579975, 19663466, 19799416, 20014826, 21137961, 21209474, 21233016, 21244093, 21321831, 21347070, 23791834, 23801106, 23868654, 23868702, 23961552, 26968608, 26974926, 27033292, 27111248, 27217506, 27314642, 27314646, 27323261, 27325951, 27325961, 27326156, 27426222, 27677196, 30390438, 31684592, 35949892, 39549494, 42260938, 43662858, 47224268, 53574797, 55553214, 55796149, 56907091, 59516775, 59642245, 59740522, 59945769, 61424364, 61441023, 61448100, 63104717, 63177950, 63238540, 63241464, 63241942, 63244156, 63248406, 63295606, 63295615, 63409690, 65857744, 66005333, 66169354, 67188970, 67250170, 67423110, 68588533, 70462301, 71482278, 71609858, 71722375, 73156747, 73415749, 74499724, 74499758, 75250669, 77698839, 77699236, 77868973, 78043190, 79134784, 80464470, 80464482, 80511890, 83174624, 83468744, 85238876, 89649306, 89802672, 89820241, 92409997, 96572502, 101030039, 101106838, 108778226, 110140518, 116418296, 118855789, 119019950, 119030422, 119042512, 119048349, 119055475, 124099521, 125113690, 125379424, 128132086, 128252210, 137181126, 137292038, 140053795, 142617722, 142787502, 142880764, 146314194, 149229045, 149229047, 151768822, 151916408, 152033942, 154463413, 154607786, 154669762, 154770038, 156644189, 158223329, 158228310, 158387591, 158387766, 160970166, 163656062, 163926152, 163944313, 165646560, 168509079, 174213792, 174366498, 175348116, 178287188, 178403386, 178421639, 178544132, 178554844, 180191197, 180287150, 180349487, 180369871, 181959037, 182044889, 182077759, 182095587, 182106474, 182140538, 182194390, 182205101, 182205105, 184689401, 184698865, 184799340, 184800945, 184892201, 187589010, 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emmet_core-0.87.2/emmet/core/_general_store.py0000644000175100017510000000115115240717720021001 0ustar00runnerrunnerfrom __future__ import annotations from pydantic import BaseModel, Field from typing import Literal from emmet.core.types.typing import DateTimeType class GeneralStoreDoc(BaseModel): """ Defines general store data """ kind: Literal["newsfeed", "seminar", "banner"] | None = Field( None, description="Type of the data." ) markdown: str | None = Field(None, description="Markdown data.") meta: dict[str, str] | None = Field(None, description="Metadata.") last_updated: DateTimeType = Field( description="Timestamp for when this document was last updated", ) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8151078 emmet_core-0.87.2/emmet/core/_messages.py0000644000175100017510000000176715240717720017774 0ustar00runnerrunnerfrom pydantic import BaseModel, Field from emmet.core.types.enums import ValueEnum from emmet.core.types.typing import DateTimeType class MessageType(ValueEnum): generic = "generic" warning = "warning" class MessagesDoc(BaseModel): """ Defines data for user messages """ title: str | None = Field( None, title="Title", description="Generic title or short summary for the message.", ) body: str | None = Field( None, title="Body", description="Main text body of message." ) authors: list[str] | None = Field( None, title="Title", description="Generic title or short summary for the message.", ) type: MessageType = Field( # type: ignore[assignment] MessageType.generic, title="Type", description="The type of the message.", ) last_updated: DateTimeType = Field( title="Last Updated", description="The last updated UTC timestamp for the message.", ) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8151078 emmet_core-0.87.2/emmet/core/_user_settings.py0000644000175100017510000000145615240717720021056 0ustar00runnerrunnerfrom pydantic import BaseModel, Field from emmet.core.types.typing import DateTimeType class UserSettings(BaseModel): """User settings assigned at sign up modal.""" institution: str | None = None sector: str | None = None job_role: str | None = None is_email_subscribed: bool = False message_last_read: DateTimeType agreed_terms: bool = False render_legacy_id: bool = True is_dev_env: bool = False class UserSettingsDoc(BaseModel): """ Defines data for user settings """ consumer_id: str | None = Field( None, title="Consumer ID", description="Consumer ID for a specific user." ) settings: UserSettings | None = Field( None, title="Consumer ID settings", description="Settings defined for a specific user.", ) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8151078 emmet_core-0.87.2/emmet/core/absorption.py0000644000175100017510000000516115240717720020176 0ustar00runnerrunnerfrom __future__ import annotations from typing import TYPE_CHECKING import numpy as np from pydantic import Field from emmet.core.material_property import PropertyDoc if TYPE_CHECKING: from emmet.core.io.pymatgen import Structure from emmet.core.types.typing import IdentifierType class AbsorptionDoc(PropertyDoc): """Absorption spectrum based on frequency dependent dielectric function calculations.""" property_name: str = "Optical absorption spectrum" energies: list[float] = Field( ..., description="Absorption energy in eV starting from 0" ) energy_max: float = Field(..., description="Maximum energy") absorption_coefficient: list[float] = Field( ..., description="Absorption coefficient in cm^-1" ) average_imaginary_dielectric: list[float] = Field( ..., description="Imaginary part of the dielectric function corresponding to the " "energies", ) average_real_dielectric: list[float] = Field( ..., description="Real part of the dielectric function corresponding to the energies", ) bandgap: float | None = Field(None, description="The electronic band gap") nkpoints: int | None = Field( None, description="The number of kpoints used in the calculation" ) @classmethod def _convert_list_to_tensor(cls, l): l = np.array(l) a = np.array([[l[0], l[3], l[4]], [l[3], l[1], l[5]], [l[4], l[5], l[2]]]) return a @classmethod def from_structure( cls, energies: list, real_d: list[np.ndarray], imag_d: list[np.ndarray], absorption_co: list, bandgap: float, structure: Structure, nkpoints: float, material_id: IdentifierType | None = None, **kwargs, ): real_d_average = [ np.average(np.diagonal(cls._convert_list_to_tensor(t))) for t in real_d ] imag_d_average = [ np.average(np.diagonal(cls._convert_list_to_tensor(t))) for t in imag_d ] absorption_co = list(np.array(absorption_co)) energy_max = np.array(energies).max() return super().from_structure( meta_structure=structure, material_id=material_id, **{ "energies": energies, "energy_max": energy_max, "absorption_coefficient": absorption_co, "average_imaginary_dielectric": imag_d_average, "average_real_dielectric": real_d_average, "bandgap": bandgap, "nkpoints": nkpoints, }, **kwargs, ) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8151078 emmet_core-0.87.2/emmet/core/alloys.py0000644000175100017510000000127215240717720017320 0ustar00runnerrunnerfrom emmet.core.base import EmmetBaseModel from emmet.core.types.pymatgen_types.alloy_adapter import ( AlloyPairType, AlloySystemType, PairID, ) class AlloyPairDoc(EmmetBaseModel): alloy_pair: AlloyPairType pair_id: PairID @classmethod def from_pair(cls, pair): return cls(alloy_pair=pair, pair_id=pair.pair_id) class AlloySystemDoc(EmmetBaseModel): alloy_system: AlloySystemType alloy_id: str @classmethod def from_pair(cls, alloy_system): # Too large to duplicate alloy pairs here. alloy_system.alloy_pairs = None # type: ignore[assignment] return cls(alloy_system=alloy_system, alloy_id=alloy_system.alloy_id) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8151078 emmet_core-0.87.2/emmet/core/arrow.py0000644000175100017510000002134615240717720017153 0ustar00runnerrunnerimport sys import types import typing from collections.abc import Iterable, Mapping from datetime import datetime from enum import Enum from pathlib import Path from types import UnionType import pyarrow as pa import typing_extensions from monty.json import MSONable from pydantic._internal._model_construction import ModelMetaclass from pydantic.types import ImportString from typing_extensions import NotRequired RED = "\033[31m" BLUE = "\033[34m" RESET = "\033[0m" PY_PRIMITIVES_TO_ARROW = { int: pa.int64(), float: pa.float64(), str: pa.string(), bool: pa.bool_(), datetime: pa.timestamp("us", tz="UTC"), } # issues re: pa.UnionType (sparse and dense) implementation: # https://github.com/apache/arrow/issues/19157 # https://github.com/apache/arrow/issues/31211 # https://github.com/apache/arrow/issues/43857 # until union (de)serialization is supported on the pyarrow side we # strictly must narrow all union types in emmet to a single type, e.g., # my_list_of_numbers: list[int | float | None] = pydantic.Field(...) # won't fly def arrowize(obj) -> pa.DataType: """ Converts Python type annotations to PyArrow data types. Recursively introspects type annotations and constructs corresponding PyArrow data types. This enables schema generation for Arrow-based serialization but does not guarantee successful serialization. Supported types: - Python primitives - Generic containers (with annotations) - Literals - Enum classes (with caveats) - Pydantic models - TypedDict - MSONable objects with type adapters Limitations: - Union types must resolve to a single primitive type - Container types must be homogeneous - Map keys cannot be Union types Args: obj: A type annotation, class, or object with type annotations Returns: A PyArrow DataType corresponding to the input type Note: Union type serialization is currently unsupported in PyArrow. All union types are narrowed to their first non-None member. """ assert obj not in ( list, tuple, set, dict, typing.Dict, typing.List, typing.Set, typing.Tuple, ), f"Cannot construct arrow type from container of type {RED}{obj}{RESET} without typed arguments" assert ( obj is not typing.Any ), f"Cannot infer arrow type from: {RED}{obj}{RESET}, ambiguous ({BLUE}Any{RESET}) values cannot be resolved" if obj in PY_PRIMITIVES_TO_ARROW: return PY_PRIMITIVES_TO_ARROW[obj] if typing.get_origin(obj) in (list, tuple, set, Iterable): args = obj.__args__ if len(args) == 1: return pa.list_(arrowize(args[0])) assert all( [ member == args[0] for member in args if not isinstance(member, types.EllipsisType) ] ), f"Cannot infer arrow compatible primitive from iterable type containing mixed types: {RED}{obj}{RESET}" # once union type roundtripping is supported in pyarrow, something like this could work: # return pa.dense_union( # [ # pa.field(f"child_{idx}", arrowize(field)) # for idx, field, in zip(range(len(args)), args) # ] # ) return pa.list_(arrowize(args[0])) if typing.get_origin(obj) in (Mapping, dict): args = typing.get_args(obj) assert all( not ( isinstance(arg, typing._UnionGenericAlias | UnionType) # type: ignore[attr-defined] and len(list(filter(lambda x: x is not types.NoneType, arg.__args__))) > 1 ) for arg in args ), f""" Cannot construct arrow map type from: {RED}{obj}{RESET}. Keys and values for map types must resolve to single primitive data type, not Union type: key annotation: {BLUE}{args[0]}{RESET} value annotation: {BLUE}{args[1]}{RESET} """ return pa.map_(arrowize(args[0]), arrowize(args[1])) if typing.get_origin(obj) is typing.Literal: args = obj.__args__ first_type = type(args[0]) assert all( isinstance(member, first_type) for member in args ), f"Cannot infer arrow compatible primitive from literal with mixed types: {RED}{obj}{RESET}" return PY_PRIMITIVES_TO_ARROW[first_type] if typing.get_origin(obj) is NotRequired: return arrowize(obj.__args__[0]) if isinstance(obj, type) and issubclass(obj, Enum): # making an assumption here based on knowledge of emmet + pymatgen enum classes... # should have better way to handle all enums to get serialized representations return PY_PRIMITIVES_TO_ARROW[str] if isinstance(obj, typing._UnionGenericAlias | UnionType): # type: ignore[attr-defined] arrow_types = [ (arg.__name__, arrowize(arg)) for arg in list( filter( lambda x: x is not types.NoneType, # noqa: E721 obj.__args__, ) ) ] assert all( [field == arrow_types[0][1] for (field_name, field) in arrow_types] ), f""" (De)Serialization of Union types is not supported in pyarrow currently, narrow the types of {RED}{obj}{RESET} to resolve to a single primitive """ # once union type roundtripping is supported in pyarrow, something like this could work: # return pa.dense_union( # [pa.field(f"{field_name}", field) for (field_name, field) in arrow_types] # ) return arrow_types[0][1] if isinstance(obj, ModelMetaclass): return pa.struct( [ pa.field( field_name, arrowize( obj.type_overrides[field_name] if hasattr(obj, "type_overrides") and field_name in obj.type_overrides else value.annotation ), ) for field_name, value in obj.model_fields.items() # type: ignore[attr-defined] if not value.exclude ] ) if any(obj is str_like for str_like in (ImportString, Path)): return PY_PRIMITIVES_TO_ARROW[str] if isinstance(obj, typing._TypedDictMeta | typing_extensions._TypedDictMeta): # type: ignore[attr-defined] return pa.struct( [ pa.field(field_name, arrowize(value)) for field_name, value in obj.__annotations__.items() ] ) if isinstance(obj, typing.ForwardRef): if sys.version_info >= (3, 14): # Behavior of _evaluate has yet again changed - needs globals / locals for module of ForwardRef # https://github.com/pydantic/pydantic/pull/12427 module = sys.modules[obj.__forward_module__] # type: ignore[index] return arrowize( obj._evaluate(module.__dict__, module.__dict__, {}, recursive_guard=frozenset()) # type: ignore[misc, arg-type] ) elif sys.version_info >= (3, 12, 4): # ``type_params`` were added in 3.13 and the signature of _evaluate() # is not backward-compatible (it was backported to 3.12.4, so anything # before 3.12.4 still has the old signature). # See: https://github.com/python/cpython/pull/118104. return arrowize( obj._evaluate(globals(), locals(), {}, recursive_guard=frozenset()) # type: ignore[misc, arg-type, call-overload] ) # DO NOT CHANGE ARGS - IGNORE MYPY # recursive_guard kwarg does not exist in python <3.12 return arrowize(obj._evaluate(globals(), locals(), frozenset())) # type: ignore[call-arg, arg-type, call-overload] if isinstance(obj, typing.TypeVar): return arrowize(obj.__constraints__[1]) if isinstance(obj, typing._AnnotatedAlias): # type: ignore[attr-defined] return arrowize(obj.__args__[0]) if isinstance(obj, type) and issubclass(obj, MSONable): assert hasattr( obj, "__type_adapter__" ), f""" No serialization adapter is specified for msonable obj: {RED}{obj}{RESET}. TypeVars can be written for external classes in the types.pymatgen_types module. Internal classes may use the set_msonable_type_adapter decorator. """ return arrowize(obj.__type_adapter__.model_fields["root"].annotation) if isinstance(obj, type): return arrowize( next( filter( lambda primitive: issubclass(obj, primitive), PY_PRIMITIVES_TO_ARROW ) ) ) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8151078 emmet_core-0.87.2/emmet/core/band_theory.py0000644000175100017510000005233115240717720020315 0ustar00runnerrunner"""Define basic schema for band theoretic properties.""" from __future__ import annotations import numbers from collections import defaultdict from itertools import product from typing import TYPE_CHECKING, Annotated import numpy as np from pydantic import BaseModel, BeforeValidator, Field, computed_field from emmet.core.io.pymatgen import BandStructure as PmgBandStructure from emmet.core.io.pymatgen import ( CompleteDos, Dos, HighSymmKpath, Kpoint, Lattice, Orbital, Spin, ) from emmet.core.math import Matrix3D, Vector3D from emmet.core.settings import EmmetSettings from emmet.core.types.enums import ValueEnum from emmet.core.types.pymatgen_types.structure_adapter import StructureType from emmet.core.vasp.calc_types import RunType if TYPE_CHECKING: from collections.abc import Callable, Generator, Sequence from typing import Any from typing_extensions import Self from emmet.core.io.pymatgen import PeriodicSite, Structure BAND_GAP_TOL = 1e-4 SETTINGS = EmmetSettings() # type: ignore[call-arg] class BSPathType(ValueEnum): """Define path conventions used in pymatgen.""" setyawan_curtarolo = "setyawan_curtarolo" hinuma = "hinuma" latimer_munro = "latimer_munro" unknown = "unknown" class BandTheoryBase(BaseModel): identifier: str | None = Field(None, description="The identifier of this object.") structure: StructureType | None = Field( None, description="The structure associated with this calculation." ) run_type: RunType | None = Field( None, description="The functional used in the calculation." ) def __str__(self) -> str: return ( f"{self.__class__.__name__}({self.identifier or ''}" f"{': ' if self.identifier else ''}" f"{self.structure.formula if self.structure else ''})" ) def __repr__(self) -> str: return self.__str__() def _deser_lattice(lattice: Lattice | dict | Matrix3D) -> Matrix3D: """Ensure the lattice matrix is stored only.""" if isinstance(lattice, Lattice): return lattice.matrix elif isinstance(lattice, dict): return lattice.get("lattice") return lattice class BandStructure( BandTheoryBase, populate_by_name=True, validate_by_alias=True, validate_by_name=True ): """Define the schema of band structures. This class is generic enough to accommodate both electronic and phonon band structures. """ path_convention: BSPathType = Field( # type: ignore[assignment] BSPathType.unknown, description="High symmetry path convention of the band structure", ) kpath: list[str] | None = None labels_dict: dict[str, Vector3D] = Field( {}, description="The high-symmetry labels of specific q-points." ) qpoints: list[Vector3D] = Field( description="The wave vectors (q-points) at which the band structure was sampled, in direct coordinates.", ) reciprocal_lattice: Annotated[Matrix3D, BeforeValidator(_deser_lattice)] = Field( description="The reciprocal lattice.", validation_alias="lattice_rec" ) def model_post_init(self, __context: Any) -> None: if self.structure and self.path_convention == BSPathType.unknown: # Try the user-input labels if provided, as well as regenerating # them on the fly if path determination fails labels = [self.labels_dict, None] if self.labels_dict else [None] for label_set in labels: try: self.path_convention, self.kpath, new_labels = next( obtain_path_type( self.structure, self.qpoints, user_kpoint_labels=label_set, ) ) if self.path_convention != BSPathType.unknown: self.labels_dict = new_labels.copy() break except StopIteration: pass # Fallback, use antipatterns to intuit path type if self.path_convention == BSPathType.unknown and self.labels_dict: self.path_convention = _loose_path_match(list(self.labels_dict)) class ProjectedBS(BaseModel): """Light memory model for projected band structure data. Only C order is used. When the data is unraveled by numpy, you should get a 4-index tensor where: - Index 1: band index - Index 2: k-point index - Index 3: site index - Index 4: index for the orbital character, ordered as s, py, pz, px, dxy, dyz, dz2, dxz, dx2-y2,... """ spin_up: list[float] | None = Field( None, description="The flattened spin-up band projections." ) spin_down: list[float] | None = Field( None, description="The flattened spin-down band projections." ) rank: tuple[int, int, int, int] = Field( description="The original shape of the band projections." ) @classmethod def from_pmg_like(cls, projections: dict[Spin, np.ndarray]) -> Self: spins = list(projections) rank = projections[spins[0]].shape if not all(proj.shape == rank for proj in projections.values()): raise ValueError( "The band structure projections should have the same shape." ) config = { f"spin_{spin.name}": proj.flatten(order="C") for spin, proj in projections.items() } return cls( **config, # type: ignore[arg-type] rank=rank, # type: ignore[arg-type] ) def to_pmg_like(self) -> dict[Spin, np.ndarray]: return { spin: np.array(getattr(self, f"spin_{spin.name}")).reshape( self.rank, order="C" ) for spin in Spin if getattr(self, f"spin_{spin.name}") } class ElectronicBS(BandStructure): """Define an electronic band structure schema.""" efermi: float = Field( description="The Fermi level (highest occupied energy level.)" ) band_gap: float | None = Field(None, description="The value of the band gap.") spin_up_bands: list[list[float]] | None = Field( None, description="The eigen-energies of the spin-up electrons. The first index represents the band, the second the k-point index.", ) spin_down_bands: list[list[float]] | None = Field( None, description="The eigen-energies of the spin-down electrons. The first index represents the band, the second the k-point index.", ) is_direct: bool | None = Field( None, description="If the bandgap is non-zero, whether the band gap is direct." ) projections: ProjectedBS | None = Field( None, description="The site- and spin-orbital-projected band structure." ) @computed_field def is_metal(self) -> bool: """Whether the band gap is almost zero.""" return ( self.band_gap < BAND_GAP_TOL if isinstance(self.band_gap, numbers.Number) else False ) @classmethod def from_pmg(cls, ebs: PmgBandStructure, **kwargs) -> Self: """Construct from a pymatgen band structure object.""" band_gap_meta = ebs.get_band_gap() config = { "qpoints": [qpt.frac_coords for qpt in ebs.kpoints], "lattice_rec": ebs.lattice_rec.matrix, "efermi": ebs.efermi, "labels_dict": { label: qpt.frac_coords for label, qpt in (ebs.labels_dict or {}).items() }, "structure": ebs.structure, "is_metal": ebs.is_metal(), "is_direct": band_gap_meta["direct"], "band_gap": band_gap_meta["energy"], } for spin in Spin: config[f"spin_{spin.name}_bands"] = ebs.bands.get(spin) config["projections"] = ( ProjectedBS.from_pmg_like(ebs.projections) # type: ignore[arg-type] if ebs.projections else None ) return cls(**config, **kwargs) def to_pmg(self, pmg_cls: Callable = PmgBandStructure) -> PmgBandStructure: """Construct the pymatgen object from the current instance. Parameters ----------- pmg_cls : PmgBandStructure or a subclass Because BandStructureSymmLine has the same constructor signature as PmgBandStructure, any PmgBandStructure-derived class which has the same signature can be used here. """ rlatt = Lattice(self.reciprocal_lattice) bands = {} for spin in Spin: if v := getattr(self, f"spin_{spin.name}_bands", None): bands[spin] = np.array(v) return pmg_cls( [Kpoint(q, lattice=rlatt).frac_coords for q in self.qpoints], # type: ignore[misc] bands, rlatt, self.efermi, labels_dict={ k: Kpoint(v, lattice=rlatt).frac_coords # type: ignore[misc] for k, v in (self.labels_dict or {}).items() }, coords_are_cartesian=False, structure=self.structure, projections=self.projections.to_pmg_like() if self.projections else None, ) class ProjectedDos(BaseModel): """Atom and orbital projected DOS.""" spin_up_densities: list[list[float] | None] | None = Field( None, description="The spin-up projected densities of state." ) spin_down_densities: list[list[float] | None] | None = Field( None, description="The spin-down projected densities of state." ) orbital: list[str | None] | None = Field( None, description="The orbital character of this DOS, if applicable." ) site_index: list[int | None] | None = Field( None, description="The index of the atom in the structure onto which this DOS is projected.", ) def _to_list_of_dict(self) -> list[dict[str, str | int | list[float]]]: """Possible serialization procedure.""" return [ {k: getattr(self, k)[i] for k in self.__class__.model_fields} for i in range(len(self.spin_up_densities or [])) ] @classmethod def _from_list_of_dict(cls, lod: list[dict[str, str | int | list[float]]]) -> Self: """Possible deserialization route.""" return cls(**{k: [entry.get(k) for entry in lod] for k in cls.model_fields}) # type: ignore[arg-type] @classmethod def from_pmg_like( cls, pdos: dict[PeriodicSite, dict[Orbital, dict[Spin, np.ndarray]]], structure: StructureType, ) -> Self: """Create a ProjectedDos from a pymatgen-like CompleteDos.pdos.""" projs = [ { **{ f"spin_{spin.name}_densities": sr_dos for spin, sr_dos in spin_dos.items() }, "orbital": orbital.name, "site_index": [ idx for idx, ref_site in enumerate(structure) if ref_site == site ][0], } for site, orb_spin_dos in pdos.items() for orbital, spin_dos in orb_spin_dos.items() ] return cls( **{ # type: ignore[arg-type] k: [proj.get(k) for proj in projs] for k in ( "spin_up_densities", "spin_down_densities", "orbital", "site_index", ) }, ) def to_pmg_like( self, structure: StructureType ) -> dict[PeriodicSite, dict[Orbital, dict[Spin, np.ndarray]]]: """Construct a pymatgen-like representation of the projected DOS.""" pdos: defaultdict[ PeriodicSite, defaultdict[Orbital, dict[Spin, np.ndarray]] ] = defaultdict(lambda: defaultdict(dict)) for i, dens in enumerate(self.spin_up_densities or []): pdos[site := structure[self.site_index[i]]][ # type: ignore[index] orb := Orbital[self.orbital[i]] # type: ignore[index,misc] ][Spin.up] = np.array(dens) if self.spin_down_densities and all( sdd for sdd in self.spin_down_densities ): pdos[site][orb][Spin.down] = np.array(self.spin_down_densities[i]) return dict(pdos) class ElectronicDos(BandTheoryBase): """Electronic density of states (DOS) with possible projections.""" spin_up_densities: list[float] | None = Field( None, description="The spin-up densities of state." ) spin_down_densities: list[float] | None = Field( None, description="The spin-down densities of state." ) energies: list[float] | None = Field( None, description="The energies at which the DOS was calculated." ) efermi: float | None = Field(None, description="The Fermi energy.") projected_densities: ProjectedDos | None = Field( None, description="The orbital- and site-projected DOS." ) @property def _available_spins(self) -> list[Spin]: """Retrive spin indices of non-null spin data.""" return [spin for spin in Spin if getattr(self, f"spin_{spin.name}_densities")] @classmethod def from_pmg(cls, dos: Dos | CompleteDos, **kwargs) -> Self: """Create an electronic DOS from a pymatgen object.""" densities: dict[str, list[float]] = { f"spin_{spin.name}_densities": sr_dos.tolist() for spin, sr_dos in dos.densities.items() } pdos = ( ProjectedDos.from_pmg_like(dos.pdos, dos.structure) # type: ignore[arg-type] if isinstance(dos, CompleteDos) else None ) return cls( **densities, # type: ignore[arg-type] efermi=dos.efermi, energies=dos.energies.tolist(), structure=getattr(dos, "structure", None), projected_densities=pdos, **kwargs, ) def to_pmg(self) -> Dos | CompleteDos: """Serialize to pymatgen.""" if self.efermi is None: raise ValueError( "Fermi level unspecified, cannot create a pymatgen (Complete)Dos object." ) densities: dict[Spin, np.ndarray] = { spin: np.array(getattr(self, f"spin_{spin.name}_densities")) for spin in self._available_spins } dos = Dos(self.efermi, np.array(self.energies), densities) if self.structure and self.projected_densities: return CompleteDos( self.structure, dos, self.projected_densities.to_pmg_like(self.structure), ) return dos def obtain_path_type( structure: Structure, kpoints: list[Vector3D], user_kpoint_labels: dict[str, Vector3D] | None = None, symprecs: list[float] = [SETTINGS.SYMPREC], # , 0.01], angtols: list[float] = [SETTINGS.ANGLE_TOL], atol: float = 1e-3, kpoint_tol: float = 1e-3, ) -> Generator: """Try to match a band structure path order to known path orders. Iterates over a list of `symprec` and `angle_tolerance` values to match paths to one of the path orders in `BSPathType` by checking that the kpoints list contains a reference k-point path dictated by the symmetry of the structure. Note that the k-points can be a superset of the high-symmetry path, the full path must be a sequential sublist of the input k-points. Ex: If the reference k-path is ["X","M","K","U"], a valid superset would be ["GAMMA", "X", "M", "K", "U", "X"], but an invalid superset would be ["X", "M", "GAMMA", "K", "U"] (as this breaks the reference order). The original k-point labels can be input by the user in `user_kpoint_labels`. This is often useful for older calculations because the high-symmetry k-points are not unique, and the path generated thereof will differ if the fractional coordinates of a given high-symmetry point represent a periodic image. Important notes: - pymatgen uses "\\Gamma" (BSPathType.setyawan_curtarolo), "GAMMA" (BSPathType.hinuma), and "Γ" (BSPathType.latimer_munro) to represent the Gamma point. - Sometimes legacy electronic structure data includes both the path set by a particular algorithm, plus a few extra points. We only check that the path matches up to the default high-symmetry path. Parameters ----------- structure : pymatgen .Structure Structure associated with the bandstructure kpoints : list of tuple[float,float,float] A list of (all) k-points included in the bandstructure. user_kpoint_labels: dict of str to Vector3D, or None = None An optional dict of user-defined k-point labels symprecs : list[float] = [SETTINGS.SYMPREC,0.01] List of `symprec` values to pass to `HighSymmKpath` angtols : list[float] = [SETTINGS.ANGLE_TOL] List `angle_tolerance` values to pass to `HighSymmKpath` atol : float = 1e-5 Absolute tolerance used by `HighSymmKpath` kpoint_tol : float = 1e-3 Absolute tolerance for matching fractional k-point coordiantes Yields ----------- BSPathType """ found_path_types: set[BSPathType] = set() for path_type in (bspt for bspt in BSPathType if bspt.value != "unknown"): # type: ignore[attr-defined] found_path_type = False for symprec, angtol in product(symprecs, angtols): if found_path_type: break try: hskp = HighSymmKpath( structure, # has_magmoms=False, magmom_axis=None, path_type=path_type.value, # symprec=symprec, # angle_tolerance=angtol, # atol=atol, ) kpoint_labels = user_kpoint_labels or { k: tuple(v.tolist()) for k, v in hskp.kpath["kpoints"].items() } ref_path = [ kpt for kpt_group in hskp.kpath["path"] for kpt in kpt_group ] inferred_kpath = _get_kpath(kpoint_labels, kpoints) num_extra = len(inferred_kpath) - len(ref_path) except Exception: continue if ( # check that the path orders are the same ref_path == inferred_kpath # or that the inferred path contains the reference one as a subset or (num_extra > 0 and _coarse_list_superset(inferred_kpath, ref_path)) ): found_path_type = True found_path_types.add(path_type) yield path_type, inferred_kpath, kpoint_labels if not found_path_types: yield BSPathType.unknown, None, {} def _coarse_list_superset(test: list, ref: list) -> bool: diff_len = len(test) - len(ref) if test[: len(ref)] == ref or test[diff_len:] == ref: return True for i in range(1, diff_len): if test[i : len(ref) + i] == ref: return True return False def _get_kpath( kpoints_labels: dict[str, Vector3D], kpoints: list[Vector3D] ) -> list[str]: labels = sorted(kpoints_labels) ref_kpoints = np.array([kpoints_labels[k] for k in labels]) kpts = np.array(kpoints) norms = np.array([np.linalg.norm(kpt - ref_kpoints, axis=1) for kpt in kpts]) ordered_labels = [labels[np.argmin(norm)] for norm in norms if norm.min() < 1e-5] processed_labels: list[str] = [] for i, lbl in enumerate(ordered_labels): if i > 0 and lbl == processed_labels[-1]: continue processed_labels.append(lbl) return processed_labels def _loose_path_match(labels: Sequence[str]) -> BSPathType: """ANTIPATTERN: Use poor labelling to determine path convention. In pymatgen, the following are true: | BSPathType | Gamma-point symbol | Other characters | | ---- | ---- | ---- | | setyawan_curtarolo | "\\Gamma" | Uppercase letters + Q_1 | | hinuma | "GAMMA" | Uppercase letters + Q_1 | | latimer_munro | "Γ" | Lowercase letters + q_{1} | We abuse these (already anti-)patterns to coarsely determine the path type, knowing a priori these were generated with pymatgen """ unique_labels = set(labels) if ( "\\Gamma" in unique_labels and unique_labels.intersection({"GAMMA", "Γ"}) == set() and set("".join(unique_labels)).intersection({r"{", r"}"}) == set() and {symb.upper() for symb in unique_labels if symb != "\\Gamma"}.issubset( unique_labels ) ): return BSPathType.setyawan_curtarolo # type: ignore[return-value] elif ( "GAMMA" in unique_labels and unique_labels.intersection({"\\Gamma", "Γ"}) == set() and set("".join(unique_labels)).intersection({r"{", r"}"}) == set() and {symb.upper() for symb in unique_labels} == unique_labels ): return BSPathType.hinuma # type: ignore[return-value] elif ( "Γ" in unique_labels and unique_labels.intersection({"\\Gamma", "GAMMA"}) == set() and {symb.lower() for symb in unique_labels if symb != "Γ"}.issubset( unique_labels ) ): return BSPathType.latimer_munro # type: ignore[return-value] return BSPathType.unknown # type: ignore[return-value] ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8151078 emmet_core-0.87.2/emmet/core/base.py0000644000175100017510000000373615240717720016736 0ustar00runnerrunner"""Base emmet model to add default metadata.""" from typing import Literal from pydantic import BaseModel, Field from emmet.core.io.pymatgen import __version__ as pmg_version from emmet.core import __version__ from emmet.core.types.typing import NullableDateTimeType class EmmetMeta(BaseModel): """Default emmet metadata.""" emmet_version: str | None = Field( __version__, description="The version of emmet this document was built with." ) pymatgen_version: str | None = Field( pmg_version, description="The version of pymatgen this document was built with." ) run_id: str | None = Field( None, description="The run id associated with this data build." ) batch_id: str | None = Field( None, description="Identifier corresponding to the origin of this document's blessed task.", ) database_version: str | None = Field( None, description="The database version for the built data." ) build_date: NullableDateTimeType = Field( # type: ignore description="The build date for this document.", ) license: Literal["BY-C", "BY-NC"] | None = Field( None, description="License for the data entry." ) class EmmetBaseModel(BaseModel): """Base Model for default emmet data.""" builder_meta: EmmetMeta | None = Field( default_factory=EmmetMeta, # type: ignore description="Builder metadata.", ) class CalcMeta(BaseModel): """Lightweight representation for storing jobflow-style job info.""" identifier: str | None = Field( None, description="The identifier (UUID, ULID, etc.) of the calculation." ) dir_name: str | None = Field( None, description="The file-systm-like path where a job was run." ) name: str | None = Field( None, description="The job name or a convenient label for it." ) parents: list[str] | None = Field( None, description="A list of all UUIDs which fed into this job." ) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8151078 emmet_core-0.87.2/emmet/core/bonds.py0000644000175100017510000001032315240717720017117 0ustar00runnerrunnerfrom __future__ import annotations from typing import TYPE_CHECKING import numpy as np from pydantic import Field from emmet.core.io.pymatgen import StructureGraph, NearNeighbors from typing_extensions import TypedDict from emmet.core.material_property import PropertyDoc from emmet.core.types.pymatgen_types.structure_graph_adapter import StructureGraphType AVAILABLE_METHODS = {nn.__name__: nn for nn in NearNeighbors.__subclasses__()} if TYPE_CHECKING: from emmet.core.io.pymatgen import Structure from typing_extensions import Self from emmet.core.mpid import MPID class TypedBondLengthStatsDict(TypedDict): all_weights: list[float] min: float max: float mean: float variance: float class BondingDoc(PropertyDoc): """Structure graphs representing chemical bonds calculated from structure using near neighbor strategies as defined in pymatgen.""" property_name: str = "bonding" structure_graph: StructureGraphType = Field( description="Structure graph", ) method: str = Field(description="Method used to compute structure graph.") bond_types: dict[str, list[float]] = Field( description="Dictionary of bond types to their length, e.g. a Fe-O to " "a list of the lengths of Fe-O bonds in Angstrom." ) bond_length_stats: TypedBondLengthStatsDict = Field( description="Dictionary of statistics of bonds in structure " ) coordination_envs: list[str] = Field( description="List of co-ordination environments, e.g. ['Mo-S(6)', 'S-Mo(3)']." ) coordination_envs_anonymous: list[str] = Field( description="List of co-ordination environments without elements " "present, e.g. ['A-B(6)', 'A-B(3)']." ) @classmethod def from_structure( cls, structure: Structure, material_id: str | MPID, preferred_methods: tuple[str | NearNeighbors, ...] = ( "CrystalNN", "MinimumDistanceNN", ), **kwargs, ) -> Self | None: """ Calculate :param structure: ideally an oxidation state-decorated structure :param material_id: mpid :param preferred_methods: list of strings of NearNeighbor classes or NearNeighbor instances :param deprecated: whether source material is or is not deprecated :param kwargs: to pass to PropertyDoc :return: """ bonding_info = None bond_analysis_methods: list[NearNeighbors] = [ AVAILABLE_METHODS[method]() if isinstance(method, str) else method for method in preferred_methods ] warnings: list[str] = [] for method in bond_analysis_methods: try: sg = StructureGraph.from_local_env_strategy(structure, method) # ensure edge weights are specifically bond lengths edge_weights = [] for u, v, d in sg.graph.edges(data=True): jimage = np.array(d["to_jimage"]) dist = sg.structure.get_distance(u, v, jimage=jimage) edge_weights.append((u, v, d["to_jimage"], dist)) for u, v, to_jimage, dist in edge_weights: sg.alter_edge(u, v, to_jimage=to_jimage, new_weight=dist) bonding_info = { "method": method.__class__.__name__, "structure_graph": sg, "bond_types": sg.types_and_weights_of_connections, "bond_length_stats": sg.weight_statistics, "coordination_envs": sg.types_of_coordination_environments(), "coordination_envs_anonymous": sg.types_of_coordination_environments( anonymous=True ), } break except Exception as e: warnings.append( f"Failed to calculate bonding: {material_id} {method} {e}" ) if bonding_info: return super().from_structure( meta_structure=structure, material_id=material_id, warnings=warnings, **bonding_info, **kwargs, ) return None ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8151078 emmet_core-0.87.2/emmet/core/charge_density.py0000644000175100017510000000267715240717720021017 0ustar00runnerrunner"""Define schemas for charge density data.""" from __future__ import annotations from typing import TYPE_CHECKING from warnings import warn from pydantic import BaseModel, Field from emmet.core.types.typing import DateTimeType, IdentifierType from emmet.core.vasp.models import ChgcarLike if TYPE_CHECKING: from typing import Any class VolumetricDataDoc(BaseModel): """ Volumetric data metadata for selected materials. """ last_updated: DateTimeType = Field( description="Timestamp for the most recent update to the charge density data.", ) task_id: IdentifierType | None = Field( None, description="The Materials Project ID of the calculation producing the charge density data. " "This comes in the form: mp-******.", ) volumetric_file: str = Field( description="The name of the VASP file, e.g., CHGCAR, AECCAR0, etc." ) volumetric_data: ChgcarLike = Field( description="The volumetric data from the CHGCAR-like VASP file." ) class ChgcarDataDoc(VolumetricDataDoc): """Legacy alias for backward compat.""" def model_post_init(self, context: Any) -> None: """Raise deprecation warning.""" warn( "ChgcarDataDoc is deprecated and will be removed in a " "future version of emmet-core. Please migrate to " "VolumetricDataDoc.", category=DeprecationWarning, stacklevel=2, ) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8152714 emmet_core-0.87.2/emmet/core/chemenv.py0000644000175100017510000004312415240717720017444 0ustar00runnerrunnerfrom __future__ import annotations from typing import TYPE_CHECKING, Literal from pydantic import Field from emmet.core.io.pymatgen import ( SpacegroupAnalyzer, Molecule, Structure, SimplestChemenvStrategy, AllCoordinationGeometries, LocalGeometryFinder, LightStructureEnvironments, ) from emmet.core.material_property import PropertyDoc if TYPE_CHECKING: from emmet.core.types.typing import IdentifierType from emmet.core.types.pymatgen_types.structure_adapter import ( MoleculeType, StructureType, ) DEFAULT_DISTANCE_CUTOFF = 1.4 DEFAULT_ANGLE_CUTOFF = 0.3 AVAILABLE_METHODS = { "DefaultSimplestChemenvStrategy": SimplestChemenvStrategy(), # "DefaultSimplestChemenvStrategy_all_bonds": SimplestChemenvStrategy(additional_condition=0), } DEFAULTSIMPLESTCHEMENVSTRATEGY = "Simplest ChemenvStrategy using fixed angle and distance parameters for the definition of neighbors in the Voronoi approach. The coordination environment is then given as the one with the lowest continuous symmetry measure. Options: distance_cutoff=1.4 angle_cutoff=0.3 additional_condition=1 continuous_symmetry_measure_cutoff=10.0" # noqa: E501 # SIMPLESTCHEMENVSTRATEGY_ALL_BONDS = "Simplest ChemenvStrategy using fixed angle and distance parameters for the definition of neighbors in the Voronoi approach. The coordination environment is then given as the one with the lowest continuous symmetry measure. Options: distance_cutoff=1.4 angle_cutoff=0.3 additional_condition=0 continuous_symmetry_measure_cutoff=10.0" # noqa: E501 METHODS_DESCRIPTION = { "DefaultSimplestChemenvStrategy": DEFAULTSIMPLESTCHEMENVSTRATEGY, # "DefaultSimplestChemenvStrategy_all_bonds": SIMPLESTCHEMENVSTRATEGY_ALL_BONDS, } COORDINATION_GEOMETRIES = Literal[ "S:1", "L:2", "A:2", "TL:3", "TY:3", "TS:3", "T:4", "S:4", "SY:4", "SS:4", "PP:5", "S:5", "T:5", "O:6", "T:6", "PP:6", "PB:7", "ST:7", "ET:7", "FO:7", "C:8", "SA:8", "SBT:8", "TBT:8", "DD:8", "DDPN:8", "HB:8", "BO_1:8", "BO_2:8", "BO_3:8", "TC:9", "TT_1:9", "TT_2:9", "TT_3:9", "HD:9", "TI:9", "SMA:9", "SS:9", "TO_1:9", "TO_2:9", "TO_3:9", "PP:10", "PA:10", "SBSA:10", "MI:10", "BS_1:10", "BS_2:10", "TBSA:10", "PCPA:11", "H:11", "DI:11", "I:12", "PBP:12", "TT:12", "C:12", "AC:12", "SC:12", "HP:12", "HA:12", "SH:13", "DD:20", "None", ] COORDINATION_GEOMETRIES_IUPAC = Literal[ "TOCT-9", "CUS-10", "PPR-10", "PPRP-11", "OCF-7", "SAPRT-10", "T-4", "PAPR-10", "DD-20", "TPR-6", "OCT-8", "PP-5", "SPY-4", "HBPY-9", "A-2", "TPRT-8", "TS-3", "SS-4", "None", "SAPR-8", "SPY-5", "SAPRS-10", "HPR-12", "L-2", "PPY-6", "SP-4", "IC-12", "HBPY-8", "TPY-3", "CUS-9", "TPRS-8", "DD-8", "TBPY-5", "TP-3", "TCA-9", "PPRP-12", "SAPRS-9", "OC-6", "PBPY-7", "TPRS-7", "TPRS-9", "CU-8", "HAPR-12", "TPRT-7", ] COORDINATION_GEOMETRIES_IUCR = Literal[ "[8do]", "[3n]", "[6p]", "[8cb]", "[5by]", "[12p]", "[6p2c]", "[8acb]", "[7by]", "[3l]", "[6p1c]", "[12aco]", "[2l]", "None", "[12i]", "[6o]", "[6p3c]", "[4n]", "[12co]", "[1l]", "[5y]", "[5l]", "[4l]", "[2n]", "[8by]", "[4t]", "[12tt]", ] COORDINATION_GEOMETRIES_NAMES = Literal[ "Pentagonal pyramid", "Metabidiminished icosahedron", "Square-face bicapped trigonal prism", "Pentagonal plane", "Cube", "Triangular cupola", "Heptagonal dipyramid", "Pentagonal prism", "T-shaped", "Square-face capped trigonal prism", "Single neighbor", "Triangular-face bicapped trigonal prism", "Pentagonal-face capped pentagonal antiprism", "Trigonal prism", "Square cupola", "See-saw", "Square non-coplanar", "Hexagonal bipyramid", "Triangular non-coplanar", "Tetrahedron", "Tricapped triangular prism (one square-face cap and two triangular-face caps)", "Square-face monocapped antiprism", "Hexagonal antiprism", "Linear", "Octahedron", "Truncated tetrahedron", "Square-face capped hexagonal prism", "Tricapped octahedron (all 3 cap faces share one atom)", "Bicapped square prism (opposite faces)", "Bicapped octahedron (cap faces with one edge in common)", "Tridiminished icosahedron", "Cuboctahedron", "Dodecahedron", "Dodecahedron with triangular faces - p2345 plane normalized", "Square-face capped square prism", "Icosahedron", "Dodecahedron with triangular faces", "Pentagonal bipyramid", "Tricapped octahedron (cap faces are aligned)", "Square pyramid", "Tricapped triangular prism (three square-face caps)", "Trigonal bipyramid", "Tricapped triangular prism (two square-face caps and one triangular-face cap)", "Pentagonal antiprism", "Diminished icosahedron", "Anticuboctahedron", "Trigonal-face bicapped square antiprism", "Bicapped octahedron (cap faces with one atom in common)", "Hexagonal prism", "Angular", "Trigonal plane", "Square plane", "Bicapped octahedron (opposed cap faces)", "Bicapped square prism (adjacent faces)", "Square antiprism", "Pentagonal-face bicapped pentagonal prism", "Square-face bicapped square antiprism", "Hendecahedron", "End-trigonal-face capped trigonal prism", "Face-capped octahedron", "Tricapped octahedron (all 3 cap faces are sharingone edge of a face)", ] COORDINATION_GEOMETRIES_NAMES_WITH_ALTERNATIVES = Literal[ "Anticuboctahedron (also known as Triangular bicupola)", "Trigonal bipyramid (also known as Trigonal dipyramid, Triangular dipyramid)", "Octahedron (also known as Square dipyramid, Square bipyramid, Triangular antiprism, Trigonal antiprism)", "Diminished icosahedron", "Square antiprism (also known as Tetragonal antiprism, Anticube)", "Square cupola", "Hexagonal bipyramid (also known as Hexagonal dipyramid)", "Square-face capped hexagonal prism", "Bicapped square prism (opposite faces) (also known as Bicapped cube)", "Square-face bicapped trigonal prism", "Square non-coplanar", "Triangular-face bicapped trigonal prism", "Square plane", "Trigonal prism (also known as Triangular prism)", "Bicapped octahedron (opposed cap faces)", "Tricapped octahedron (all 3 cap faces share one atom)", "Pentagonal prism", "Triangular cupola", "Bicapped square prism (adjacent faces) (also known as Bicapped cube)", "Cuboctahedron", "Tricapped triangular prism (two square-face caps and one triangular-face cap) (also known as Triaugmented trigonal prism)", # noqa: E501 "Square-face capped trigonal prism (also known as Augmented triangular prism)", "Tetrahedron (also known as Triangular pyramid, Trigonal pyramid)", "Cube (also known as Square prism, Tetragonal prism)", "Bicapped octahedron (cap faces with one edge in common)", "Bicapped octahedron (cap faces with one atom in common)", "Pentagonal pyramid", "Dodecahedron with triangular faces (also known as Snub disphenoid, Siamese dodecahedron)", "Pentagonal antiprism (also known as Paradiminished icosahedron)", "Tricapped octahedron (cap faces are aligned)", "Icosahedron", "Dodecahedron", "Face-capped octahedron (also known as Monocapped octahedron)", "Angular", "Hendecahedron (also known as Bisymmetric hendecahedron)", "Trigonal-face bicapped square antiprism", "Pentagonal-face capped pentagonal antiprism (also known as Gyroelongated pentagonal pyramid, Diminished icosahedron, Truncated icosahedron)", # noqa: E501 "Linear", "Pentagonal plane (also known as Pentagon)", "Tricapped triangular prism (three square-face caps) (also known as Triaugmented trigonal prism)", "Tricapped octahedron (all 3 cap faces are sharingone edge of a face)", "Heptagonal dipyramid (also known as Heptagonal bipyramid)", "T-shaped", "Single neighbor", "Trigonal plane (also known as Triangular planar)", "Dodecahedron with triangular faces - p2345 plane normalized (also known as Snub disphenoid - p2345 plane normalized, Siamese dodecahedron - p2345 plane normalized)", # noqa: E501 "Tricapped triangular prism (one square-face cap and two triangular-face caps) (also known as Triaugmented trigonal prism)", # noqa: E501 "Truncated tetrahedron", "Hexagonal prism", "Tridiminished icosahedron", "Metabidiminished icosahedron", "See-saw", "Square-face capped square prism (also known as Monocapped cube)", "Square pyramid", "Pentagonal-face bicapped pentagonal prism", "Hexagonal antiprism", "Triangular non-coplanar", "End-trigonal-face capped trigonal prism (also known as Augmented triangular prism)", "Pentagonal bipyramid (also known as Pentagonal dipyramid)", "Square-face monocapped antiprism (also known as Gyroelongated square pyramid)", "Square-face bicapped square antiprism (also known as Square-face bicapped square anticube, Bicapped anticube, Gyroelongated square dipyramid)", # noqa: E501 ] class ChemEnvDoc(PropertyDoc): """ Coordination environments based on cation-anion bonds computed for all unique cations in this structure. If no oxidation states are available, all bonds will be considered as a fall-back. """ property_name: str = "coord_environment" structure: StructureType | None = Field( ..., description="The structure used in the generation of the chemical environment data", ) valences: list[float] = Field( description="List of valences for each site in this material to determine cations" ) species: list[str] = Field( description="List of unique (cationic) species in structure." ) chemenv_symbol: list[COORDINATION_GEOMETRIES] = Field( description="List of ChemEnv symbols for unique (cationic) species in structure" ) chemenv_iupac: list[COORDINATION_GEOMETRIES_IUPAC] = Field( description="List of symbols for unique (cationic) species in structure in IUPAC format" ) chemenv_iucr: list[COORDINATION_GEOMETRIES_IUCR] = Field( description="List of symbols for unique (cationic) species in structure in IUCR format" ) chemenv_name: list[COORDINATION_GEOMETRIES_NAMES] = Field( description="List of text description of coordination environment for unique (cationic) species in structure." ) chemenv_name_with_alternatives: list[ COORDINATION_GEOMETRIES_NAMES_WITH_ALTERNATIVES ] = Field( description="List of text description of coordination environment including alternative descriptions for unique (cationic) species in structure." # noqa: E501 ) csm: list[float | None] = Field( description="Saves the continous symmetry measures for unique (cationic) species in structure" ) method: str | None = Field( description="Method used to compute chemical environments" ) mol_from_site_environments: list[MoleculeType | None] = Field( description="List of Molecule Objects describing the detected environment." ) wyckoff_positions: list[str] = Field( description="List of Wyckoff positions for unique (cationic) species in structure." ) warnings: str | None = Field(None, description="Warning") @classmethod def from_structure( cls, structure: Structure, material_id: IdentifierType | None = None, **kwargs, ): # type: ignore[override] """ Args: structure: structure including oxidation states material_id: mpid **kwargs: Returns: """ d = { "valences": [], "species": [], "chemenv_symbol": [], "chemenv_iupac": [], "chemenv_iucr": [], "chemenv_name": [], "chemenv_name_with_alternatives": [], "csm": [], "mol_from_site_environments": [], "wyckoff_positions": [], "method": None, "warnings": None, # "structure_environment": None, } # type: dict # try: list_mol = [] list_chemenv = [] list_chemenv_iupac = [] list_chemenv_iucr = [] list_chemenv_text = [] list_chemenv_text_with_alternatives = [] list_csm = [] list_wyckoff = [] list_species = [] valences = [getattr(site.specie, "oxi_state", None) for site in structure] valences = [v for v in valences if v is not None] sga = SpacegroupAnalyzer(structure) symm_struct = sga.get_symmetrized_structure() # We still need the whole list of indices inequivalent_indices = [ indices[0] for indices in symm_struct.equivalent_indices ] # if len(inequivalent_indices)>5: # print("Too many sites") # raise Exception # wyckoff symbols for all inequivalent indices wyckoffs_unique = symm_struct.wyckoff_symbols # use the local geometry finder to get the important information lgf = LocalGeometryFinder() lgf.setup_structure(structure=structure) all_ce = AllCoordinationGeometries() if len(valences) == len(structure): # Standard alorithm will only focus on cations and cation-anion bonds! method_description = "DefaultSimplestChemenvStrategy" method = AVAILABLE_METHODS[method_description] # We will only focus on cations! inequivalent_indices_cations = [ indices[0] for indices in symm_struct.equivalent_indices if valences[indices[0]] > 0.0 # type: ignore[operator] ] se = lgf.compute_structure_environments( only_indices=inequivalent_indices_cations, valences=valences, maximum_distance_factor=DEFAULT_DISTANCE_CUTOFF, minimum_angle_factor=DEFAULT_ANGLE_CUTOFF, ) lse = LightStructureEnvironments.from_structure_environments( strategy=method, structure_environments=se ) warnings = None else: d.update( { "warnings": "No oxidation states available. Cation-anion bonds cannot be identified." } ) return super().from_structure( meta_structure=structure, material_id=material_id, **d, **kwargs, ) # method_description = "DefaultSimplestChemenvStrategy_all_bonds" # method = AVAILABLE_METHODS[method_description] # se = lgf.compute_structure_environments( # only_indices=inequivalent_indices, # ) # lse = LightStructureEnvironments.from_structure_environments(strategy=method, # structure_environments=se) # Trick to get rid of duplicate code # inequivalent_indices_cations = inequivalent_indices # warnings = "No oxidation states. Analysis will now include all bonds" for index, wyckoff in zip(inequivalent_indices, wyckoffs_unique): # ONLY CATIONS if ( index in inequivalent_indices_cations and len(lse.neighbors_sets[index] or []) > 0 ): # Coordinaton environment will be saved as a molecule! mol = Molecule.from_sites( [structure[index]] + lse.neighbors_sets[index][0].neighb_sites ) mol = mol.get_centered_molecule() env = lse.coordination_environments[index] co = all_ce.get_geometry_from_mp_symbol(env[0]["ce_symbol"]) name = co.name if co.alternative_names: name += f" (also known as {', '.join(co.alternative_names)})" # save everything in a list list_chemenv_text.append(co.name) list_chemenv_text_with_alternatives.append(name) list_chemenv.append(co.ce_symbol) list_chemenv_iucr.append(co.IUCr_symbol_str) list_chemenv_iupac.append(co.IUPAC_symbol_str) list_mol.append(mol) list_wyckoff.append(wyckoff) list_species.append(structure[index].species_string) list_csm.append(env[0]["csm"]) d.update( { "valences": valences, "species": list_species, "chemenv_symbol": list_chemenv, "chemenv_iupac": list_chemenv_iupac, "chemenv_iucr": list_chemenv_iucr, "chemenv_name": list_chemenv_text, "chemenv_name_with_alternatives": list_chemenv_text_with_alternatives, "csm": list_csm, "mol_from_site_environments": list_mol, "wyckoff_positions": list_wyckoff, # "structure_environment": se.as_dict(), "method": METHODS_DESCRIPTION[method_description], "warnings": warnings, } ) # except Exception: # d.update({"warnings": "ChemEnv algorithm failed."}) return super().from_structure( meta_structure=structure, material_id=material_id, **d, **kwargs, ) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011028.9639914 emmet_core-0.87.2/emmet/core/connectors/0000755000175100017510000000000015240717725017623 5ustar00runnerrunner././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8152714 emmet_core-0.87.2/emmet/core/connectors/__init__.py0000644000175100017510000000010415240717720021722 0ustar00runnerrunner"""Aggregate resources for making external queries to databases.""" ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8159072 emmet_core-0.87.2/emmet/core/connectors/analysis.py0000644000175100017510000001643415240717720022023 0ustar00runnerrunner"""Tools for processing database CIFs.""" from __future__ import annotations from contextlib import contextmanager, redirect_stderr, redirect_stdout from io import StringIO from tempfile import NamedTemporaryFile from typing import Generator from emmet.core.io.pymatgen import Structure, CifBlock, CifParser, SpacegroupAnalyzer from emmet.core.settings import EmmetSettings try: from pycodcif import parse as cod_tools_parse_cif except ImportError: cod_tools_parse_cif = None EMMET_SETTINGS = EmmetSettings() @contextmanager def _get_context_manager(verbose: bool) -> Generator[None, None, None]: if verbose: yield else: with redirect_stderr(StringIO()), redirect_stdout(StringIO()): yield def parse_cif_cod_tools( cif_str: str, verbose: bool = False, cif_parser: CifParser | None = None, ) -> tuple[list[Structure | None], list[str]]: """Parse a CIF with the COD tools parser. Parameters ----------- cif_str : str The CIF string to parse verbose : bool = False Whether to pass error messages from pymatgen and CIF parsing tools. Defaults to suppressing these messages. cif_parser : pymatgen.io.cif.CifParser or None (default) Existing instance of a CifParser to use Returns ----------- List of Structure if parsing is successful List of str documenting any parsing issues """ structures: list[Structure | None] = [] remarks: list[str] = [] temp_file = NamedTemporaryFile(suffix=".cif") cif_data = [] with open(temp_file.name, "w", encoding="utf-8") as f: # remove non-ASCII characters f.write(cif_str.encode("ascii", "ignore").decode("ascii")) f.seek(0) try: cif_data, _, _ = cod_tools_parse_cif(temp_file.name) except Exception as exc: remarks += [f"pycodcif.parse: {exc}"] temp_file.close() if cif_data: try: cif_parser = cif_parser or CifParser.from_str(cif_str) with _get_context_manager(verbose): structures += [ cif_parser._get_structure( CifBlock(block["values"], block["loops"], block["name"]), primitive=True, symmetrized=False, check_occu=True, ) for block in cif_data ] except Exception as exc: remarks += [f"pycodcif/pymatgen: {exc}"] return structures, remarks def remove_artificial_disorder( structures: list[Structure], in_place: bool = True ) -> list[Structure]: """Remove artificial disorder from a structure. Some of the ICSD CIFs are disordered in oxidation states only. Because these are assigned by hand and don't reflect actual chemical or configurational disorder, we remove this artificial disorder here. Parameters ----------- structures : list of Structure in_place : bool = True Whether to modify `Structure`s in place Returns ----------- list of Structure """ output_structs = structures if in_place else structures[:] for idx, structure in enumerate(structures): if ( not structure.is_ordered and ( non_oxi_struct := Structure( structure.lattice, species=[site.species for site in structure], coords=structure.frac_coords, coords_are_cartesian=False, charge=structure.charge, ).remove_oxidation_states() ).is_ordered ): output_structs[idx] = non_oxi_struct return output_structs def remove_structures_with_fictive_elements( structures: list[Structure], ) -> list[Structure]: """Remove structures with fictive elements. This is used to ensure that a Structure contains only real elements. Sometimes, ICSD structures will use fictive elements to represent, e.g., cation substitution. Without a list of substituents, this is not useful for atomistic modelling. Parameters ----------- structures : list of Structure Returns ----------- list of Structure """ output_structs = [] for structure in structures: try: _ = structure.composition.remove_charges().as_dict() output_structs.append(structure) except Exception: continue return output_structs def remove_structures_with_unphysical_symmetry( structures: list[Structure], ) -> list[Structure]: """Remove structures whose symmetry cannot be determined. Sometimes the distances between atoms in a CIF is unphysically small, or some other issue prevents symmetry determination of a CIF. Parameters ----------- structures : list of Structure Returns ----------- list of Structure """ output_structures = [] for structure in structures: try: sga = SpacegroupAnalyzer( structure, symprec=EMMET_SETTINGS.SYMPREC, angle_tolerance=EMMET_SETTINGS.ANGLE_TOL, ) _ = sga.get_space_group_number() output_structures.append(structure) except Exception: continue return output_structures def parse_cif(cif_str: str, verbose: bool = False) -> tuple[list[Structure], list[str]]: """Parse a CIF string and apply sanity checks. Parameters ----------- cif_str : str The CIF string to parse verbose : bool = False Whether to pass error messages from pymatgen and CIF parsing tools. Defaults to suppressing these messages. Returns ----------- List of Structure if parsing is successful List of str documenting any parsing issues """ structures: list[Structure] = [] remarks: list[str] = [] cif_parser = CifParser.from_str(cif_str, check_cif=False) # Step 1: Try to parse with pymatgen without any changes to the CIF try: with _get_context_manager(verbose): structures = cif_parser.parse_structures(primitive=True) except Exception as exc: remarks.append(f"pymatgen.io.cif.CifParser: {exc}") # Step 2 (Optional): Use the Crystallography Open Database CIF parser # to correct errors in the CIF if the structures could not be parsed. if not structures and cod_tools_parse_cif: # TODO: structures could be list[Structure | None] here -> should narrow # and update rest of annotations + code in this module to # handle heterogeneous type structures, new_remarks = parse_cif_cod_tools( # type: ignore[assignment] cif_str, verbose=verbose, cif_parser=cif_parser ) remarks.extend(new_remarks) # Step 3: Remove structures with fictive elements structures = remove_structures_with_fictive_elements(structures) # Step 4: Remove structures whose symmetry cannot be determined structures = remove_structures_with_unphysical_symmetry(structures) # Step 5: Check remaining CIFs with pymatgen CIF checker structures = [ structure for structure in structures if (not cif_parser.check(structure)) ] # Step 6: Remove artificial disorder in oxidation states return remove_artificial_disorder(structures), remarks ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011028.9648297 emmet_core-0.87.2/emmet/core/connectors/icsd/0000755000175100017510000000000015240717725020545 5ustar00runnerrunner././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8159072 emmet_core-0.87.2/emmet/core/connectors/icsd/__init__.py0000644000175100017510000000021415240717720022646 0ustar00runnerrunner"""Tools for querying the ICSD API programmatically.""" from emmet.core.connectors.icsd.client import IcsdClient __all__ = ["IcsdClient"] ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8160295 emmet_core-0.87.2/emmet/core/connectors/icsd/client.py0000644000175100017510000002572515240717720022403 0ustar00runnerrunner"""Retrieve CIF and metadata from the ICSD API. This module is based on https://github.com/lrcfmd/ICSDClient/ """ from __future__ import annotations import logging import multiprocessing import os import re from time import time from typing import TYPE_CHECKING, Self import numpy as np import requests from pydantic import BaseModel, Field, PrivateAttr from requests.adapters import HTTPAdapter from urllib3.util.retry import Retry from emmet.core.connectors.icsd.enums import ( IcsdAdvancedSearchKeys, IcsdDataFields, IcsdSubset, ) from emmet.core.connectors.icsd.schemas import IcsdPropertyDoc from emmet.core.connectors.icsd.settings import IcsdClientSettings if TYPE_CHECKING: from typing import Any SETTINGS = IcsdClientSettings() # ICSD tokens expire in one hour _ICSD_TOKEN_TIMEOUT = 3600 logger = logging.getLogger(__name__) class IcsdClient(BaseModel): """Query data via the ICSD API.""" username: str = Field(SETTINGS.USERNAME) password: str = Field(SETTINGS.PASSWORD) max_retries: int = Field(SETTINGS.MAX_RETRIES) timeout: float = Field(SETTINGS.TIMEOUT) max_batch_size: int = Field(SETTINGS.MAX_BATCH_SIZE) use_document_model: bool = Field(True) num_parallel_requests: int | None = Field(None) _auth_token: str | None = PrivateAttr(None) _session_start_time: float | None = PrivateAttr(None) _session: requests.Session | None = PrivateAttr(None) @property def _is_windows(self) -> bool: return os.name == "nt" def refresh_session(self, force: bool = False) -> None: if self._session_start_time is None: self._session_start_time = time() if ( self._auth_token is None or ((time() - self._session_start_time) > 0.98 * _ICSD_TOKEN_TIMEOUT) or force ): if self._session: self.logout() self._session_start_time = time() self.login() def login(self) -> None: response = requests.post( "https://icsd.fiz-karlsruhe.de/ws/auth/login", headers={ "accept": "text/plain", "Content-Type": "application/x-www-form-urlencoded", }, data={ "loginid": self.username, "password": self.password, }, ) if response.status_code == 200: self._auth_token = response.headers["ICSD-Auth-Token"] if self._auth_token is None: raise ValueError( f"{self.__module__}.{self.__class__.__name__} " f"failed to fetch auth token: {response.content}" ) else: raise ValueError( f"{self.__module__}.{self.__class__.__name__} " "failed to fetch auth token with status code " f"{response.status_code}: {response.content!r}" ) self._session = requests.Session() self._session.headers = {"ICSD-Auth-Token": self._auth_token} retry = Retry( total=self.max_retries, read=self.max_retries, connect=self.max_retries, respect_retry_after_header=True, status_forcelist=[429, 504, 502], # rate limiting backoff_factor=0.1, ) adapter = HTTPAdapter(max_retries=retry) self._session.mount("http://", adapter) self._session.mount("https://", adapter) def logout(self) -> None: if not self._session: return _ = self._session.get( "https://icsd.fiz-karlsruhe.de/ws/auth/logout", headers={ "accept": "text/plain", }, params=[("windowsclient", self._is_windows)], ) self._auth_token = None self._session_start_time = None self._session.close() self._session = None def __enter__(self) -> Self: self.login() return self def __exit__(self, *args) -> None: self.logout() def __del__(self) -> None: self.logout() def _get(self, *args, **kwargs) -> requests.Response: self.refresh_session() assert self._session is not None params = tuple( list(kwargs.pop("params", [])) + [("windowsclient", self._is_windows)] ) resp = self._session.get(*args, **kwargs, params=params) if resp.status_code != 200: logger.warning( f"{self.__module__}.{self.__class__.__name__} " "failed to fetch content with status code " f"{resp.status_code}: {resp.content!r}" ) return resp def _get_cifs(self, collection_codes: str | list[str]) -> dict[int, str]: if isinstance(collection_codes, str): collection_codes = [collection_codes] if len(collection_codes) == 1: url = f"https://icsd.fiz-karlsruhe.de/ws/cif/{collection_codes[0]}" params = [] else: url = "https://icsd.fiz-karlsruhe.de/ws/cif/multiple" params = [("idnum", collection_codes)] cif_str = self._get( url, headers={"accept": "application/cif"}, params=params, ).content.decode() return { int(match.group(1)): "#(C)" + cif_body for cif_body in cif_str.split("\n#(C)")[1:] if (match := re.search(r"_database_code_ICSD ([0-9]+)", cif_body)) } def _search( self, indices: list[str], properties: list[str | IcsdDataFields] | None = None, include_cif: bool = False, include_metadata: bool = False, _data: list | None = None, ) -> list[dict[str, Any]]: self.refresh_session(force=True) search_props = [ ( prop.value if isinstance(prop, IcsdDataFields) else IcsdDataFields(prop).value ) for prop in (properties or list(IcsdDataFields)) ] if len(indices) > self.max_batch_size: batched_ids: list[list[str]] = [ v.tolist() for v in np.array_split( indices, np.ceil(len(indices) / self.max_batch_size) ) ] data = [] for i, batch in enumerate(batched_ids): data.extend( self._search( batch, properties=search_props, include_cif=include_cif, include_metadata=include_metadata, _data=_data, ) ) return data if not include_cif and not include_metadata: return [{"icsd_internal_id": int(idx) for idx in indices}] if include_metadata: if "CollectionCode" not in search_props: search_props.append("CollectionCode") response = self._get( "https://icsd.fiz-karlsruhe.de/ws/csv", headers={ "accept": "application/csv", }, params=( ("idnum", tuple(indices)), ("listSelection", search_props), ), ) data = [] if response.status_code == 200: csv_data = [ row.split("\t") for row in response.content.decode().splitlines() ] columns = csv_data[0][:-1] data += [ {IcsdDataFields[k].value: row[i] for i, k in enumerate(columns)} for row in csv_data[1:] ] else: if "Authentication not successful" in response.content: raise ValueError( "Failed to authenticate ICSD client. Please check your credentials" ) logger.warning( f"{self.__module__}.{self.__class__.__name__} " "csv search failed with status code " f"{response.status_code}: {response.content!r}" ) if include_cif: cifs = self._get_cifs(indices) if include_metadata: for i, doc in enumerate(data): data[i]["cif"] = cifs.get(int(doc["collection_code"])) else: data = [{"collection_code": cc, "cif": cif} for cc, cif in cifs.items()] if _data: _data.extend(data) return data def search( self, subset: IcsdSubset | str | None = None, properties: list[str | IcsdDataFields] | None = None, include_cif: bool = False, include_metadata: bool = False, **kwargs, ) -> list: query_vars = [] for k in IcsdAdvancedSearchKeys: if (v := kwargs.get(k.value)) is not None or ( v := kwargs.get(k.name) is not None ): if isinstance(v, tuple): v = f"{v[0]}-{v[1]}" elif isinstance(v, list): v = ",".join(v) query_vars.append(f"{k.name.lower()} : {v}") query_str = " and ".join(query_vars) params = [("query", query_str)] if subset: params.append(("content type", IcsdSubset(subset).name)) response = self._get( "https://icsd.fiz-karlsruhe.de/ws/search/expert", headers={ "accept": "application/xml", }, params=params, ) idxs: list[str] = [] if matches := re.match(".*(.*).*", response.content.decode()): idxs.extend(list(matches.groups())[0].split()) if self.num_parallel_requests and len(idxs) > self.num_parallel_requests: batched_idxs = np.array_split(idxs, self.num_parallel_requests) manager = multiprocessing.Manager() procs = [] res = manager.list() for iproc in range(self.num_parallel_requests): proc = multiprocessing.Process( target=self._search, args=(batched_idxs[iproc].tolist(),), kwargs={ "properties": properties, "include_cif": include_cif, "include_metadata": include_metadata, "_data": res, }, ) proc.start() procs.append(proc) for proc in procs: proc.join() return list(res) data = self._search( idxs, properties=properties, include_cif=include_cif, include_metadata=include_metadata, ) if subset: for i in range(len(data)): data[i]["subset"] = subset if self.use_document_model: data = [IcsdPropertyDoc(**props) for props in data] return data ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8160295 emmet_core-0.87.2/emmet/core/connectors/icsd/enums.py0000644000175100017510000000626115240717720022246 0ustar00runnerrunner"""Define ICSD-specific enums.""" from enum import Enum class IcsdSubset(Enum): EXPERIMENTAL_INORGANIC = "experimental_inorganic" EXPERIMENTAL_METALORGANIC = "experimental_metalorganic" THERORETICAL_STRUCTURES = "theoretical" class IcsdAdvancedSearchKeys(Enum): AUTHORS = "authors" ARTICLE = "article" DOI = "doi" PUBLICATIONYEAR = "publication_year" PAGEFIRST = "page_first" JOURNAL = "journal" VOLUME = "volume" ABSTRACT = "abstract" KEYWORDS = "keywords" CELLVOLUME = "cell_volume" CALCDENSITY = "calc_density" CELLPARAMETERS = "cell_parameters" SEARCH = "search" STRUCTUREDFORMULA = "structured_formula" CHEMICALNAME = "chemical_name" MINERALNAME = "mineral_name" MINERALNAMEIMA = "mineral_name_ima" MINERALGROUP = "mineral_group" ZVALUECHEMISTRY = "z_value_chemistry" ANXFORMULA = "anx_formula" ABFORMULA = "ab_formula" FORMULAWEIGHT = "formula_weight" NUMBEROFELEMENTS = "number_of_elements" COMPOSITION = "composition" COLLECTIONCODE = "collection_code" PDFNUMBER = "pdf_number" RELEASE = "release" RECORDINGDATE = "recording_date" MODIFICATIONDATE = "modification_date" COMMENT = "comment" RVALUE = "r_value" TEMPERATURE = "temperature" PRESSURE = "pressure" SAMPLETYPE = "sample_type" RADIATIONTYPE = "radiation_type" STRUCTURETYPE = "structure_type" SPACEGROUPSYMBOL = "space_group_symbol" SPACEGROUPNUMBER = "space_group_number" BRAVAISLATTICE = "bravais_lattice" CRYSTALSYSTEM = "crystal_system" CRYSTALCLASS = "crystal_class" LAUECLASS = "laue_class" WYCKOFFSEQUENCE = "wyckoff_sequence" PEARSONSYMBOL = "pearson_symbol" INVERSIONCENTER = "inversion_center" POLARAXIS = "polaraxis" class IcsdDataFields(Enum): CollectionCode = "collection_code" CcdcNo = "ccdc_no" HMS = "h_m_s" StructuredFormula = "structured_formula" StructureType = "structure_type" Title = "title" Authors = "authors" Reference = "reference" CellParameter = "cell_parameter" ReducedCellParameter = "reduced_cell_parameter" StandardisedCellParameter = "standardised_cell_parameter" CellVolume = "cell_volume" FormulaUnitsPerCell = "formula_units_per_cell" FormulaWeight = "formula_weight" Temperature = "temperature" Pressure = "pressure" RValue = "r_value" SumFormula = "sum_formula" ANXFormula = "a_n_x_formula" ABFormula = "a_b_formula" ChemicalName = "chemical_name" MineralName = "mineral_name" MineralNameIma = "mineral_name_ima" MineralGroup = "mineral_group" MineralSeries = "mineral_series" MineralRootGroup = "mineral_root_group" MineralSubGroup = "mineral_sub_group" MineralSuperGroup = "mineral_super_group" MineralSubClass = "mineral_sub_class" MineralClass = "mineral_class" CalculatedDensity = "calculated_density" MeasuredDensity = "measured_density" PearsonSymbol = "pearson_symbol" WyckoffSequence = "wyckoff_sequence" Journal = "journal" Volume = "volume" PublicationYear = "publication_year" Page = "page" Quality = "quality" Keywords = "keywords" Ccdc = "ccdc" Pdf = "pdf" ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8160295 emmet_core-0.87.2/emmet/core/connectors/icsd/schemas.py0000644000175100017510000001107115240717720022535 0ustar00runnerrunner"""Define document models used by the client.""" from __future__ import annotations from typing import TYPE_CHECKING from pydantic import BaseModel, Field, model_validator, ConfigDict from uncertainties import ufloat_fromstr from emmet.core.connectors.analysis import parse_cif from emmet.core.connectors.icsd.enums import IcsdSubset from emmet.core.types.pymatgen_types.structure_adapter import StructureType if TYPE_CHECKING: from typing import Any class UFloat(BaseModel): value: float | None = None uncertainty: float | None = None @model_validator(mode="before") @classmethod def parse_uncert(cls, config: Any) -> Any: if isinstance(config, str): if "(" in config: parsed = ufloat_fromstr(config) config = {"value": parsed.n, "uncertainty": parsed.s} else: try: config = {"value": float(config)} except ValueError: config = {} return config class CellParameters(BaseModel): a: UFloat | None = None b: UFloat | None = None c: UFloat | None = None alpha: UFloat | None = None beta: UFloat | None = None gamma: UFloat | None = None @model_validator(mode="before") @classmethod def from_str(cls, config): """Parse space-separated lattice parameters.""" lps = ["a", "b", "c", "alpha", "beta", "gamma"] if isinstance(config, str): vals = config.split() config = {lp: vals[i] for i, lp in enumerate(lps)} return config class IcsdPropertyDoc(BaseModel): """General container for ICSD data.""" model_config = ConfigDict(use_enum_values=True) collection_code: int | None = Field( None, description="The ICSD identifier of this entry." ) icsd_internal_id: int | None = Field( None, description="The internal identifier for the ICSD, not the collection code / ICSD ID.", ) cif: str | None = Field( None, description="The CIF file associated with this entry." ) subset: IcsdSubset | None = Field( None, description="The subset of the ICSD to which this entry belongs." ) ccdc_no: int | None = Field(None) ccdc: int | None = None h_m_s: str | None = None pearson_symbol: str | None = None wyckoff_sequence: str | None = None structured_formula: str | None = None sum_formula: str | None = None a_n_x_formula: str | None = None a_b_formula: str | None = None structure_type: str | None = None title: str | None = None authors: list[str] | None = None journal: str | None = None publication_year: int | None = None volume: int | None = None page: str | None = None reference: str | None = None cell_parameter: CellParameters | None = None reduced_cell_parameter: CellParameters | None = None standardised_cell_parameter: CellParameters | None = None cell_volume: UFloat | None = None formula_units_per_cell: int | None = None formula_weight: float | None = None temperature: float | None = None pressure: float | None = None r_value: float | None = None chemical_name: str | None = None mineral_name: str | None = None mineral_name_ima: str | None = None mineral_group: str | None = None mineral_series: str | None = None mineral_root_group: str | None = None mineral_sub_group: str | None = None mineral_super_group: str | None = None mineral_sub_class: str | None = None mineral_class: str | None = None calculated_density: UFloat | None = None measured_density: UFloat | None = None quality: int | None = None keywords: str | None = None pdf: str | None = None structures: list[StructureType] | None = Field( None, description="A list of validated `Structure`s parsed from the CIF." ) cif_parsing_errors: list[str] | None = Field( None, description="A list of any errors encountered while parsing the CIF." ) @model_validator(mode="before") @classmethod def deserialize(cls, config: Any) -> Any: """Parse ICSD data into a structured format.""" if isinstance(config.get("authors"), str): config["authors"] = config["authors"].split(";") for k, v in config.items(): if isinstance(v, str) and len(v) == 0: config[k] = None if config.get("cif") and config.get("structures") is None: config["structures"], config["cif_parsing_errors"] = parse_cif( config["cif"] ) return config ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8160295 emmet_core-0.87.2/emmet/core/connectors/icsd/settings.py0000644000175100017510000000146515240717720022760 0ustar00runnerrunner"""Define basic settings for the ICSD API client.""" from pydantic import Field from pydantic_settings import BaseSettings, SettingsConfigDict class IcsdClientSettings(BaseSettings): USERNAME: str | None = Field(None, description="ICSD username.") PASSWORD: str | None = Field(None, description="ICSD password.") MAX_RETRIES: int = Field( 10, description="The maximum number of retries when querying the ICSD API." ) TIMEOUT: float = Field( 15.0, description="The time in seconds to wait for a query to complete." ) MAX_BATCH_SIZE: int = Field( 500, description=( "The maximum number of structures to retrieve " "during pagination of query results." ), ) model_config = SettingsConfigDict(env_prefix="ICSD_API_") ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8160295 emmet_core-0.87.2/emmet/core/corrected_entries.py0000644000175100017510000000216115240717720021516 0ustar00runnerrunner"""Core definition of a CorrectedEntriesDoc Document""" from pydantic import Field from emmet.core.base import EmmetBaseModel from emmet.core.types.enums import ThermoType from emmet.core.types.pymatgen_types.computed_entries_adapter import ( ComputedStructureEntryType, ) from emmet.core.types.typing import DateTimeType from emmet.core.utils import type_override from emmet.core.vasp.calc_types.enums import RunType @type_override({"entries": dict[ThermoType, list[ComputedStructureEntryType]]}) class CorrectedEntriesDoc(EmmetBaseModel): """ A corrected entries document """ property_name: str = "corrected_entries" chemsys: str = Field( ..., title="Chemical System", description="Dash-delimited string of elements in the material.", ) entries: dict[RunType | ThermoType, list[ComputedStructureEntryType]] = Field( ..., description="List of all corrected entries that are valid for the specified thermo type.", ) last_updated: DateTimeType = Field( description="Timestamp for the most recent calculation update for this property.", ) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8160295 emmet_core-0.87.2/emmet/core/defect.py0000644000175100017510000001004715240717720017247 0ustar00runnerrunnerfrom __future__ import annotations from typing import TYPE_CHECKING from pydantic import BaseModel, Field from emmet.core.tasks import _VOLUMETRIC_FILES, TaskDoc from emmet.core.types.pymatgen_types.defect_adapter import DefectType if TYPE_CHECKING: from pathlib import Path from typing import Any class DefectInfo(BaseModel): """Information related to a point defect.""" defect_name: str = Field( title="The name of the defect", ) bulk_formula: str = Field( title="Bulk Formula", description="Formula of the bulk structure.", ) defect: DefectType = Field( title="Defect Object", description="Unit cell representation of the defect object.", ) charge_state: int | None = Field( None, title="Charge State", description="Charge state of the defect.", ) supercell_matrix: list[list[int, int, int]] | None = Field( # type: ignore[type-arg] None, title="Supercell Matrix", description="Supercell matrix used to construct the defect supercell.", ) class DefectTaskDoc(DefectInfo, TaskDoc): """Defect Task Document. Contains all the task-level information for a defect supercell calculation. """ @classmethod def from_directory( cls, dir_name: Path | str, volumetric_files: tuple[str, ...] = _VOLUMETRIC_FILES, additional_fields: dict[str, Any] | None = None, volume_change_warning_tol: float = 0.2, defect_info_key: str = "info", **vasp_calculation_kwargs, ) -> TaskDoc: """ Create a task document from a directory containing VASP files. Parameters ---------- dir_name The path to the folder containing the calculation outputs. store_additional_json Whether to store additional json files found in the calculation directory. volumetric_files Volumetric files to search for. additional_fields Dictionary of additional fields to add to output document. volume_change_warning_tol Maximum volume change allowed in VASP relaxations before the calculation is tagged with a warning. defect_info_key The key in the `additional_json` to extract the defect information from **vasp_calculation_kwargs Additional parsing options that will be passed to the :obj:`.Calculation.from_vasp_files` function. Returns ------- TaskDoc A task document for the calculation. """ tdoc = TaskDoc.from_directory( dir_name=dir_name, volumetric_files=volumetric_files, store_additional_json=True, additional_fields=additional_fields, volume_change_warning_tol=volume_change_warning_tol, **vasp_calculation_kwargs, ) return cls.from_taskdoc(tdoc, defect_info_key=defect_info_key) @classmethod def from_taskdoc( cls, taskdoc: TaskDoc, defect_info_key: str = "info", ) -> DefectTaskDoc: """ Create a DefectTaskDoc from a TaskDoc Args: taskdoc: TaskDoc to convert defect_info_key: The key in the `additional_json` to extract the defect information from Returns: DefectTaskDoc """ additional_info = taskdoc.additional_json[defect_info_key] defect = additional_info["defect"] charge_state = additional_info["charge_state"] defect_name = additional_info["defect_name"] bulk_formula = additional_info["bulk_formula"] supercell_matrix = additional_info["sc_mat"] task_dict = taskdoc.model_dump() task_dict.update( { "defect_name": defect_name, "defect": defect, "charge_state": charge_state, "bulk_formula": bulk_formula, "supercell_matrix": supercell_matrix, } ) return cls(**task_dict) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8160295 emmet_core-0.87.2/emmet/core/disorder.py0000644000175100017510000001023115240717720017623 0ustar00runnerrunnerfrom emmet.core.tasks import _VOLUMETRIC_FILES, CoreTaskDoc from emmet.core.types.typing import IdentifierType from emmet.core.types.pymatgen_types.structure_adapter import StructureType from emmet.core.trajectory import RelaxTrajectory from pydantic import Field from emmet.core.io.pymatgen import Structure from pathlib import Path import json from typing_extensions import TypedDict, Self from typing import Any REQUIRED_METADATA_KEYS: tuple[str, ...] = ( "ordered_task_id", "reference_structure", "supercell_diag", "prototype", "prototype_params", "composition_map", "versions", ) class TypedDisorderedTaskMetadata(TypedDict): ordered_task_id: IdentifierType reference_structure: dict[str, Any] supercell_diag: tuple[int, int, int] prototype: str prototype_params: dict[str, float] composition_map: dict[str, dict[str, int]] versions: dict[str, str] def parse_json(dir_name: Path | str) -> TypedDisorderedTaskMetadata: """Parse the disordered_task_doc_metadata.json file from the given directory.""" dir_path = Path(dir_name) json_file = dir_path / "disordered_task_doc_metadata.json" if not json_file.exists(): raise FileNotFoundError( f"No disordered_task_doc_metadata.json found in directory: {dir_name}" ) with json_file.open("r") as f: data = json.load(f) missing_keys = [key for key in REQUIRED_METADATA_KEYS if key not in data] if missing_keys: missing_str = ", ".join(missing_keys) raise ValueError( f"Missing required keys in disordered_task_doc_metadata.json: {missing_str}" ) supercell_x, supercell_y, supercell_z = data["supercell_diag"] data["supercell_diag"] = (supercell_x, supercell_y, supercell_z) return data class DisorderedTaskDoc(CoreTaskDoc): """Document for a disordered structure task, extending the CoreTaskDoc with additional metadata to capture disorder-specific information and its relationship to the ordered structure. """ ordered_task_id: IdentifierType = Field( ..., description="The task ID of the ordered structure task from which this disordered structure was generated.", ) reference_structure: StructureType = Field( ..., description="The reference disordered structure used to start relaxation and represent this disordered structure in the cluster expansion.", ) supercell_diag: tuple[int, int, int] = Field( ..., description="The supercell diagonal used to generate this disordered structure from the prototype structure.", ) prototype: str = Field( ..., description="The prototype name from which this disordered structure was generated.", ) prototype_params: dict[str, float] = Field( ..., description="The parameters used to generate the prototype structure.", ) composition_map: dict[str, dict[str, int]] = Field( ..., description="A mapping of which elements are in each sublattice for the disordered structure.", ) versions: dict[str, str] = Field( ..., description="A dictionary capturing the versions of relevant software packages used during the calculation.", ) @classmethod def from_directory( cls, dir_name: Path | str, volumetric_files: tuple[str, ...] = _VOLUMETRIC_FILES, **vasp_calculation_kwargs, ) -> tuple[Self, RelaxTrajectory]: base_doc, trajectory = CoreTaskDoc.from_directory( dir_name, volumetric_files=volumetric_files, **vasp_calculation_kwargs, ) metadata = parse_json(dir_name) data = base_doc.model_dump() data.update( ordered_task_id=metadata["ordered_task_id"], reference_structure=Structure.from_dict(metadata["reference_structure"]), supercell_diag=tuple(metadata["supercell_diag"]), prototype=metadata["prototype"], prototype_params=metadata["prototype_params"], composition_map=metadata["composition_map"], versions=metadata["versions"], ) return (cls.model_validate(data), trajectory) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8160295 emmet_core-0.87.2/emmet/core/dois.py0000644000175100017510000000073315240717720016754 0ustar00runnerrunnerfrom pydantic import BaseModel, Field from emmet.core.types.typing import MaterialIdentifierType class DOIDoc(BaseModel): """ DOIs to reference specific materials on Materials Project. """ doi: str | None = Field( None, description="DOI of the material.", ) material_id: MaterialIdentifierType | None = Field( None, description="The Materials Project ID of the material. This comes in the form: mp-******.", ) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8160295 emmet_core-0.87.2/emmet/core/elasticity.py0000644000175100017510000006434115240717720020175 0ustar00runnerrunnerfrom __future__ import annotations from typing import Any import numpy as np from pydantic import BaseModel, Field from emmet.core.io.pymatgen import ( ElasticTensor, ElasticTensorExpansion, Deformation, Strain, Stress, Structure, SpacegroupAnalyzer, TensorMapping, ) from emmet.core.material_property import PropertyDoc from emmet.core.math import Matrix3D, MatrixVoigt from emmet.core.settings import EmmetSettings from emmet.core.types.enums import TaskState from emmet.core.types.pymatgen_types.structure_adapter import StructureType from emmet.core.types.typing import IdentifierType SETTINGS = EmmetSettings() class ElasticTensorDoc(BaseModel): raw: MatrixVoigt | None = Field( None, description="Elastic tensor corresponding to structure orientation (GPa)", ) ieee_format: MatrixVoigt | None = Field( None, description="Elastic tensor corresponding to IEEE orientation (GPa)", ) class ComplianceTensorDoc(BaseModel): raw: MatrixVoigt | None = Field( None, description="Compliance tensor corresponding to structure orientation (TPa^-1)", ) ieee_format: MatrixVoigt | None = Field( None, description="Compliance tensor corresponding to IEEE orientation (TPa^-1)", ) class BulkModulus(BaseModel): voigt: float | None = Field(None, description="Bulk modulus Voigt average (GPa)") reuss: float | None = Field(None, description="Bulk modulus Reuss average (GPa)") vrh: float | None = Field( None, description="Bulk modulus Voigt-Reuss-Hill average (GPa)" ) class ShearModulus(BaseModel): voigt: float | None = Field(None, description="Shear modulus Voigt average (GPa)") reuss: float | None = Field(None, description="Shear modulus Reuss average (GPa)") vrh: float | None = Field( None, description="Shear modulus Voigt-Reuss-Hill average (GPa)" ) class SoundVelocity(BaseModel): transverse: float | None = Field( None, description="Transverse sound velocity (SI units)" ) longitudinal: float | None = Field( None, description="Longitudinal sound velocity (SI units)" ) snyder_acoustic: float | None = Field( None, description="Snyder's acoustic sound velocity (SI units)" ) snyder_optical: float | None = Field( None, description="Snyder's optical sound velocity (SI units)" ) snyder_total: float | None = Field( None, description="Snyder's total sound velocity (SI units)" ) class ThermalConductivity(BaseModel): clarke: float | None = Field( None, description="Clarke's thermal conductivity (SI units)" ) cahill: float | None = Field( None, description="Cahill's thermal conductivity (SI units)" ) class FittingData(BaseModel): """ Data used to fit the elastic tensor. Note, this only consists of the explicitly calculated primary data. With the data here, one can redo the fitting to regenerate the elastic data, e.g. using `ElasticityDoc.from_deformations_and_stresses()`. """ # data of strained structures deformations: list[Matrix3D] = Field( description="Deformations corresponding to the strained structures" ) strains: list[Matrix3D] = Field( description="Lagrangian strain tensors applied to structures" ) cauchy_stresses: list[Matrix3D] = Field( description="Cauchy stress tensors on strained structures" ) second_pk_stresses: list[Matrix3D] = Field( description="Second Piola-Kirchhoff stress tensors on structures" ) deformation_tasks: list[IdentifierType] | None = Field( None, description="Deformation task ids corresponding to the strained structures", ) deformation_dir_names: list[str] | None = Field( None, description="Paths to the running directories of deformation tasks" ) # data of equilibrium structure equilibrium_cauchy_stress: Matrix3D | None = Field( None, description="Cauchy stress tensor of the relaxed structure" ) optimization_task: IdentifierType | None = Field( None, description="Optimization task corresponding to the relaxed structure" ) optimization_dir_name: str | None = Field( None, description="Path to the running directory of the optimization task" ) # derived strains stresses num_total_strain_stress_states: int | None = Field( None, description="Number of total strain--stress states used for fitting, i.e. the " "sum of explicitly calculated deformations and derived deformations from " "symmetry.", ) class CriticalMessage(BaseModel): FITTING: str = "Critical: cannot fit elastic tensor. Error: {}." COMPLIANCE: str = ( "Critical: cannot invert elastic tensor to get compliance tensor. Error: {}." ) STRAIN_RANK: str = ( "Critical: insufficient number of valid strains. Expect the matrix of all " "strains to be of rank 6; got rank {}." ) N_STATES: str = ( "Critical: Expect 24 total (primary plus derived) strain--stress states " "to fit the data; got {}." ) class WarningMessage(BaseModel): NEGATIVE_EIGVAL: str = ( "Elastic tensor has negative eigenvalue(s), indicating that the structure is " "mechanically unstable." ) # https://doi.org/10.1103/PhysRevB.90.224104 NEGATIVE_MODULUS: str = ( "Negative modulus: {} {} is {}, indicating that the structure is mechanically " "unstable." ) SMALL_MODULUS: str = "Small modulus. {} {} is {}; smaller than {}." LARGE_MODULUS: str = "Large modulus. {} {} is {}; larger than {}." LARGE_YOUNG_MODULUS: str = "Large Young's modulus {}; larger than {}." LARGE_COMPONENT: str = "Elastic tensor has component larger than {}." class ElasticityDoc(PropertyDoc): property_name: str = "elasticity" structure: StructureType | None = Field( None, description="Structure to compute the elasticity", ) order: int = Field( default=2, description="Order of the expansion of the elastic tensor" ) elastic_tensor: ElasticTensorDoc | None = Field(None, description="Elastic tensor") compliance_tensor: ComplianceTensorDoc | None = Field( None, description="Compliance tensor" ) # derived properties bulk_modulus: BulkModulus | None = Field(None, description="Bulk modulus") shear_modulus: ShearModulus | None = Field(None, description="Shear modulus") sound_velocity: SoundVelocity | None = Field(None, description="Sound velocity") thermal_conductivity: ThermalConductivity | None = Field( None, description="Thermal conductivity" ) young_modulus: float | None = Field( None, description="Young's modulus (SI units)", alias="youngs_modulus" ) universal_anisotropy: float | None = Field( None, description="Universal elastic anisotropy" ) homogeneous_poisson: float | None = Field( None, description="Homogeneous Poisson ratio" ) debye_temperature: float | None = Field( None, description="Debye temperature (SI units)" ) fitting_data: FittingData | None = Field( None, description="Data used to fit the elastic tensor" ) fitting_method: str | None = Field( None, description="Method used to fit the elastic tensor" ) state: TaskState | None = Field( None, description="State of the fitting/analysis: `successful` or `failed`", ) @classmethod def from_deformations_and_stresses( cls, structure: Structure, deformations: list[Deformation], stresses: list[Stress], material_id: IdentifierType | None = None, deformation_task_ids: list[IdentifierType] | None = None, deformation_dir_names: list[str] | None = None, equilibrium_stress: Stress | None = None, optimization_task_id: IdentifierType | None = None, optimization_dir_name: str | None = None, fitting_method: str = "finite_difference", **kwargs, ): """ Fitting the elastic tensor from deformation and stresses. Note, the elastic tensor are obtained by fitting to the Lagrangian strains and second Piola-Kirchhoff stresses. In continuum mechanics nomenclature, it's the `material elasticity tensor`. For more, see Section 6.5 of `Continuum Mechanics and Thermodynamics -- From Fundamental Concepts to Governing Equations` Tadmor, Miller, and Elliott, Cambridge University Press, 2012. Args: structure: relaxed structure. material_id: material id. deformations: deformations applied to the relaxed structure that generate the strained structures. stresses: Cauchy stresses on the deformed structures. Expected units: GPa. deformation_task_ids: id of the deformation tasks. deformation_dir_names: directories where the deformation tasks are run. equilibrium_stress: Cauchy stress on the relaxed structure. optimization_task_id: id of the optimization task to relax the structure. optimization_dir_name: directory where the optimization task run. fitting_method: method used to fit the elastic tensor: {`finite_difference`, `pseudoinverse`, `independent`}. """ CM = CriticalMessage() # primary fitting data p_deforms = deformations p_stresses = stresses ( p_strains, p_2nd_pk_stresses, p_task_ids, p_dir_names, ) = generate_primary_fitting_data( p_deforms, p_stresses, deformation_task_ids, deformation_dir_names ) # derived fitting data ( d_deforms, d_strains, d_stresses, d_2nd_pk_stresses, ) = generate_derived_fitting_data(structure, p_strains, p_stresses) fitting_strains = p_strains + d_strains fitting_stresses = p_2nd_pk_stresses + d_2nd_pk_stresses # avoid symmop-related strains having non-symmop-related stresses fitting_stresses = symmetrize_stresses( fitting_stresses, fitting_strains, structure ) # fitting elastic tensor try: elastic_tensor = fit_elastic_tensor( fitting_strains, fitting_stresses, eq_stress=equilibrium_stress, fitting_method=fitting_method, ) # elastic and compliance tensors, only round ieee format ones ieee_et = elastic_tensor.voigt_symmetrized.convert_to_ieee(structure) et_doc = ElasticTensorDoc( raw=elastic_tensor.voigt.tolist(), ieee_format=ieee_et.round(0).voigt.tolist(), ) except np.linalg.LinAlgError as e: et_doc = None ct_doc = None derived_props = {} state = TaskState("failed") warnings = [CM.FITTING.format(e)] else: try: compliance = elastic_tensor.compliance_tensor compliance_ieee = ieee_et.compliance_tensor # compliance tensor, *1000 to convert units to TPa^-1, i.e. 10^-12 Pa, # assuming elastic tensor in units of GPa ct_doc = ComplianceTensorDoc( raw=(compliance * 1000).voigt.tolist(), ieee_format=(compliance_ieee * 1000).round(0).voigt.tolist(), ) # derived properties # (should put it here since some derived properties also dependent on # compliance tensor) derived_props = get_derived_properties(structure, elastic_tensor) # check all state, warnings = sanity_check(structure, et_doc, fitting_strains, derived_props) # type: ignore except np.linalg.LinAlgError as e: ct_doc = None derived_props = {} state = TaskState("failed") warnings = [CM.COMPLIANCE.format(e)] # fitting data eq_stress = None if equilibrium_stress is None else equilibrium_stress.tolist() n_states = len(p_deforms) + len(d_deforms) if n_states != 24: warnings.append(CM.N_STATES.format(n_states)) state = TaskState("failed") fitting_data = FittingData( deformations=[x.tolist() for x in p_deforms], # type: ignore strains=[x.tolist() for x in p_strains], # type: ignore cauchy_stresses=[x.tolist() for x in p_stresses], # type: ignore second_pk_stresses=[x.tolist() for x in p_2nd_pk_stresses], # type: ignore deformation_tasks=p_task_ids, # type: ignore deformation_dir_names=p_dir_names, # type: ignore equilibrium_cauchy_stress=eq_stress, optimization_task=optimization_task_id, optimization_dir_name=optimization_dir_name, # type: ignore num_total_strain_stress_states=n_states, ) return cls.from_structure( structure, material_id, order=2, elastic_tensor=et_doc, compliance_tensor=ct_doc, fitting_data=fitting_data, fitting_method=fitting_method, warnings=warnings, state=state, deprecated=state == TaskState("failed"), **derived_props, **kwargs, ) def generate_primary_fitting_data( deforms: list[Deformation], stresses: list[Stress], task_ids: list[IdentifierType] | None = None, dir_names: list[str] | None = None, ) -> tuple[list[Strain], list[Stress], list[IdentifierType] | None, list[str] | None]: """ Get the primary fitting data, i.e. data obtained from a calculation. Args: deforms: primary deformations stresses: primary stresses task_ids: ids of the tasks that generate the data dir_names: directories where the calculations are performed Returns: strains: primary strains second_pk_stresses: primary second Piola-Kirchhoff stresses task_ids: ids of the tasks that generate the data dir_names: directories where the calculations are performed """ size = len(deforms) assert len(stresses) == size if task_ids is not None: assert len(task_ids) == size if dir_names is not None: assert len(dir_names) == size strains = [d.green_lagrange_strain for d in deforms] second_pk_stresses = [s.piola_kirchoff_2(d) for (s, d) in zip(stresses, deforms)] return strains, second_pk_stresses, task_ids, dir_names def generate_derived_fitting_data( structure: Structure, strains: list[Strain], stresses: list[Stress], symprec=SETTINGS.SYMPREC, tol: float = 0.002, ) -> tuple[list[Deformation], list[Strain], list[Stress], list[Stress]]: """ Get the derived fitting data from symmetry operations on the primary fitting data. It can happen that multiple primary deformations can be mapped to the same derived deformation from different symmetry operations. Ideally, this cannot happen if one use the same structure to determine all the symmetry operations. However, this is not the case in atomate, where the deformation tasks are determined based on the symmetry of the structure before the tight relaxation, which in this function the structure is the relaxed structure. The symmetries can be different. In atomate2, this is not a problem, because the deformation tasks are determined based on the relaxed structure. To make it work for all cases, the stress for a derived deformation is the average of all derived stresses, each corresponding to a primary calculation. In doing so, we also check to ensure that: 1. only independent derived deformations are used 2. for a specific derived strain, a primary deformation is only used (mapped) once to obtain the average Args: structure: relaxed structure strains: primary strains stresses: primary stresses symprec: symmetry operation precision tol: tolerance for comparing strains and also for determining whether the deformation corresponds to the train is independent. The elastic workflow use a minimum strain of 0.005, so the default tolerance of 0.002 should be able to distinguish different strain states. Returns: derived_deforms: derived deformations derived_strains: derived strains derived_stresses: derived Cauchy stresses derived_2nd_pk_stresses: derived second Piola-Kirchhoff stresses """ sga = SpacegroupAnalyzer(structure, symprec=symprec) symmops = sga.get_symmetry_operations(cartesian=True) # primary strain mapping (used only for checking purpose below) p_mapping = TensorMapping(strains, strains, tol=tol) # Warnings: # Do not use deformations to replace strains in generating the derived fitting # data. More specifically, do not create TensorMapping using deformation. This is # because the Lagrangian strain is symmetric, but the deformation gradient is not. # Then, more derived data can be generated than enough/necessary, due to the # asymmetry of the deformation gradient. # generated derived deforms mapping = TensorMapping(tol=tol) for i, p_strain in enumerate(strains): for op in symmops: d_strain = p_strain.transform(op) # sym op generates another primary strain if d_strain in p_mapping: continue # sym op generates a non-independent deform if not d_strain.get_deformation_matrix().is_independent(tol=tol): continue # seen this derived deform before if d_strain in mapping: # all the existing `i` current = [t[1] for t in mapping[d_strain]] if i not in current: mapping[d_strain].append((op, i)) # not seen this derived deform before else: mapping[d_strain] = [(op, i)] # get average stress from derived deforms derived_strains = [] derived_stresses = [] derived_deforms = [] derived_2nd_pk_stresses = [] for d_strain, op_set in mapping.items(): symmops, p_indices = zip(*op_set) # type: ignore[assignment] p_stresses = [stresses[i] for i in p_indices] d_stresses = [s.transform(op) for s, op in zip(p_stresses, symmops)] d_stress = Stress(np.average(d_stresses, axis=0)) derived_strains.append(d_strain) derived_stresses.append(d_stress) deform = d_strain.get_deformation_matrix() derived_deforms.append(deform) derived_2nd_pk_stresses.append(d_stress.piola_kirchoff_2(deform)) return derived_deforms, derived_strains, derived_stresses, derived_2nd_pk_stresses def symmetrize_stresses( stresses: list[Stress], strains: list[Strain], structure: Structure, symprec=SETTINGS.SYMPREC, tol: float = 0.002, ) -> list[Stress]: """ Symmetrize stresses by averaging over all symmetry operations. Args: stresses: stresses to be symmetrized strains: strains corresponding to the stresses structure: materials structure symprec: symmetry operation precision tol: tolerance for comparing strains and also for determining whether the deformation corresponds to the train is independent. The elastic workflow use a minimum strain of 0.005, so the default tolerance of 0.002 should be able to distinguish different strain states. Returns: symmetrized stresses """ sga = SpacegroupAnalyzer(structure, symprec=symprec) symmops = sga.get_symmetry_operations(cartesian=True) # for each strain, get the stresses from other strain states related by symmetry symmmetrized_stresses = [] # type: list[Stress] for strain, _stress in zip(strains, stresses): mapping = TensorMapping([strain], [[]], tol=tol) for strain2, stress2 in zip(strains, stresses): for op in symmops: if strain2.transform(op) in mapping: mapping[strain].append(stress2.transform(op)) sym_stress = np.average(mapping[strain], axis=0) symmmetrized_stresses.append(Stress(sym_stress)) return symmmetrized_stresses def fit_elastic_tensor( strains: list[Strain], stresses: list[Stress], eq_stress: Stress | None, fitting_method: str = "finite_difference", order: int = 2, ) -> ElasticTensor: """ Fitting the elastic tensor. Args: strains: strains (primary and derived) to fit the elastic tensor stresses: stresses (primary and derived) to fit the elastic tensor eq_stress: equilibrium stress, i.e. stress on the relaxed structure fitting_method: method used to fit the elastic tensor: {`finite_difference`, `pseudoinverse`, `independent`} order: expansion order of the elastic tensor, 2 or 3 Returns: fitted elastic tensor """ if order > 2 or fitting_method == "finite_difference": # force finite diff if order > 2 result = ElasticTensorExpansion.from_diff_fit( strains, stresses, eq_stress=eq_stress, order=order ) if order == 2: result: ElasticTensor = ElasticTensor(result[0]) # type: ignore[no-redef] elif fitting_method == "pseudoinverse": result: ElasticTensor = ElasticTensor.from_pseudoinverse(strains, stresses) # type: ignore[no-redef] elif fitting_method == "independent": result: ElasticTensor = ElasticTensor.from_independent_strains( # type: ignore[no-redef] strains, stresses, eq_stress=eq_stress ) else: raise ValueError(f"Unsupported elastic fitting method {fitting_method}") return result # type: ignore[return-value] def get_derived_properties( structure: Structure, tensor: ElasticTensor ) -> dict[str, Any]: """ Get derived elasticity properties. Args: structure: relaxed structure tensor: elastic tensor corresponding to the structure Returns: a dict of derived elasticity properties """ try: prop_dict = tensor.get_structure_property_dict(structure) prop_dict.pop("structure") structure_prop_computed = True except ValueError: prop_dict = tensor.property_dict structure_prop_computed = False decimals = 3 derived_prop = { "bulk_modulus": BulkModulus( voigt=np.round(prop_dict["k_voigt"], decimals), # type: ignore[arg-type] reuss=np.round(prop_dict["k_reuss"], decimals), # type: ignore[arg-type] vrh=np.round(prop_dict["k_vrh"], decimals), # type: ignore[arg-type] ), "shear_modulus": ShearModulus( voigt=np.round(prop_dict["g_voigt"], decimals), # type: ignore[arg-type] reuss=np.round(prop_dict["g_reuss"], decimals), # type: ignore[arg-type] vrh=np.round(prop_dict["g_vrh"], decimals), # type: ignore[arg-type] ), "young_modulus": np.round(prop_dict["y_mod"], 0), # type: ignore[arg-type] "homogeneous_poisson": np.round(prop_dict["homogeneous_poisson"], decimals), # type: ignore[arg-type] "universal_anisotropy": np.round(prop_dict["universal_anisotropy"], decimals), # type: ignore[arg-type] } if structure_prop_computed: derived_prop.update( { "sound_velocity": SoundVelocity( transverse=prop_dict["trans_v"], # type: ignore[arg-type] longitudinal=prop_dict["long_v"], # type: ignore[arg-type] snyder_acoustic=prop_dict["snyder_ac"], # type: ignore[arg-type] snyder_optical=prop_dict["snyder_opt"], # type: ignore[arg-type] snyder_total=prop_dict["snyder_total"], # type: ignore[arg-type] ), "thermal_conductivity": ThermalConductivity( clarke=prop_dict["clarke_thermalcond"], # type: ignore[arg-type] cahill=prop_dict["cahill_thermalcond"], # type: ignore[arg-type] ), "debye_temperature": prop_dict["debye_temperature"], # type: ignore[arg-type] } ) return derived_prop def sanity_check( structure: Structure, elastic_doc: ElasticTensorDoc, strains: list[Strain], derived_props: dict[str, Any], ) -> tuple[TaskState, list[str]]: """ Post analysis to generate warnings if any. Returns: state: state of the calculation warnings: all warning messages. Messages starting with `Critical` are the ones resulting in a `failed` state. """ failed = False warnings = [] CM = CriticalMessage() WM = WarningMessage() # rank of all strains < 6? voigt_strains = [s.voigt for s in strains] rank = np.linalg.matrix_rank(voigt_strains) if rank != 6: failed = True warnings.append(CM.STRAIN_RANK.format(rank)) # elastic tensor eigenvalues eig_vals, _ = np.linalg.eig(elastic_doc.raw) # type: ignore if np.any(eig_vals < 0.0): warnings.append(WM.NEGATIVE_EIGVAL) # elastic tensor individual components et = np.asarray(elastic_doc.ieee_format) tol = 1e6 if np.any(et > tol): warnings.append(WM.LARGE_COMPONENT.format(tol)) # modulus low = 2.0 high = 1000.0 for p in ["bulk_modulus", "shear_modulus"]: doc = derived_props[p] doc = doc.model_dump() p = p.replace("_", " ") for name in ["voigt", "reuss", "vrh"]: v = doc[name] if v < 0: failed = True warnings.append(WM.NEGATIVE_MODULUS.format(name, p, v)) elif v < low: warnings.append(WM.SMALL_MODULUS.format(name, p, v, low)) elif v > high: warnings.append(WM.LARGE_MODULUS.format(name, p, v, high)) # young's modulus (note it is in Pa, not GPa) high = 1e12 v = derived_props["young_modulus"] if v > high: warnings.append(WM.LARGE_YOUNG_MODULUS.format(v, high)) state = TaskState("failed") if failed else TaskState("successful") return state, warnings ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8160295 emmet_core-0.87.2/emmet/core/elasticity_legacy.py0000644000175100017510000000372215240717720021515 0ustar00runnerrunnerfrom pydantic import BaseModel, Field class ElasticityData(BaseModel): """ Elastic tensors and associated information. """ k_voigt: float | None = Field( None, description="Voigt average of the bulk modulus in GPa.", ) k_reuss: float | None = Field( None, description="Reuss average of the bulk modulus in GPa.", ) k_vrh: float | None = Field( None, description="Voigt-Reuss-Hill average of the bulk modulus in GPa.", ) g_voigt: float | None = Field( None, description="Voigt average of the shear modulus in GPa.", ) g_reuss: float | None = Field( None, description="Reuss average of the shear modulus in GPa.", ) g_vrh: float | None = Field( None, description="Voigt-Reuss-Hill average of the shear modulus in GPa.", ) universal_anisotropy: float | None = Field( None, description="Elastic anisotropy.", ) homogeneous_poisson: float | None = Field( None, description="Poisson's ratio.", ) elastic_tensor: list[list[float]] | None = Field( None, description="Stiffness tensor in GPa.", ) compliance_tensor: list[list[float]] | None = Field( None, description="Compliance tensor in 10^(-12)/Pa.", ) class ElasticityDoc(BaseModel): """ Model for a document containing elasticity data """ pretty_formula: str | None = Field( None, description="Cleaned representation of the material formula", ) chemsys: str | None = Field( None, description="Dash-delimited string of elements in the material.", ) elasticity: ElasticityData | None = Field( None, description="Elasticity data for the material.", ) task_id: str | None = Field( None, description="The Materials Project ID of the material. This comes in the form: mp-******.", ) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8160295 emmet_core-0.87.2/emmet/core/electrode.py0000644000175100017510000005137015240717720017767 0ustar00runnerrunnerfrom __future__ import annotations import re from collections import defaultdict from functools import cached_property from typing import TYPE_CHECKING, overload from pydantic import BaseModel, Field from emmet.core.base import EmmetBaseModel from emmet.core.io.pymatgen import ( AbstractElectrode, Composition, ComputedEntry, ComputedStructureEntry, ConversionElectrode, DummySpecies, Element, InsertionElectrode, PhaseDiagram, Species, ) from emmet.core.types.enums import BatteryType from emmet.core.types.pymatgen_types.balanced_reaction_adapter import ( BalancedReactionType, ) from emmet.core.types.pymatgen_types.composition_adapter import CompositionType from emmet.core.types.pymatgen_types.electrode_adapter import ( ConversionElectrodeType, InsertionElectrodeType, ) from emmet.core.types.pymatgen_types.element_adapter import ElementType from emmet.core.types.pymatgen_types.structure_adapter import StructureType from emmet.core.types.typing import ( DateTimeType, MaterialIdentifierType, validate_compound_identifier, ) from emmet.core.utils import type_override, utcnow if TYPE_CHECKING: from typing import Literal from emmet.core.types.typing import CompoundIDType @overload def validate_battery_id( idx: str, as_components: Literal[True] = True ) -> CompoundIDType: ... @overload def validate_battery_id(idx: str, as_components: Literal[False] = False) -> str: ... def validate_battery_id(idx: str, as_components: bool = False) -> str | CompoundIDType: """Validate an insertion electode battery ID.""" return validate_compound_identifier( idx, suffixes=(Element,), separator="_", use_prefix=True, as_components=as_components, ) class VoltagePairDoc(BaseModel): """ Data for individual voltage steps. Note: Each voltage step is represented as a sub_electrode (ConversionElectrode/InsertionElectrode) object to gain access to some basic statistics about the voltage step """ formula_charge: str | None = Field( None, description="The chemical formula of the charged material." ) formula_discharge: str | None = Field( None, description="The chemical formula of the discharged material." ) max_delta_volume: float | None = Field( None, description="Volume changes in % for a particular voltage step using: " "max(charge, discharge) / min(charge, discharge) - 1.", ) average_voltage: float | None = Field( None, description="The average voltage in V for a particular voltage step." ) capacity_grav: float | None = Field( None, description="Gravimetric capacity in mAh/g." ) capacity_vol: float | None = Field( None, description="Volumetric capacity in mAh/cc." ) energy_grav: float | None = Field( None, description="Gravimetric energy (Specific energy) in Wh/kg." ) energy_vol: float | None = Field( None, description="Volumetric energy (Energy Density) in Wh/l." ) fracA_charge: float | None = Field( None, description="Atomic fraction of the working ion in the charged state." ) fracA_discharge: float | None = Field( None, description="Atomic fraction of the working ion in the discharged state." ) @classmethod def from_sub_electrode(cls, sub_electrode: AbstractElectrode, **kwargs): """ Convert a pymatgen electrode object to a document """ return cls(**sub_electrode.get_summary_dict(), **kwargs) @type_override( {"id_charge": MaterialIdentifierType, "id_discharge": MaterialIdentifierType} ) class InsertionVoltagePairDoc(VoltagePairDoc): """ Features specific to insertion electrode """ stability_charge: float | None = Field( None, description="The energy above hull of the charged material in eV/atom." ) stability_discharge: float | None = Field( None, description="The energy above hull of the discharged material in eV/atom." ) id_charge: MaterialIdentifierType | int | None = Field( None, description="The Materials Project ID of the charged structure." ) id_discharge: MaterialIdentifierType | int | None = Field( None, description="The Materials Project ID of the discharged structure." ) class ConversionVoltagePairDoc(VoltagePairDoc): """ Features specific to conversion electrode """ reaction: BalancedReactionType | None = Field( None, description="The reaction that characterizes that particular voltage step.", ) @type_override({"all_elements": list[ElementType]}) class EntriesCompositionSummary(BaseModel): """ Composition summary data for all material entries associated with this electrode. Included to enable better searching via the API. """ all_formulas: list[str] | None = Field( None, description="Reduced formulas for material entries across all voltage pairs.", ) all_chemsys: list[str] | None = Field( None, description="Chemical systems for material entries across all voltage pairs.", ) all_formula_anonymous: list[str] | None = Field( None, description="Anonymous formulas for material entries across all voltage pairs.", ) all_elements: list[ElementType | Species | DummySpecies] | None = Field( None, description="Elements in material entries across all voltage pairs.", ) all_composition_reduced: dict[str, list[float]] | None = Field( None, description="Composition reduced data for entries across all voltage pairs.", ) @classmethod def from_compositions(cls, compositions: list[Composition]): all_formulas = list({comp.reduced_formula for comp in compositions}) all_chemsys = list({comp.chemical_system for comp in compositions}) all_formula_anonymous = list({comp.anonymized_formula for comp in compositions}) all_elements = sorted(compositions)[-1].elements all_composition_reduced = defaultdict(set) for comp in compositions: comp_red = comp.get_reduced_composition_and_factor()[0].as_dict() for ele, num in comp_red.items(): all_composition_reduced[ele].add(num) return cls( all_formulas=all_formulas, all_chemsys=all_chemsys, all_formula_anonymous=all_formula_anonymous, all_elements=all_elements, all_composition_reduced={ k: sorted(list(v)) for k, v in all_composition_reduced.items() }, ) class BaseElectrode(EmmetBaseModel): battery_type: BatteryType | None = Field( None, description="The type of battery (insertion or conversion)." ) material_ids: list[MaterialIdentifierType] | None = Field( None, description="The ids of all structures that matched to the present host lattice, regardless of stability. " "The stable entries can be found in the adjacent pairs.", ) thermo_type: str | None = Field( None, description="The functional type used to compute the thermodynamics of this electrode document.", ) battery_formula: str | None = Field( None, description="Reduced formula with working ion range produced by combining the charge and discharge formulas.", ) working_ion: ElementType | None = Field( None, description="The working ion as an Element object." ) num_steps: int | None = Field( None, description="The number of distinct voltage steps in from fully charge to " "discharge based on the stable intermediate states.", ) max_voltage_step: float | None = Field( None, description="Maximum absolute difference in adjacent voltage steps." ) last_updated: DateTimeType = Field( description="Timestamp for the most recent calculation for this Material document.", ) framework: CompositionType | None = Field( None, description="The chemical compositions of the host framework." ) framework_formula: str | None = Field( None, description="The id for this battery document." ) elements: list[ElementType] | None = Field( None, description="The atomic species contained in this electrode (not including the working ion).", ) nelements: int | None = Field( None, description="The number of elements in the material (not including the working ion).", ) chemsys: str | None = Field( None, description="The chemical system this electrode belongs to (not including the working ion).", ) formula_anonymous: str | None = Field( None, title="Anonymous Formula", description="Anonymized representation of the formula (not including the working ion).", ) warnings: list[str] | None = Field( None, description="Any warnings related to this electrode data." ) @cached_property def battery_id(self) -> str: """Retrieve battery ID from other fields.""" if not self.material_ids or not self.working_ion: raise ValueError("No battery identifer could be determined.") min_mpid = min( idx for idx in self.material_ids if not idx.string.startswith("mvc") ) return validate_battery_id(f"{min_mpid}_{self.working_ion}") class InsertionElectrodeDoc(InsertionVoltagePairDoc, BaseElectrode): """ Insertion electrode """ host_structure: StructureType | None = Field( None, description="Host structure (structure without the working ion)." ) adj_pairs: list[InsertionVoltagePairDoc] | None = Field( None, description="Returns all of the voltage steps material pairs." ) entries_composition_summary: EntriesCompositionSummary | None = Field( None, description="Composition summary data for all material in entries across all voltage pairs.", ) electrode_object: InsertionElectrodeType | None = Field( None, description="The Pymatgen electrode object.", ) @classmethod def from_entries( cls, grouped_entries: list[ComputedStructureEntry], working_ion_entry: ComputedEntry, strip_structures: bool = False, ) -> InsertionElectrodeDoc | None: try: ie = InsertionElectrode.from_entries( entries=grouped_entries, working_ion_entry=working_ion_entry, strip_structures=strip_structures, ) except IndexError: return None d = cls.get_elec_doc(ie) d["last_updated"] = utcnow() stripped_host = ie.fully_charged_entry.structure.copy() stripped_host.remove_species([d["working_ion"]]) elements = stripped_host.composition.elements chemsys = stripped_host.composition.chemical_system framework = Composition(d["framework_formula"]) dchg_comp = Composition(d["formula_discharge"]) battery_formula = get_battery_formula( Composition(d["formula_charge"]), dchg_comp, ie.working_ion, ) compositions = [] for doc in d["adj_pairs"]: compositions.append(Composition(doc["formula_charge"])) compositions.append(Composition(doc["formula_discharge"])) entries_composition_summary = EntriesCompositionSummary.from_compositions( compositions ) # Check if more than one working ion per transition metal and warn warnings = [] if any([element.is_transition_metal for element in dchg_comp]): transition_metal_fraction = sum( [ dchg_comp.get_atomic_fraction(elem) for elem in dchg_comp if elem.is_transition_metal ] ) if ( dchg_comp.get_atomic_fraction(ie.working_ion) / transition_metal_fraction > 1.0 ): warnings.append("More than one working ion per transition metal") else: warnings.append("Transition metal not found") return cls( battery_type="insertion", # type: ignore host_structure=stripped_host.as_dict(), framework=framework, battery_formula=battery_formula, electrode_object=ie, elements=elements, nelements=len(elements), chemsys=chemsys, formula_anonymous=framework.anonymized_formula, entries_composition_summary=entries_composition_summary, warnings=warnings, **d, ) @staticmethod def get_elec_doc(ie: InsertionElectrode) -> dict: """ Gets a summary doc for an InsertionElectrode object. Similar to InsertionElectrode.get_summary_dict() with modifications specific to the Materials Project. Args: ie (pymatgen InsertionElectrode): electrode_object Returns: summary doc """ entries = ie.get_all_entries() def get_dict_from_elec(ie): d = { "average_voltage": ie.get_average_voltage(), "max_voltage": ie.max_voltage, "min_voltage": ie.min_voltage, "max_delta_volume": ie.max_delta_volume, "max_voltage_step": ie.max_voltage_step, "capacity_grav": ie.get_capacity_grav(), "capacity_vol": ie.get_capacity_vol(), "energy_grav": ie.get_specific_energy(), "energy_vol": ie.get_energy_density(), "working_ion": ie.working_ion.symbol, "num_steps": ie.num_steps, "fracA_charge": ie.voltage_pairs[0].frac_charge, "fracA_discharge": ie.voltage_pairs[-1].frac_discharge, "framework_formula": ie.framework_formula, "id_charge": ie.fully_charged_entry.data["material_id"], "formula_charge": ie.fully_charged_entry.composition.reduced_formula, "id_discharge": ie.fully_discharged_entry.data["material_id"], "formula_discharge": ie.fully_discharged_entry.composition.reduced_formula, "max_instability": ie.get_max_instability(), "min_instability": ie.get_min_instability(), "material_ids": [itr_ent.data["material_id"] for itr_ent in entries], "stable_material_ids": [ itr_ent.data["material_id"] for itr_ent in ie.get_stable_entries() ], "unstable_material_ids": [ itr_ent.data["material_id"] for itr_ent in ie.get_unstable_entries() ], } if all("decomposition_energy" in e.data for e in entries): thermo_data = { "stability_charge": ie.fully_charged_entry.data[ "decomposition_energy" ], "stability_discharge": ie.fully_discharged_entry.data[ "decomposition_energy" ], "stability_data": { e.entry_id: e.data["decomposition_energy"] for e in entries }, } else: thermo_data = { "stability_charge": None, "stability_discharge": None, "stability_data": {}, } d.update(thermo_data) return d d = get_dict_from_elec(ie) d["adj_pairs"] = list( map(get_dict_from_elec, ie.get_sub_electrodes(adjacent_only=True)) ) return d class ConversionElectrodeDoc(ConversionVoltagePairDoc, BaseElectrode): """ Conversion electrode """ initial_comp_formula: str | None = Field( None, description="The starting composition for the ConversionElectrode represented as a string/formula.", ) adj_pairs: list[ConversionVoltagePairDoc] | None = Field( None, description="Returns all of the voltage steps material pairs." ) electrode_object: ConversionElectrodeType | None = Field( None, description="The Pymatgen conversion electrode object." ) @classmethod def from_composition_and_entries( cls, composition: Composition, entries: list[ComputedEntry], working_ion_symbol: str, battery_id: str, thermo_type: str, ): ce = ConversionElectrode.from_composition_and_entries( comp=composition, entries_in_chemsys=entries, working_ion_symbol=working_ion_symbol, ) d = cls.get_conversion_elec_doc(ce) # type: ignore[arg-type] return cls(battery_id=battery_id, thermo_type=thermo_type, **d) @classmethod def from_composition_and_pd( cls, comp: Composition, pd: PhaseDiagram, working_ion_symbol: str, battery_id: str, thermo_type: str, ): ce = ConversionElectrode.from_composition_and_pd( comp=comp, pd=pd, working_ion_symbol=working_ion_symbol ) d = cls.get_conversion_elec_doc(ce) # type: ignore[arg-type] return cls(battery_id=battery_id, thermo_type=thermo_type, **d) @staticmethod def get_conversion_elec_doc(ce: ConversionElectrode) -> dict: """ Gets a summary doc for a ConversionElectrode object. Args: ie (pymatgen ConversionElectrode): electrode_object Returns: summary doc """ def get_dict_from_conversion_elec(ce): fracA_charge = ce.voltage_pairs[0].frac_charge fracA_discharge = ce.voltage_pairs[-1].frac_discharge x_charge = fracA_charge * ce.framework.num_atoms / (1 - fracA_charge) x_discharge = ( fracA_discharge * ce.framework.num_atoms / (1 - fracA_discharge) ) comp_charge = ce.framework + {ce.working_ion.symbol: x_charge} comp_discharge = ce.framework + {ce.working_ion.symbol: x_discharge} battery_formula = get_battery_formula( comp_charge, comp_discharge, ce.working_ion, ) d = { "battery_type": "conversion", "battery_formula": battery_formula, "framework": ce.framework, "framework_formula": ce.framework_formula, "initial_comp_formula": ce.initial_comp_formula, "chemsys": ce.framework.chemical_system, "elements": ce.framework.elements, "nelements": len(ce.framework.elements), "formula_anonymous": ce.framework.anonymized_formula, "electrode_object": ce.as_dict(), "average_voltage": ce.get_average_voltage(), "max_voltage": ce.max_voltage, "min_voltage": ce.min_voltage, "max_delta_volume": ce.max_delta_volume, "max_voltage_step": ce.max_voltage_step, "capacity_grav": ce.get_capacity_grav(), "capacity_vol": ce.get_capacity_vol(), "energy_grav": ce.get_specific_energy(), "energy_vol": ce.get_energy_density(), "working_ion": ce.working_ion.symbol, "num_steps": ce.num_steps, "fracA_charge": fracA_charge, "fracA_discharge": fracA_discharge, "formula_charge": comp_charge.reduced_formula, "formula_discharge": comp_discharge.reduced_formula, "reaction": ce.voltage_pairs[0].rxn.as_dict(), "last_updated": utcnow(), } return d d = get_dict_from_conversion_elec(ce) d["adj_pairs"] = list( map( get_dict_from_conversion_elec, ce.get_sub_electrodes(adjacent_only=True) ) ) return d def get_battery_formula( charge_comp: Composition, discharge_comp: Composition, working_ion: Element ): working_ion_subscripts = [] for comp in [charge_comp, discharge_comp]: comp_dict = comp.get_el_amt_dict() working_ion_num = ( comp_dict.pop(working_ion.value) if working_ion.value in comp_dict else 0 ) temp_comp = Composition.from_dict(comp_dict) (temp_reduced, n) = temp_comp.get_reduced_composition_and_factor() new_subscript = re.sub(".00$", "", "{:.2f}".format(working_ion_num / n)) if new_subscript != "0": new_subscript = new_subscript.rstrip("0") working_ion_subscripts.append(new_subscript) return ( working_ion.value + "-".join(working_ion_subscripts) + temp_reduced.reduced_formula ) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8162715 emmet_core-0.87.2/emmet/core/electronic_structure.py0000644000175100017510000005565415240717720022301 0ustar00runnerrunner"""Core definition of an Electronic Structure""" from __future__ import annotations from collections import defaultdict from math import isnan from typing import TYPE_CHECKING, Annotated, Generator, Literal, TypeVar import numpy as np from pydantic import BaseModel, BeforeValidator, Field, WrapSerializer from emmet.core.io.pymatgen import ( CollinearMagneticStructureAnalyzer, Ordering, BandStructureSymmLine, OrbitalType, Spin, MPStaticSet, SpacegroupAnalyzer, ) from emmet.core.material import PropertyOrigin from emmet.core.material_property import PropertyDoc from emmet.core.settings import EmmetSettings from emmet.core.types.enums import ValueEnum from emmet.core.types.pymatgen_types.bandstructure_symm_line_adapter import ( TypedBandDict, ) from emmet.core.types.pymatgen_types.element_adapter import ElementType from emmet.core.types.typing import IdentifierType if TYPE_CHECKING: from typing import Any from emmet.core.io.pymatgen import Structure from typing_extensions import Self from emmet.core.types.electronic_structure import BSShim, DosShim SETTINGS = EmmetSettings() OrderingType = Annotated[ Ordering, BeforeValidator(lambda x: Ordering(x) if isinstance(x, str) else x), WrapSerializer(lambda x, nxt, info: x.value, return_type=str), ] class DOSProjectionType(ValueEnum): total = "total" elemental = "elemental" orbital = "orbital" class ElectronicStructureBaseData(BaseModel): band_gap: float = Field(..., description="Band gap energy in eV.") cbm: float | None = Field(None, description="Conduction band minimum data.") vbm: float | None = Field(None, description="Valence band maximum data.") efermi: float | None = Field(None, description="Fermi energy in eV.") class ElectronicStructureSummary(ElectronicStructureBaseData): is_gap_direct: bool | None = Field( None, description="Whether the band gap is direct." ) is_metal: bool | None = Field(None, description="Whether the material is a metal.") magnetic_ordering: OrderingType | None = Field( None, description="Magnetic ordering of the calculation." ) def _deser_cbm_vbm(band: Any) -> TypedBandDict: """Validate annotated CBM and VBM dicts.""" if ( isinstance(band, dict) and isinstance(band.get("kpoint"), dict) and "label" not in band["kpoint"] ): band["kpoint"]["label"] = None return band class BandStructureSummaryData(ElectronicStructureSummary): """Schematize high-level band structure data for the API.""" task_id: IdentifierType | None = Field( None, description="The source calculation (task) ID that this band structure comes from.", ) nbands: float = Field(..., description="Number of bands.") direct_gap: float = Field(..., description="Direct gap energy in eV.") cbm: Annotated[TypedBandDict | None, BeforeValidator(_deser_cbm_vbm)] | None = ( Field(None, description="Conduction band minimum data.") ) vbm: Annotated[TypedBandDict | None, BeforeValidator(_deser_cbm_vbm)] | None = ( Field(None, description="Valence band maximum data.") ) class DosSummaryData(ElectronicStructureBaseData): """Schematize high-level DOS data for the API.""" spin_polarization: float | None = Field( None, description="Spin polarization at the fermi level." ) class BandstructureData(BaseModel): setyawan_curtarolo: BandStructureSummaryData | None = Field( None, description="Band structure summary data using the Setyawan-Curtarolo path convention.", ) hinuma: BandStructureSummaryData | None = Field( None, description="Band structure summary data using the Hinuma et al. path convention.", ) latimer_munro: BandStructureSummaryData | None = Field( None, description="Band structure summary data using the Latimer-Munro path convention.", ) SpinTypeVar = TypeVar("SpinTypeVar", Spin, Literal["1", "-1"]) SpinType = Annotated[ SpinTypeVar, BeforeValidator(lambda x: Spin(int(x)) if isinstance(x, str) else x), WrapSerializer(lambda x, nxt, info: str(x), return_type=str), ] def _deser_elemental( elemental: dict, ) -> dict[str, dict[str, dict[str, DosSummaryData]]]: """Validate DosData.elemental field.""" if isinstance(next(iter(elemental.values())), list): elemental = { element: { oribital_type: { spin: summary_data for spin, summary_data in spin_summary_pairs } for oribital_type, spin_summary_pairs in orbital } for element, orbital in elemental.items() } return elemental def _deser_orbital(orbital): """Validate DosData.orbital field.""" if isinstance(next(iter(orbital.values())), list): orbital = { oribital_type: { spin: summary_data for spin, summary_data in spin_summary_pairs } for oribital_type, spin_summary_pairs in orbital.items() } return orbital class DosData(BaseModel): task_id: IdentifierType | None = Field( None, description="The source calculation (task) ID that this density of states comes from.", ) total: dict[SpinType, DosSummaryData] | None = Field( None, description="Total DOS summary data." ) elemental: Annotated[ dict[ ElementType, dict[Literal["s", "p", "d", "f", "total"], dict[SpinType, DosSummaryData]], ] | None, BeforeValidator(_deser_elemental), ] = Field( None, description="Band structure summary data using the Hinuma et al. path convention.", ) orbital: Annotated[ dict[Literal["s", "p", "d", "f", "total"], dict[SpinType, DosSummaryData]] | None, BeforeValidator(_deser_orbital), ] = Field( None, description="Band structure summary data using the Latimer-Munro path convention.", ) magnetic_ordering: OrderingType | None = Field( None, description="Magnetic ordering of the calculation." ) class ElectronicStructureDoc(PropertyDoc, ElectronicStructureSummary): """ Definition for a core Electronic Structure Document """ property_name: str = "electronic_structure" bandstructure: BandstructureData | None = Field( None, description="Band structure data for the material." ) dos: DosData | None = Field( None, description="Density of states data for the material." ) @classmethod def from_bs( cls, bandstructures: BSShim, origins: list[PropertyOrigin], structures: dict[IdentifierType, Structure], **kwargs, ) -> Self: """ Builds an electronic structure document using band structure data. Args: bandstructures (BSShim): Struct of bandstructures with identifiers. origins (list[PropertyOrigin]): Optional origins information for final doc. structures (dict[AlphaID or MPID, Structure]) = Dictionary mapping a calculation (task) ID to the structures used as inputs. This is to ensures correct magnetic moment information is included. material_id (AlphaID or MPID): A material ID. """ bs_data = _generate_bs_data(bandstructures, origins, structures) origins = [origin for origin in origins] + [bs_data["es_origins_from_bs"]] return bs_checks( cls.from_structure( band_gap=bs_data["band_gap"], cbm=bs_data["cbm"], vbm=bs_data["vbm"], efermi=bs_data["efermi"], is_gap_direct=bs_data["is_gap_direct"], is_metal=bs_data["is_metal"], magnetic_ordering=bs_data["bs_magnetic_ordering"], bandstructure=bs_data["bandstructure"], origins=origins, **kwargs, ), structures, bandstructures, ) @classmethod def from_dos( cls, dos: DosShim, is_gap_direct: bool, origins: list[PropertyOrigin], structures: dict[IdentifierType, Structure], **kwargs, ) -> Self: """ Builds an electronic structure document using density of states data. Args: dos (DosShim): Struct with a CompleteDos and identifier. is_gap_direct (bool): Direct gap indicator included at root level of document, result of VASP outputs. origins (list[PropertyOrigin]): Origins information for final doc. structures (dict[AlphaID or MPID, Structure]) = Dictionary mapping a calculation (task) ID to the structures used as inputs. This is to ensures correct magnetic moment information is included. material_id (AlphaID or MPID): A material ID. """ dos_data = _generate_dos_data(dos, origins, structures) origins = [origin for origin in origins] + [dos_data["es_origins_from_dos"]] return dos_checks( cls.from_structure( band_gap=dos_data["band_gap"], cbm=dos_data["cbm"], vbm=dos_data["vbm"], efermi=dos_data["efermi"], is_gap_direct=is_gap_direct, is_metal=dos_data["is_metal"], magnetic_ordering=dos_data["dos_magnetic_ordering"], dos=dos_data["dos_entry"], origins=origins, **kwargs, ), structures, dos, ) @classmethod def from_bsdos( cls, bandstructures: BSShim, dos: DosShim, origins: list[PropertyOrigin], structures: dict[IdentifierType, Structure], **kwargs, ) -> Self: """ Builds an electronic structure document using band structure and density of states data. Args: bandstructures (BSShim): Struct of bandstructures with identifiers. dos (DosShim): Struct with a CompleteDos and identifier. origins (list[PropertyOrigin]): Origins information for final doc. structures (dict[AlphaID or MPID, Structure]) = Dictionary mapping a calculation (task) ID to the structures used as inputs. This is to ensures correct magnetic moment information is included. material_id (AlphaID or MPID): A material ID. """ bs_data = _generate_bs_data(bandstructures, origins, structures) dos_data = _generate_dos_data(dos, origins, structures) # TODO: add ability to add blessed structure from material into ranking # for es origins, i.e., r2SCAN static/relax > GGA NSCF line > ... origins = [origin for origin in origins] + [bs_data["es_origins_from_bs"]] magnetic_ordering = bs_data["bs_magnetic_ordering"] return bsdos_checks( cls.from_structure( band_gap=bs_data["band_gap"], cbm=bs_data["cbm"], vbm=bs_data["vbm"], efermi=bs_data["efermi"], is_gap_direct=bs_data["is_gap_direct"], is_metal=bs_data["is_metal"], magnetic_ordering=magnetic_ordering, bandstructure=bs_data["bandstructure"], dos=dos_data["dos_entry"], origins=origins, **kwargs, ), structures, bandstructures, dos, ) def _generate_bs_data( bandstructures: BSShim, origins: list[PropertyOrigin], structures: dict[IdentifierType, Structure], ) -> dict: bs_data = { # type: ignore "setyawan_curtarolo": bandstructures.setyawan_curtarolo, "hinuma": bandstructures.hinuma, "latimer_munro": bandstructures.latimer_munro, } bs_type: str bs_input: tuple[IdentifierType, BandStructureSymmLine, int] bs_task_id: IdentifierType bs: BandStructureSymmLine for bs_type, bs_input in bs_data.items(): if bs_input is not None: bs_task_id, bs, _ = bs_input bs_mag_ordering = CollinearMagneticStructureAnalyzer( structures[bs_task_id] ).ordering gap_dict = bs.get_band_gap() is_metal = bs.is_metal() direct_gap = bs.get_direct_band_gap() if is_metal: band_gap = 0.0 cbm = None # type: ignore[assignment] vbm = None # type: ignore[assignment] is_gap_direct = False else: band_gap = gap_dict["energy"] cbm = bs.get_cbm() # type: ignore[assignment] vbm = bs.get_vbm() # type: ignore[assignment] is_gap_direct = gap_dict["direct"] # coerce type here, mixture of str and int types in bs objects cbm["kpoint_index"] = [int(x) for x in cbm["kpoint_index"]] # type: ignore[index] vbm["kpoint_index"] = [int(x) for x in vbm["kpoint_index"]] # type: ignore[index] bs_efermi = bs.efermi nbands = bs.nb_bands bs_data[bs_type] = BandStructureSummaryData( # type: ignore task_id=bs_task_id, band_gap=band_gap, direct_gap=direct_gap, cbm=cbm, vbm=vbm, is_gap_direct=is_gap_direct, is_metal=is_metal, efermi=bs_efermi, nbands=nbands, magnetic_ordering=bs_mag_ordering, ) def _bs_eval( bs_data: dict[str, BandStructureSymmLine | None], bs_rank: list[str] = ["latimer_munro", "hinuma", "setyawan_curtarolo"], ) -> Generator[str, None, None]: for bs_type in bs_rank: if bs_data[bs_type] is not None: yield bs_type blessed_bs_key = next(_bs_eval(bs_data)) bs_entry = BandstructureData(**bs_data) # type: ignore band_gap = getattr(bs_entry, blessed_bs_key).band_gap cbm = (getattr(bs_entry, blessed_bs_key).cbm or {}).get("energy", None) # type: ignore vbm = (getattr(bs_entry, blessed_bs_key).vbm or {}).get("energy", None) # type: ignore efermi = getattr(bs_entry, blessed_bs_key).efermi # type: ignore is_gap_direct = getattr(bs_entry, blessed_bs_key).is_gap_direct # type: ignore is_metal = getattr(bs_entry, blessed_bs_key).is_metal # type: ignore for origin in origins: if origin.name == blessed_bs_key: es_origins_from_bs = PropertyOrigin( name="electronic_structure", last_updated=origin.last_updated, task_id=origin.task_id, ) bs_magnetic_ordering = CollinearMagneticStructureAnalyzer( structures[es_origins_from_bs.task_id], round_magmoms=True, threshold_nonmag=0.2, threshold=0, ).ordering return { "band_gap": band_gap, "cbm": cbm, "vbm": vbm, "efermi": efermi, "is_gap_direct": is_gap_direct, "is_metal": is_metal, "bs_magnetic_ordering": bs_magnetic_ordering, "bandstructure": bs_entry, "es_origins_from_bs": es_origins_from_bs, } def _generate_dos_data( dos: DosShim, origins: list[PropertyOrigin], structures: dict[IdentifierType, Structure], ) -> dict: dos_task, dos_obj, _ = dos.dos orbitals = [OrbitalType.s, OrbitalType.p, OrbitalType.d] spins = list(dos_obj.densities.keys()) ele_dos = dos_obj.get_element_dos() tot_orb_dos = dos_obj.get_spd_dos() elements = ele_dos.keys() dos_efermi = dos_obj.efermi structure = structures[dos_task] dos_magnetic_ordering = CollinearMagneticStructureAnalyzer(structure).ordering dos_data = { "task_id": dos_task, "total": defaultdict(dict), "elemental": {element: defaultdict(dict) for element in elements}, "orbital": defaultdict(dict), "magnetic_ordering": dos_magnetic_ordering, } for spin in spins: # - Process total DOS data band_gap = dos_obj.get_gap(spin=spin) (cbm, vbm) = dos_obj.get_cbm_vbm(spin=spin) try: spin_polarization = dos_obj.spin_polarization if spin_polarization is None or isnan(spin_polarization): spin_polarization = None except KeyError: spin_polarization = None dos_data["total"][spin] = DosSummaryData( # type: ignore[index] band_gap=band_gap, cbm=cbm, vbm=vbm, efermi=dos_efermi, spin_polarization=spin_polarization, ) # - Process total orbital projection data for orbital in orbitals: band_gap = tot_orb_dos[orbital].get_gap(spin=spin) (cbm, vbm) = tot_orb_dos[orbital].get_cbm_vbm(spin=spin) spin_polarization = None dos_data["orbital"][str(orbital)][spin] = DosSummaryData( # type: ignore[index] band_gap=band_gap, cbm=cbm, vbm=vbm, efermi=dos_efermi, spin_polarization=spin_polarization, ) # - Process element and element orbital projection data for ele in ele_dos: orb_dos = dos_obj.get_element_spd_dos(ele) for orbital in ["total"] + list(orb_dos.keys()): # type: ignore[assignment] if orbital == "total": proj_dos = ele_dos label = ele else: proj_dos = orb_dos label = orbital for spin in spins: band_gap = proj_dos[label].get_gap(spin=spin) (cbm, vbm) = proj_dos[label].get_cbm_vbm(spin=spin) spin_polarization = None dos_data["elemental"][ele][str(orbital)][spin] = DosSummaryData( # type: ignore[index] band_gap=band_gap, cbm=cbm, vbm=vbm, efermi=dos_efermi, spin_polarization=spin_polarization, ) dos_entry = DosData(**dos_data) # type: ignore[arg-type] dos_cbm, dos_vbm = dos_obj.get_cbm_vbm() dos_gap = max(dos_cbm - dos_vbm, 0.0) is_metal = True if np.isclose(dos_gap, 0.0, atol=0.01, rtol=0) else False es_origins_from_dos = None for origin in origins: if origin.task_id == dos_task: es_origins_from_dos = PropertyOrigin( name="electronic_structure", last_updated=origin.last_updated, task_id=dos_task, ) return { "band_gap": dos_gap, "cbm": dos_cbm, "vbm": dos_vbm, "efermi": dos_efermi, "is_metal": is_metal, "dos_magnetic_ordering": dos_magnetic_ordering, "es_origins_from_dos": es_origins_from_dos, "dos_entry": dos_entry, } def bs_checks( doc: ElectronicStructureDoc, structures: dict[str, Structure], bandstructures: BSShim, skip_primitive_check: bool = False, ) -> ElectronicStructureDoc: assert doc.bandstructure is not None for _, bs_summary in doc.bandstructure: if bs_summary is not None: _bandgap_diff_check(doc, bs_summary.band_gap, bs_summary.task_id) mag_orderings: list[tuple[str, Ordering]] = [ (bs_summary.task_id, bs_summary.magnetic_ordering) for _, bs_summary in doc.bandstructure if bs_summary is not None ] _magnetic_ordering_check(doc, mag_orderings) if not skip_primitive_check: _structure_primitive_checks(doc, structures) for _, bandstructure in bandstructures: if bandstructure is not None: task_id, _, lmaxmix = bandstructure _lmaxmix_check(doc, structures[task_id], lmaxmix, task_id) return doc def dos_checks( doc: ElectronicStructureDoc, structures: dict[str, Structure], dos: DosShim, skip_primitive_check: bool = False, ) -> ElectronicStructureDoc: assert doc.dos is not None assert doc.dos.total is not None _bandgap_diff_check( doc, doc.dos.total[Spin.up].band_gap, doc.dos.task_id, ) mag_orderings: list[tuple[str, Ordering]] = [ ( # type: ignore[list-item] doc.dos.task_id, doc.dos.magnetic_ordering, ) ] _magnetic_ordering_check(doc, mag_orderings) if not skip_primitive_check: _structure_primitive_checks(doc, structures) task_id, dos_obj, lmaxmix = dos.dos _lmaxmix_check(doc, structures[task_id], lmaxmix, doc.dos.task_id) return doc def bsdos_checks( doc: ElectronicStructureDoc, structures: dict[str, Structure], bandstructures: BSShim, dos: DosShim, ) -> ElectronicStructureDoc: _structure_primitive_checks(doc, structures) return dos_checks( bs_checks( doc, structures, bandstructures, skip_primitive_check=True, ), structures, dos, skip_primitive_check=True, ) def _bandgap_diff_check( doc: ElectronicStructureDoc, band_gap: float, task_id: IdentifierType ) -> None: if abs(doc.band_gap - band_gap) > 0.25: doc.warnings.append( "Absolute difference between blessed band gap and the band gap for" f"task {str(task_id)} is larger than 0.25 eV.", ) def _magnetic_ordering_check( doc: ElectronicStructureDoc, mag_orderings: list[tuple[IdentifierType, Ordering]] ) -> None: for task_id, ordering in mag_orderings: if doc.magnetic_ordering != ordering: doc.warnings.append( f"Summary data magnetic ordering does not agree with the ordering from {str(task_id)}" ) def _lmaxmix_check( doc: ElectronicStructureDoc, structure: Structure, lmaxmix: int, task_id: IdentifierType, ) -> None: # VASP default LMAXMIX is 2 expected_lmaxmix = MPStaticSet(structure).incar.get("LMAXMIX", 2) if lmaxmix != expected_lmaxmix: doc.warnings.append( "An incorrect calculation parameter may lead to errors in the band gap of " f"0.1-0.2 eV (LMAXIX is {lmaxmix} and should be {expected_lmaxmix} for " f"{str(task_id)})." ) def _structure_primitive_checks( doc: ElectronicStructureDoc, structures: dict[IdentifierType, Structure] ) -> None: for task_id, struct in structures.items(): struct_prim = SpacegroupAnalyzer(struct).get_primitive_standard_structure( international_monoclinic=False ) if not np.allclose( struct.lattice.matrix, struct_prim.lattice.matrix, atol=1e-3 ): if np.isclose(struct_prim.volume, struct.volume, atol=5, rtol=0): doc.warnings.append( f"The input structure for {str(task_id)} is primitive but may not exactly match the " f"standard primitive setting." ) else: doc.warnings.append( f"The input structure for {str(task_id)} does not match the expected standard primitive" ) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8162715 emmet_core-0.87.2/emmet/core/eos.py0000644000175100017510000001212215240717720016577 0ustar00runnerrunner"""Define equation of state (EOS) schemas.""" from __future__ import annotations import warnings from typing import TYPE_CHECKING, Annotated from pydantic import BaseModel, BeforeValidator, Field from emmet.core.io.pymatgen import EOS, EOSError from emmet.core.types.enums import ValueEnum from emmet.core.types.typing import IdentifierType if TYPE_CHECKING: from collections.abc import Sequence from typing import Any class LegacyEOSModel(ValueEnum): """EOS models used to fit legacy data. Is overly permissive in allowing for spelling mistakes present in legacy data. """ MIE_GRUNEISEN = "mie_gruneisen" PACK_EVANS_JAMES = "pack_evans_james" BIRCH = "birch" VINET = "vinet" TAIT = "tait" BIRCH_EULER = "birch_euler" POIRIER_TARANTOLA = "poirier_tarantola" BIRCH_LAGRANGE = "birch_lagrange" MURNAGHAN = "murnaghan" BIRCH_MURNAGHAN = "birch_murnaghan" @classmethod def _missing_(cls, value): if value == "pourier_tarantola": return cls.POIRIER_TARANTOLA # Subset of EOS models known to pymatgen PYMATGEN_KNOWN_EOS_MODELS = { LegacyEOSModel(eos_name) for eos_name in ("murnaghan", "birch", "birch_murnaghan", "vinet") } class EOSFit(BaseModel): """Schematize fitted EOS data. The only required field is `model`. If no other fields are instantiated, it is assumed that the fit failed. """ model: LegacyEOSModel = Field(description="The EOS model used to fit the data.") V0: float | None = Field(None, description="The equilibrium volume in ų.") B0: float | None = Field(None, description="The equilibrium bulk modulus, in GPa.") B1: float | None = Field( None, description="The pressure derivative of the bulk modulus at V0, dimensionless.", ) E0: float | None = Field(None, description="The equilibrium energy, in eV.") R2: float | None = Field( None, description="The fit R2 (coefficient of determination), dimensionless." ) @classmethod def from_ev_data( cls, volumes: Sequence[float], energies: Sequence[float], model: str | LegacyEOSModel, ): eos_pars: dict[str, float] = {} eos_model = LegacyEOSModel(model) try: eos = EOS(eos_name=eos_model.value).fit(volumes, energies) eos_pars.update({k.upper(): eos.results[k] for k in ("e0", "v0", "b1")}) eos_pars["B0"] = float(eos.b0_GPa) sum_res_sq = ((eos.energies - eos.func(eos.volumes)) ** 2).sum() sum_tot_sq = ((eos.energies - eos.energies.mean()) ** 2).sum() eos_pars["R2"] = 1.0 - sum_res_sq / sum_tot_sq except EOSError: pass return cls(model=eos_model, **eos_pars) def _migrate_legacy_data( config: dict[str, Any] | list[dict[str, Any]] | None, ) -> list[EOSFit] | None: """Migrate legacy data to new EOS schema.""" migration = {"V0": "V0", "E0": "E0", "B": "B0", "C": "B1"} if isinstance(config, dict): config = [ EOSFit( # type: ignore[misc] **{v: config[model].get(k) for k, v in migration.items()}, model=LegacyEOSModel(model), ) for model in set(config).intersection({x.value for x in LegacyEOSModel}) # type: ignore[attr-defined] ] elif isinstance(config, list) and all(isinstance(x, dict) for x in config): config = [EOSFit(**x) for x in config] # type: ignore[misc] return config # type: ignore[return-value] class EOSDoc(BaseModel): """Fitted equations of state, and energy-volume fit data.""" task_id: IdentifierType | None = Field( None, description="task_id corresponding to this EOS document", ) energies: list[float] | None = Field( None, description="Energies in eV that the equations of state are plotted with.", ) volumes: list[float] | None = Field( None, description="Volumes in A³ that the equations of state are plotted with.", ) eos: Annotated[list[EOSFit] | None, BeforeValidator(_migrate_legacy_data)] = Field( None, description="Data for each type of equation of state.", ) @classmethod def from_ev_data( cls, volumes: Sequence[float], energies: Sequence[float], task_id: IdentifierType | None = None, models: list[str | LegacyEOSModel] | None = None, ): return cls( task_id=task_id, energies=energies, volumes=volumes, eos=[ EOSFit.from_ev_data(volumes, energies, model) for model in (models or PYMATGEN_KNOWN_EOS_MODELS) # type: ignore[union-attr] ], ) @property def material_id(self) -> str: """Temporarily enable access to deprecated attr.""" warnings.warn( "`material_id` has been renamed `task_id` in the EOS data. " "Please transition to using `task_id` by 1 July, 2026.", category=DeprecationWarning, stacklevel=2, ) return self.task_id # type: ignore[return-value] ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011028.9651582 emmet_core-0.87.2/emmet/core/featurization/0000755000175100017510000000000015240717725020332 5ustar00runnerrunner././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8162715 emmet_core-0.87.2/emmet/core/featurization/__init__.py0000644000175100017510000000220215240717720022432 0ustar00runnerrunner"""Define analysis tools to featurize crystallographic data. These modules are adapted from the following packages: - matminer: https://github.com/hackingmaterials/matminer/ - Specifically, the `matminer.featurizers` module - robocrys: https://github.com/hackingmaterials/robocrystallographer - All modules except the CLI Credits to the original authors: - matminer: Alex Dunn, Alireza Faghaninia, Anubhav Jain, Logan Ward, Nils E. R. Zimmermann - robocrys: Alex Ganose Author accrediation and references can be found within those packages. """ # emmet core featurizer uses different syntax than matminer's from emmet.core.featurization.featurizers import Featurizer, SiteStatsFingerprint try: # Use robocrys + matminer if installed from robocrys import StructureCondenser, StructureDescriber except ImportError: # Fall back to emmet if not installed. from emmet.core.featurization.robocrys import ( # type: ignore[no-redef] StructureCondenser, StructureDescriber, ) __all__ = [ "Featurizer", "SiteStatsFingerprint", "StructureCondenser", "StructureDescriber", ] ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8168995 emmet_core-0.87.2/emmet/core/featurization/featurizers.py0000644000175100017510000002225415240717720023247 0ustar00runnerrunner"""Basic schematic of a featurizer. Adapted from `matminer.featurizers.base` """ from __future__ import annotations from abc import ABC, abstractmethod from dataclasses import dataclass, field from enum import StrEnum from typing import TYPE_CHECKING import numpy as np from emmet.core.io.pymatgen import ( CrystalNN, LocalStructOrderParams, CN_OPT_PARAMS, Structure, ) if TYPE_CHECKING: from typing import Any, Literal from typing_extensions import Self class FeatureStats(StrEnum): mean = "mean" maximum = "maximum" minimum = "minimum" std_dev = "std_dev" CN_TARGET_MOTIF_OP: dict[int, list[str]] = { 1: ["sgl_bd"], 2: ["L-shaped", "water-like", "bent 120 degrees", "bent 150 degrees", "linear"], 3: ["trigonal planar", "trigonal non-coplanar", "T-shaped"], 4: [ "square co-planar", "tetrahedral", "rectangular see-saw-like", "see-saw-like", "trigonal pyramidal", ], 5: ["pentagonal planar", "square pyramidal", "trigonal bipyramidal"], 6: ["hexagonal planar", "octahedral", "pentagonal pyramidal"], 7: ["hexagonal pyramidal", "pentagonal bipyramidal"], 8: ["body-centered cubic", "hexagonal bipyramidal"], 9: ["q2", "q4", "q6"], 10: ["q2", "q4", "q6"], 11: ["q2", "q4", "q6"], 12: ["cuboctahedral", "q2", "q4", "q6"], } @dataclass class Featurizer(ABC): """Abstract class to calculate features from structural input data.""" @abstractmethod def featurize(self, *args, **kwargs) -> list[Any]: """ Main featurizer function, which has to be implemented in any derived featurizer subclass. Args: x: input data to featurize (type depends on featurizer). Returns: (list) one or more features. """ return [] @property @abstractmethod def feature_labels(self) -> list[str]: """ Generate feature labels. Returns: list of str, the labels of the features """ return [] @dataclass class CrystalNNFingerprint(Featurizer): """ A local order parameter fingerprint for periodic crystals. Adapted from `matminer.featurizers.site.fingerprint`. The fingerprint represents the value of various order parameters for the site. The "wt" order parameter describes how consistent a site is with a certain coordination number. The remaining order parameters are computed by multiplying the "wt" for that coordination number with the OP value. Args: op_types (dict): a dict of coordination number (int) to a list of str representing the order parameter types crystalnn (CrystalNN) : an instance of CrystalNN to use in identifying local coordination environments. """ op_types: dict[int, list[str]] = field(default_factory=dict) crystalnn: CrystalNN = field(default_factory=CrystalNN) _ops: dict[int, list[str | LocalStructOrderParams]] | None = None @classmethod def from_preset(cls, preset: Literal["cn", "ops"], **kwargs) -> Self: """ Use preset parameters to get the fingerprint Args: preset ('cn' | 'ops'): Initializes the featurizer to use coordination number ('cn') or structural order parameters like octahedral, tetrahedral ('ops'). **kwargs: other settings to be passed into CrystalNN class """ cnn = CrystalNN(**kwargs) match preset: case "cn": return cls(op_types={k + 1: ["wt"] for k in range(24)}, crystalnn=cnn) case "ops": return cls( op_types={ k: ["wt"] + CN_TARGET_MOTIF_OP.get(k, []) for k in range(1, 25) }, crystalnn=cnn, ) case _: raise ValueError( f"preset `{preset}` is not supported in `CrystalNNFingerprint`" ) @property def ops(self) -> dict[int, list[str | LocalStructOrderParams]]: if not self._ops: self._ops = {} # load order parameter objects & parameters for cn, t_list in self.op_types.items(): self._ops[cn] = [] for t in t_list: if t == "wt": self._ops[cn].append(t) else: ot = t p = None if cn in CN_OPT_PARAMS.keys(): if t in CN_OPT_PARAMS[cn].keys(): ot = CN_OPT_PARAMS[cn][t][0] # type: ignore[assignment] if len(CN_OPT_PARAMS[cn][t]) > 1: p = CN_OPT_PARAMS[cn][t][1] self._ops[cn].append( LocalStructOrderParams([ot], parameters=[p]) # type: ignore[list-item] ) return self._ops def featurize(self, struct: Structure, idx: int) -> list[float]: """ Get crystal fingerprint of site with given index in input structure. Args: struct (Structure): Pymatgen Structure object. idx (int): index of target site in structure. Returns: list of weighted order parameters of target site. """ nndata = self.crystalnn.get_nn_data(struct, idx) max_cn = sorted(self.op_types)[-1] cn_fingerprint = [] for k in range(max_cn): cn = k + 1 wt = nndata.cn_weights.get(cn, 0) if cn in self.ops: for op in self.ops[cn]: if op == "wt": cn_fingerprint.append(wt) elif wt == 0: cn_fingerprint.append(wt) elif isinstance(op, LocalStructOrderParams): neigh_sites = [d["site"] for d in nndata.cn_nninfo[cn]] opval = op.get_order_parameters( [struct[idx]] + neigh_sites, # type: ignore[arg-type] 0, indices_neighs=[i for i in range(1, len(neigh_sites) + 1)], )[0] opval = opval or 0 # handles None cn_fingerprint.append(wt * opval) return cn_fingerprint @property def feature_labels(self): labels = [] max_cn = sorted(self.op_types)[-1] for k in range(max_cn): cn = k + 1 if cn in list(self.ops.keys()): for op in self.op_types[cn]: labels.append(f"{op} CN_{cn}") return labels @dataclass class SiteStatsFingerprint(Featurizer): """Computes statistics of properties across all sites in a structure. Adapted and simplified significantly from `matminer.featurizers.structure.sites`. Args: site_featurizer : `Featurizer` Featurization to use on each site of a structure. stats : list of str Should be a list of statistics in `FeatureStats` """ site_featurizer: Featurizer = field( default_factory=lambda: CrystalNNFingerprint.from_preset( "ops", distance_cutoffs=None, x_diff_weight=None, ) ) stats: list[str] = field(default_factory=lambda: ["mean", "maximum"]) @staticmethod def get_stat(op: str | FeatureStats, vals: list[float]) -> float: # type: ignore[valid-type] """Compute statistics of a 1-D array. Args: op : str or FeatureStats A statistic name to compute vals : list[float] An ordered list of floats Returns: float, the statistic """ v = np.array(vals) match op: case "mean": return v.mean() case "maximum": return v.max() case "minimum": return v.min() case "std_dev": return v.std() case _: raise ValueError(f"Unknown operation {op}") def featurize(self, s: Structure) -> list[float] | list[list[float]]: # Get each feature for each site vals: list[list[float]] = [[] for t in self.site_featurizer.feature_labels] for i, site in enumerate(s.sites): opvalstmp = self.site_featurizer.featurize(s, i) for j, opval in enumerate(opvalstmp): vals[j].append(0.0 if opval is None else opval) # If the user does not request statistics, return the site features now if not self.stats: return vals # Compute the requested statistics return [self.get_stat(stat, v) for v in vals for stat in self.stats] # type: ignore[arg-type] @property def feature_labels(self) -> list[str]: orig_feature_labels = self.site_featurizer.feature_labels if self.stats: labels = [] # Make labels associated with the statistics for attr in orig_feature_labels: for stat in self.stats: labels.append(f"{stat} {attr}") return labels return orig_feature_labels ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011028.9656465 emmet_core-0.87.2/emmet/core/featurization/robocrys/0000755000175100017510000000000015240717725022174 5ustar00runnerrunner././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8168995 emmet_core-0.87.2/emmet/core/featurization/robocrys/__init__.py0000644000175100017510000000055115240717720024301 0ustar00runnerrunnerfrom __future__ import annotations from emmet.core.featurization.robocrys.condense.condenser import StructureCondenser from emmet.core.featurization.robocrys.describe.describer import StructureDescriber from emmet.core.featurization.robocrys.util import common_formulas __all__ = ["__version__", "StructureDescriber", "StructureCondenser", "common_formulas"] ././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1787011023.817032 emmet_core-0.87.2/emmet/core/featurization/robocrys/adapter.py0000644000175100017510000001214415240717720024163 0ustar00runnerrunner"""This module implements a class to resolve the symbolic references in condensed structure data. """ from __future__ import annotations from typing import TYPE_CHECKING if TYPE_CHECKING: from typing import Any class BaseAdapter: """Base adapter class for facilitating access to condensed structure data. Attributes: elements: The site elements. Args: condensed_structure: The condensed structure data, formatted as produced by :meth:`robocrys.condense.StructureCondenser.condense_structure`. """ def __init__(self, condensed_structure: dict[str, Any]): self._condensed_structure = condensed_structure self.elements = { site_index: site_data["element"] for site_index, site_data in self.sites.items() } def get_distance_details( self, from_site: int, to_sites: int | list[int] ) -> list[float]: """Gets the bond lengths between two sets of sites. Args: from_site: An inequivalent site index. to_sites: One or more inequivalent site indices. Returns: The distances between the sites. """ if isinstance(to_sites, int): # If only one to_site is provided turn it into a list to_sites = [to_sites] return [ distance for to_site in to_sites for distance in self.distances[from_site][to_site] ] def get_angle_details( self, from_site: int, to_sites: int | list[int], connectivity: str ) -> list[float]: """Gets the connectivity angles between two sets of sites. Args: from_site: An inequivalent site index. to_sites: One or more inequivalent site indices. connectivity: The site connectivity type. I.e. "corner", "edge", or "face". Returns: The distances between the sites. """ if isinstance(to_sites, int): # If only one to_site is provided turn it into a list to_sites = [to_sites] return [ angle for to_site in to_sites for angle in self.angles[from_site][to_site][connectivity] ] @property def mineral(self) -> dict[str, str | int | bool]: """The mineral data. See :meth:`robocrys.condense.StructureCondenser.condense_structure` for more details. """ return self._condensed_structure["mineral"] @property def formula(self) -> str: """The structure formula. See :meth:`robocrys.condense.StructureCondenser.condense_structure` for more details. """ return self._condensed_structure["formula"] @property def spg_symbol(self) -> str: """The space group symbol. See :meth:`robocrys.condense.StructureCondenser.condense_structure` for more details. """ return self._condensed_structure["spg_symbol"] @property def crystal_system(self) -> str: """The crystal system. See :meth:`robocrys.condense.StructureCondenser.condense_structure` for more details. """ return self._condensed_structure["crystal_system"] @property def dimensionality(self) -> int: """The overall dimensionality. See :meth:`robocrys.condense.StructureCondenser.condense_structure` for more details. """ return self._condensed_structure["dimensionality"] @property def sites(self) -> dict[int, dict[str, Any]]: """The site data. See :meth:`robocrys.condense.StructureCondenser.condense_structure` for more details. """ return self._condensed_structure["sites"] @property def distances(self) -> dict[int, dict[int, list[float]]]: """The distance data. See :meth:`robocrys.condense.StructureCondenser.condense_structure` for more details. """ return self._condensed_structure["distances"] @property def angles(self) -> dict[int, dict[int, dict[str, list[float]]]]: """The angle data. See :meth:`robocrys.condense.StructureCondenser.condense_structure` for more details. """ return self._condensed_structure["angles"] @property def components(self) -> dict[int, dict[str, Any]]: """The component data. See :meth:`robocrys.condense.StructureCondenser.condense_structure` for more details. """ return self._condensed_structure["components"] @property def component_makeup(self) -> list[int]: """The component makeup of the structure. See :meth:`robocrys.condense.StructureCondenser.condense_structure` for more details. """ return self._condensed_structure["component_makeup"] @property def is_vdw_heterostructure(self) -> bool: """Whether the structure is a vdw heterostructure. See :meth:`robocrys.condense.StructureCondenser.condense_structure` for more details. """ return bool(self._condensed_structure["vdw_heterostructure_info"]) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011028.9682212 emmet_core-0.87.2/emmet/core/featurization/robocrys/condense/0000755000175100017510000000000015240717725023772 5ustar00runnerrunner././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1787011023.817032 emmet_core-0.87.2/emmet/core/featurization/robocrys/condense/__init__.py0000644000175100017510000000000015240717720026064 0ustar00runnerrunner././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8171382 emmet_core-0.87.2/emmet/core/featurization/robocrys/condense/component.py0000644000175100017510000005736715240717720026363 0ustar00runnerrunner"""This module implements functions for handling structure components.""" from __future__ import annotations from copy import deepcopy from typing import TYPE_CHECKING, Any import networkx as nx import numpy as np from monty.fractions import gcd from emmet.core.io.pymatgen import ( StructureMatcher, Composition, get_el_sp, PeriodicSite, Structure, SpacegroupAnalyzer, formula_double_format, ) from emmet.core.featurization.robocrys.condense.fingerprint import ( get_structure_fingerprint, ) from emmet.core.featurization.robocrys.util import common_formulas if TYPE_CHECKING: Component = dict[str, Any] def get_structure_inequiv_components( components: list[Component], use_structure_graph: bool = False, fingerprint_tol: float = 0.01, ) -> list[Component]: """Gets and counts the structurally inequivalent components. Supports matching through StructureMatcher or by a combined structure graph/ site fingerprint approach. For the latter method, the component data has to have been generated with ``inc_graph=True``. Args: components: A list of structure components, generated using :obj:`pymatgen.analysis.dimensionality.get_structure_components`. If ``use_structure_graph=True``, the components should be generated with ``inc_graph=True``. use_structure_graph: Whether to use the bonding graph and site fingerprints to determine if components are structurally equivalent. If ``False``, :obj:`pymatgen.analysis.structure_matcher.StructureMatcher` will be used to compare the components. fingerprint_tol: The fingerprint tolerance to determine whether two components have a matching fingerprint. This option is ignored if ``use_structure_graph=False``. Returns: A list of the structurally inequivalent components. Any duplicate components will only be returned once. The component objects are in the same format is given by :obj:`pymatgen.analysis.dimensionality.get_structure_components` but have an additional field: - ``"count"`` (:obj:`int`): The number of times this component appears in the structure. """ components = deepcopy(components) for component in components: component["count"] = 1 if use_structure_graph: # check fingerprints match and components are isomorphic. fingerprints = [ get_structure_fingerprint(c["structure_graph"].structure) for c in components ] seen_components = [components[0]] seen_fingers = [fingerprints[0]] for component, fingerprint in zip(components[1:], fingerprints[1:]): graph_match = [ components_are_isomorphic(component, c) for c in seen_components ] finger_match = [ np.linalg.norm(fingerprint - c) < fingerprint_tol for c in seen_fingers ] structure_match = [i and f for i, f in zip(graph_match, finger_match)] if any(structure_match): # there should only ever be a single match so we take index of # the first match and increment the component count loc = np.where(structure_match)[0][0] seen_components[loc]["count"] += 1 else: seen_components.append(component) seen_fingers.append(fingerprint) else: sm = StructureMatcher() seen_components = [components[0]] for component in components[1:]: structure_match = [ sm.fit( component["structure_graph"].structure, c["structure_graph"].structure, ) for c in seen_components ] if any(structure_match): # there should only ever be a single match so we take index of # the first match and increment the component count loc = np.where(structure_match)[0][0] seen_components[loc]["count"] += 1 else: seen_components.append(component) return seen_components def components_are_isomorphic( component_a: Component, component_b: Component, use_weights: bool = False ): """Determines whether the graphs of two components are isomorphic. Only takes into account graph connectivity and not local geometry (e.g. bond angles and distances). Args: component_a: The first component. component_b: The second component. use_weights: Whether to use the graph edge weights in comparing graphs. Returns: Whether the components are isomorphic. """ def node_match(n1, n2): return n1["specie"] == n2["specie"] def edge_match(e1, e2): if use_weights: return e1["weight"] == e2["weight"] return True graph_a = component_a["structure_graph"].graph graph_b = component_b["structure_graph"].graph species_a = { n: {"specie": str(component_a["structure_graph"].structure[n].specie)} for n in graph_a } species_b = { n: {"specie": str(component_b["structure_graph"].structure[n].specie)} for n in graph_b } nx.set_node_attributes(graph_a, species_a) nx.set_node_attributes(graph_b, species_b) return nx.is_isomorphic( graph_a, graph_b, node_match=node_match, edge_match=edge_match ) def get_sym_inequiv_components( components: list[Component], spg_analyzer: SpacegroupAnalyzer ) -> list[Component]: """Gets and counts the symmetrically inequivalent components. Component data has to have been generated with ``inc_site_ids=True``. Args: components: A list of structure components, generated using :obj:`pymatgen.analysis.dimensionality.get_structure_components`, with ``inc_site_ids=True``. spg_analyzer: A `pymatgen.symmetry.analyzer.SpacegroupAnalyzer` analyzer object for the structure containing the components. Returns: A list of the symmetrically inequivalent components. Any duplicate components will only be returned once. The component objects are in the same format is given by :obj:`pymatgen.analysis.dimensionality.get_structure_components` but the additional property: - ``"count"`` (:obj:`int`): The number of times this component appears in the structure. """ components = deepcopy(components) sym_inequiv_components: dict[frozenset, Component] = {} equivalent_atoms = spg_analyzer.get_symmetry_dataset().equivalent_atoms for component in components: sym_indices = frozenset(equivalent_atoms[x] for x in component["site_ids"]) # if two components are composed of atoms that are symmetrically # equivalent they are the same. if sym_indices in sym_inequiv_components: sym_inequiv_components[sym_indices]["count"] += 1 continue component["count"] = 1 sym_inequiv_components[sym_indices] = component return list(sym_inequiv_components.values()) def get_formula_inequiv_components( components: list[Component], use_iupac_formula: bool = True, use_common_formulas: bool = True, ) -> list[Component]: """Gets and counts the inequivalent components based on their formuula. Note that the counting of compounds is different to in ``get_sym_inequiv_equivalent``. I.e. the count is not the number of components with the same formula. For example, the count of the formula "GaAs" in a system with two Ga2As2 components would be 4. Args: components: A list of structure components, generated using :obj:`pymatgen.analysis.dimensionality.get_structure_components`, with ``inc_site_ids=True``. use_iupac_formula (bool, optional): Whether to order formulas by the iupac "electronegativity" series, defined in Table VI of "Nomenclature of Inorganic Chemistry (IUPAC Recommendations 2005)". This ordering effectively follows the groups and rows of the periodic table, except the Lanthanides, Actanides and hydrogen. If set to ``False``, the elements will be ordered according to the electronegativity values. use_common_formulas: Whether to use the database of common formulas. The common formula will be used preferentially to the iupac or reduced formula. Returns: A list of the compositionally inequivalent components. Any duplicate components will only be returned once. The component objects are in the same format is given by :obj:`pymatgen.analysis.dimensionality.get_structure_components` but have two additional fields: - ``"count"`` (:obj:`int`): The number of formula units of this component. Note, this is not the number of components with the same formula. For example, the count of the formula "GaAs" in a system with two Ga2As2 components would be 4. - ``"formula"`` (``list[int]``): The reduced formula of the component. """ components = deepcopy(components) inequiv_components: dict[str, Component] = {} for component in components: formula, factor = get_component_formula_and_factor( component, use_iupac_formula=use_iupac_formula, use_common_formulas=use_common_formulas, ) # if two components have the same composition we treat them as the same if formula in inequiv_components: inequiv_components[formula]["count"] += factor continue component["count"] = factor component["formula"] = formula inequiv_components[formula] = component return list(inequiv_components.values()) def filter_molecular_components( components: list[Component], ) -> tuple[list[Component], list[Component]]: """Separate list of components into molecular and non-molecular components. Args: components: A list of structure components, generated using :obj:`pymatgen.analysis.dimensionality.get_structure_components`. Returns: The filtered components as a tuple of ``(molecular_components, other_components)``. """ molecular_components = [c for c in components if c["dimensionality"] == 0] other_components = [c for c in components if c["dimensionality"] != 0] return molecular_components, other_components def get_reconstructed_structure( components: list[Component], simplify_molecules: bool = True ) -> Structure: """Reconstructs a structure from a list of components. Has the option to simplify molecular components into a single site positioned at the centre of mass of the molecular. If using this option, the components must have been generated with ``inc_molecule_graph=True``. Args: components: A list of structure components, generated using :obj:`pymatgen.analysis.dimensionality.get_structure_components`, with ``inc_molecule_graph=True``. simplify_molecules: Whether to simplify the molecular components into a single site positioned at the centre of mass of the molecule. Returns: The reconstructed structure. """ mol_sites = [] other_sites = [] if simplify_molecules: mol_components, components = filter_molecular_components(components) if mol_components: lattice = mol_components[0]["structure_graph"].structure.lattice mol_sites = [ PeriodicSite( c["structure_graph"].structure[0].specie, c["molecule_graph"].molecule.center_of_mass, lattice, coords_are_cartesian=True, ) for c in mol_components ] if components: other_sites = [ site for c in components for site in c["structure_graph"].structure ] return Structure.from_sites(other_sites + mol_sites) def get_component_formula_and_factor( component: Component, use_iupac_formula: bool = True, use_common_formulas: bool = True, ) -> tuple[str, int]: """Gets the reduced formula and factor of a single component. Args: component: A structure component, generated using :obj:`pymatgen.analysis.dimensionality.get_structure_components`. use_iupac_formula (bool, optional): Whether to order formulas by the iupac "electronegativity" series, defined in Table VI of "Nomenclature of Inorganic Chemistry (IUPAC Recommendations 2005)". This ordering effectively follows the groups and rows of the periodic table, except the Lanthanides, Actanides and hydrogen. If set to ``False``, the elements will be ordered according to the electronegativity values. use_common_formulas: Whether to use the database of common formulas. The common formula will be used preferentially to the iupac or reduced formula. Returns: The formula and factor of the component. """ formula, factor = component[ "structure_graph" ].structure.composition.get_reduced_formula_and_factor( iupac_ordering=use_iupac_formula ) reduced_formula = component["structure_graph"].structure.composition.reduced_formula if use_common_formulas and reduced_formula in common_formulas: formula = common_formulas[reduced_formula] return formula, factor def get_component_formula( component: Component, use_iupac_formula: bool = True, use_common_formulas: bool = True, ) -> str: """Gets the reduced formula of a single component. Args: component: A structure component, generated using :obj:`pymatgen.analysis.dimensionality.get_structure_components`. use_iupac_formula (bool, optional): Whether to order formulas by the iupac "electronegativity" series, defined in Table VI of "Nomenclature of Inorganic Chemistry (IUPAC Recommendations 2005)". This ordering effectively follows the groups and rows of the periodic table, except the Lanthanides, Actanides and hydrogen. If set to ``False``, the elements will be ordered according to the electronegativity values. use_common_formulas: Whether to use the database of common formulas. The common formula will be used preferentially to the iupac or reduced formula. Returns: The formula and factor of the component. """ return get_component_formula_and_factor( component, use_iupac_formula=use_iupac_formula, use_common_formulas=use_common_formulas, )[0] def get_formula_from_components( components: list[Component], molecules_first: bool = False, use_iupac_formula: bool = True, use_common_formulas: bool = True, ) -> str: """Reconstructs a chemical formula from structure components. The chemical formulas for the individual components will be grouped together. If two components share the same composition, they will be treated as equivalent. Args: components: A list of structure components, generated using :obj:`pymatgen.analysis.dimensionality.get_structure_components`. molecules_first: Whether to put any molecules (zero-dimensional components) at the beginning of the formula. use_iupac_formula (bool, optional): Whether to order formulas by the iupac "electronegativity" series, defined in Table VI of "Nomenclature of Inorganic Chemistry (IUPAC Recommendations 2005)". This ordering effectively follows the groups and rows of the periodic table, except the Lanthanides, Actanides and hydrogen. If set to ``False``, the elements will be ordered according to the electronegativity values. use_common_formulas: Whether to use the database of common formulas. The common formula will be used preferentially to the iupac or reduced formula. Returns: The chemical formula. """ def order(comp_formula): composition = Composition(comp_formula) if use_iupac_formula: return sum( [get_el_sp(s).iupac_ordering for s in composition.elements] ) / len(composition.elements) return composition.average_electroneg components = get_formula_inequiv_components( components, use_iupac_formula=use_iupac_formula, use_common_formulas=use_common_formulas, ) if molecules_first: mol_comps, other_comps = filter_molecular_components(components) else: mol_comps = [] other_comps = components formulas = sorted([c["formula"] for c in mol_comps], key=order) + sorted( [c["formula"] for c in other_comps], key=order ) # if components include special formulas, then the count can be 0.5 # therefore if any non integer amounts we can just use a factor of 2 all_int = all(v["count"] % 1 == 0 for v in components) prefactor = 1 if all_int else 2 form_count_dict = {c["formula"]: int(c["count"] * prefactor) for c in components} # the following is based on ``pymatgen.core.composition.reduce_formula`` num_comps = len(formulas) factor = abs(gcd(*(form_count_dict.values()))) reduced_form = [] for i in range(0, num_comps): formula = formulas[i] norm_amt = form_count_dict[formula] * 1.0 / factor formatted_formula = formula if norm_amt == 1 else f"({formula})" reduced_form.append(formatted_formula) reduced_form.append(str(formula_double_format(norm_amt))) return "".join(reduced_form) def components_are_vdw_heterostructure(components: list[Component]) -> bool: """Whether a list of components form a van der Waals heterostructure. A heterostructure is defined here as a structure with more than one formula inequivalent 2D component. Args: components: A list of structure components, generated using :obj:`pymatgen.analysis.dimensionality.get_structure_components`. Returns: Whether the list of components from a heterostructure. """ components = get_formula_inequiv_components(components) return bool(len([c for c in components if c["dimensionality"] == 2])) def get_vdw_heterostructure_information( components: list[Component], use_iupac_formula: bool = True, use_common_formulas: bool = True, inc_ordered_components: bool = False, inc_intercalants: bool = False, ) -> dict[str, Any]: """Gets information about ordering of components in a vdw heterostructure. Args: components: A list of structure components, generated using :obj:`pymatgen.analysis.dimensionality.get_structure_components` with ``inc_orientation=True``. use_iupac_formula (bool, optional): Whether to order formulas by the iupac "electronegativity" series, defined in Table VI of "Nomenclature of Inorganic Chemistry (IUPAC Recommendations 2005)". This ordering effectively follows the groups and rows of the periodic table, except the Lanthanides, Actanides and hydrogen. If set to ``False``, the elements will be ordered according to the electronegativity values. use_common_formulas: Whether to use the database of common formulas. The common formula will be used preferentially to the iupac or reduced formula. inc_ordered_components: Whether to return a list of the ordered components. If False, just the component formulas will be returned. inc_intercalants: Whether to return a list of the intercalants. If False, just the intercalant formulas will be returned. Returns: Information on the heterostructure, as an :obj:`dict` with they keys: - ``"repeating_unit"`` (``list[str]``): A :obj:`List` of formulas of the smallest repeating series of components. For example. if the structure consists of A and B components ordered as "A B A B A B", the repeating unit is "A B". - ``"num_repetitions"`` (:obj:`int`): The number of repetitions of the repeating unit that forms the overall structure. For example. if the structure consists of A and B components ordered as "A B A B A B", the number of repetitions is 3. - ``"intercalant_formulas"`` (:obj:`list[str]`): The formulas of the intercalated compounds. - ``"ordered_components"`` (``list[component]``): If ``inc_ordered_components``, a :obj:`List` of components, ordered as they appear in the heteostructure stacking direction. - ``"intercalants"`` (``list[component]``: If ``inc_intercalants``, a :obj:`List` of intercalated components. """ if not components_are_vdw_heterostructure(components): raise ValueError("Components do not form a heterostructure.") try: millers = {c["orientation"] for c in components if c["dimensionality"] == 2} except KeyError as exc: if "orientation" in str(exc): raise KeyError( "Components not generated with inc_orientation=True" ) from exc raise exc if len(millers) != 1: raise ValueError("2D components don't all have the same orientation.") cart_miller = ( components[0]["structure_graph"] .structure.lattice.get_cartesian_coords(millers.pop()) .tolist() ) # plane is used to find the distances of all components along a certain axis # should use normal vector of plane to get exact distances but we just care # about relative distances. def distances_to_plane(points): return [np.dot(cart_miller, pp) for pp in points] min_distances = [ min(distances_to_plane(c["structure_graph"].structure.cart_coords)) for c in components ] # sort the components by distance to plane ordering = np.argsort(min_distances) ordered_components = [components[x] for x in ordering] # only consider the layered components formulae ordered_layers = [c for c in ordered_components if c["dimensionality"] == 2] ordered_layers_formula = [ get_component_formula( c, use_iupac_formula=use_iupac_formula, use_common_formulas=use_common_formulas, ) for c in ordered_layers ] num_layer_formulas = len(set(ordered_layers_formula)) repeating_formula = ordered_layers_formula num_repetitions = 1 # depending on the number of inequivalent formulae, there is a maximum # number of repetitions that can occur. To avoid unnecessary work we start # from this number of repetitions and move to 1 repetition (e.g. no # repetition) max_repetitions = int(np.floor(len(ordered_layers) / num_layer_formulas)) for n in range(max_repetitions, 0, -1): if ( all( len(set(ordered_layers_formula[i::num_layer_formulas])) == 1 for i in range(n) ) and len(ordered_layers) % n == 0 ): repeating_formula = ordered_layers_formula[: int(len(ordered_layers) / n)] num_repetitions = n break intercalants = [c for c in components if c["dimensionality"] < 2] intercalant_formulas = [get_component_formula(c) for c in intercalants] data = { "repeating_unit": repeating_formula, "num_repetitions": num_repetitions, "intercalant_formulas": intercalant_formulas, } if inc_intercalants: data["intercalants"] = intercalants if inc_ordered_components: data["ordered_components"] = ordered_components return data ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8171382 emmet_core-0.87.2/emmet/core/featurization/robocrys/condense/condenser.py0000644000175100017510000003232015240717720026317 0ustar00runnerrunner"""This module defines a class for condensing structures into dict representations.""" from __future__ import annotations from typing import TYPE_CHECKING, Any from emmet.core.io.pymatgen import ( get_structure_components, CrystalNN, NearNeighbors, Structure, SpacegroupAnalyzer, ) from emmet.core.featurization.robocrys.condense.component import ( components_are_vdw_heterostructure, get_component_formula, get_formula_from_components, get_reconstructed_structure, get_structure_inequiv_components, get_sym_inequiv_components, get_vdw_heterostructure_information, ) from emmet.core.featurization.robocrys.condense.mineral import MineralMatcher from emmet.core.featurization.robocrys.condense.molecule import MoleculeNamer from emmet.core.featurization.robocrys.condense.site import SiteAnalyzer from emmet.core.featurization.robocrys.util import common_formulas if TYPE_CHECKING: from emmet.core.featurization.robocrys.condense.component import Component class StructureCondenser: """Class to transform a structure into an intermediate dict representation. Args: use_conventional_cell: Whether to always use the convention cell representation of the structure. near_neighbors: A ``NearNeighbors`` instance used to calculate the bonding in the structure. For example, one of :class:`pymatgen.analysis.local_env.CrystalNN`, :class:`pymatgen.analysis.local_env.VoronoiNN`, etc. Defaults to ``None``, in which case :class:`pymatgen.analysis.local_env.CrystalNN` will be used. mineral_matcher: A ``MineralMatcher`` instance. Defaults to ``None`` in which case the default ``MineralMatcher`` settings will be used. If set to ``False``, no mineral matching will occur. use_symmetry_equivalent_sites: Whether to use symmetry to determine if sites are inequivalent. If ``False``, the site geometry and (next) nearest neighbor information will be used. symprec: The tolerance used when determining the symmetry of the structure. The symmetry can used both to determine if multiple sites are symmetrically equivalent (if use_symmetry_equivalent_sites is ``True``) and to obtain the symmetry labels for each site. use_iupac_formula (bool, optional): Whether to order formulas by the iupac "electronegativity" series, defined in Table VI of "Nomenclature of Inorganic Chemistry (IUPAC Recommendations 2005)". This ordering effectively follows the groups and rows of the periodic table, except the Lanthanides, Actanides and hydrogen. If set to ``False``, the elements will be ordered according to the electronegativity values. use_common_formulas: Whether to use the database of common formulas. The common formula will be used preferentially to the iupac or reduced formula. """ def __init__( self, use_conventional_cell: bool = True, near_neighbors: NearNeighbors | None = None, mineral_matcher: MineralMatcher | None = None, use_symmetry_equivalent_sites: bool = False, symprec: float = 0.01, simplify_molecules: bool = True, use_iupac_formula: bool = True, use_common_formulas: bool = True, ): if not near_neighbors: near_neighbors = CrystalNN() if mineral_matcher is None: mineral_matcher = MineralMatcher() self.use_conventional_cell = use_conventional_cell self.near_neighbors = near_neighbors self.mineral_matcher = mineral_matcher self.use_symmetry_equivalent_sites = use_symmetry_equivalent_sites self.symprec = symprec self.simplify_molecules = simplify_molecules self.use_common_formulas = use_common_formulas self.use_iupac_formula = use_iupac_formula def condense_structure(self, structure: Structure) -> dict[str, Any]: """Condenses the structure into an intermediate dict representation. Args: structure: A pymatgen structure object. Returns: The condensed structure information. The data is formatted as a :obj:`dict` with a fixed set of keys. An up-to-date example of the, the condensed representation of MoS2 given in the documentation. See: ``robocrystallographer/docs_rst/source/format.rst`` or https://hackingmaterials.lbl.gov/robocrystallographer/format.html """ # sort so we can give proper symmetry labels structure = structure.get_sorted_structure() # wrap all site coords into unit cell structure.translate_sites(range(structure.num_sites), [1, 1, 1]) sga = SpacegroupAnalyzer(structure, symprec=self.symprec) if self.use_conventional_cell: structure = sga.get_conventional_standard_structure() else: structure = sga.get_symmetrized_structure() bonded_structure = self.near_neighbors.get_bonded_structure(structure) components = get_structure_components( bonded_structure, inc_orientation=True, inc_site_ids=True, inc_molecule_graph=True, ) dimensionality = max(c["dimensionality"] for c in components) mineral = self._condense_mineral(structure, components) formula = self._condense_formula(structure, components) structure_data = { "formula": formula, "spg_symbol": sga.get_space_group_symbol(), "crystal_system": sga.get_crystal_system(), "mineral": mineral, "dimensionality": dimensionality, } site_analyzer = SiteAnalyzer( bonded_structure, # type: ignore[arg-type] symprec=self.symprec, use_symmetry_equivalent_sites=self.use_symmetry_equivalent_sites, ) structure_data["sites"] = site_analyzer.get_all_site_summaries() structure_data["distances"] = site_analyzer.get_all_bond_distance_summaries() structure_data["angles"] = site_analyzer.get_all_connectivity_angle_summaries() structure_data["nnn_distances"] = site_analyzer.get_all_nnn_distance_summaries() component_summary, component_makeup = self._condense_components( components, sga, site_analyzer ) structure_data["components"] = component_summary structure_data["component_makeup"] = component_makeup if components_are_vdw_heterostructure(components): hs_info = get_vdw_heterostructure_information( components, use_iupac_formula=self.use_iupac_formula, use_common_formulas=self.use_common_formulas, ) else: hs_info = None structure_data["vdw_heterostructure_info"] = hs_info return structure_data def _condense_mineral( self, structure: Structure, components: list[Component] ) -> dict[str, Any]: """Condenses the mineral data. Initially the original structure will be matched against a library of known minerals. If no match is found, the structure will be reconstructed, with all zero-dimensional components replaced by single atoms at their centre of mass, and the matching performed again. This allows for matching of systems with molecular substituents in known mineral prototypes. Args: structure: A pymatgen structure object. components: A list of structure components, generated using :obj:`pymatgen.analysis.dimensionality.get_structure_components` with ``inc_molecule_graph=True``. Returns: The mineral information as a :obj:`dict`, formatted as:: { 'type': mineral (str), 'distance': distance (float), 'n_species_type_match': match (bool), 'simplified': simplified (bool } The ``type``, ``distance``, and ``n_species_types_match`` keys correspond to the mineral name (e.g. Perovskite), the fingerprint distance between the prototype and known mineral, and whether the number of species types in the structure matches the number in the known prototype. If no mineral match is determined, the mineral type will be ``None``. If an exact mineral match is found the distance will be set to ``-1``. """ if not self.mineral_matcher: return { "type": None, "distance": -1, "n_species_type_match": True, "simplified": False, } mineral = self.mineral_matcher.get_best_mineral_name(structure) if not mineral["type"]: mineral_structure = get_reconstructed_structure( components, simplify_molecules=self.simplify_molecules ) mineral = self.mineral_matcher.get_best_mineral_name(mineral_structure) mineral["simplified"] = True else: mineral["simplified"] = False return mineral def _condense_formula( self, structure: Structure, components: list[Component] ) -> str: """Condenses the structure formula. If :attr:`StructureCondenser.use_common_formulas` is ``True`` and the formula is found in a database of 100,000 known formulas we preferentially use the known formula, otherwise we reconstruct the formula from the structure components. Args: structure: A pymatgen structure object. components: A list of structure components, generated using :obj:`pymatgen.analysis.dimensionality.get_structure_components` Returns: The chemical formula. """ reduced_formula = structure.composition.reduced_formula if self.use_common_formulas and reduced_formula in common_formulas: formula = common_formulas[reduced_formula] else: formula = get_formula_from_components( components, use_common_formulas=self.use_common_formulas ) return formula def _condense_components( self, components: list[Component], spacegroup_analyzer: SpacegroupAnalyzer, site_analyzer: SiteAnalyzer, ) -> tuple[dict[int, dict[str, Any]], list[int]]: """Condenses the component data. Args: components: A list of structure components, generated using :obj:`pymatgen.analysis.dimensionality.get_structure_components` site_analyzer: A site analyzer object for the structure containing the components. spacegroup_analyzer: A space group analyzer object for the structure containing the components. Returns: The condensed component data and the component makeup of the structure. The condensed components have the form:: { 0: { 'formula': 'MoS2', 'sites': [0, 2] 'dimensionality': 2, 'molecule_name': None, 'orientation': (0, 0, 1) } } Where the ``0`` key is the component identifier. The ``sites`` key gives a :obj:`list` of the indexes of the inequivalent sites in the structure. If the component is zero-dimensional and is a known molecule, the ``molecule_name`` key will be a :obj:`str` with the molecule name. The ``orientation`` key is the miller index (for two-dimensional components) or direction of propagation (for one-dimensional components). """ if self.use_symmetry_equivalent_sites: inequiv_components = get_sym_inequiv_components( components, spacegroup_analyzer ) else: inequiv_components = get_structure_inequiv_components(components) molecule_namer = MoleculeNamer() new_components: dict[int, dict[str, Any]] = {} component_makeup = [] for i, component in enumerate(inequiv_components): formula = get_component_formula( component, use_iupac_formula=self.use_iupac_formula, use_common_formulas=self.use_common_formulas, ) sites = site_analyzer.get_inequivalent_site_indices(component["site_ids"]) if component["dimensionality"] == 0: molecule_name = molecule_namer.get_name_from_molecule_graph( component["molecule_graph"] ) else: molecule_name = None new_components[i] = { "formula": formula, "dimensionality": component["dimensionality"], "orientation": component["orientation"], "molecule_name": molecule_name, "sites": sites, } component_makeup.extend([i] * component["count"]) return new_components, component_makeup ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8171382 emmet_core-0.87.2/emmet/core/featurization/robocrys/condense/fingerprint.py0000644000175100017510000001102715240717720026667 0ustar00runnerrunnerfrom __future__ import annotations from collections.abc import Iterable import numpy as np from emmet.core.io.pymatgen import IStructure from emmet.core.featurization.featurizers import ( CrystalNNFingerprint, SiteStatsFingerprint, ) def get_site_fingerprints( structure: IStructure, as_dict: bool = True, preset: str = "CrystalNNFingerprint_ops", ) -> list[dict[str, int]] | np.ndarray: """Gets the fingerprint for all sites in a structure. Args: structure: A structure. as_dict: Whether to return the fingerprints as a dictionary of ``{'op': val}``. Defaults to ``True``. preset: The preset to use when calculating the fingerprint. See :class:`emmet.core.featurization.featurizers.SiteStatsFingerprint`` for more details. Returns: The fingerprint for all sites in the structure. If ``as_dict == True``, the data will be returned as a :obj:`list` of :obj:`dict` containing the order parameters as:: [{'op': val}] for each site. If ``as_dict == False``, the data will be returned as a :class:`numoy.ndarray` containing the fingerprint for each site as:: [site_index][op_index] """ ssf = SiteStatsFingerprint( site_featurizer=CrystalNNFingerprint.from_preset( "ops", cation_anion=False, ), stats=None, ) # transpose fingerprint from [op_type][site] to [site][op_type] site_fingerprints = np.array(ssf.featurize(structure)).T if as_dict: return [dict(zip(ssf.feature_labels, x)) for x in site_fingerprints] return site_fingerprints def get_structure_fingerprint( structure: IStructure, preset: str = "CrystalNNFingerprint_ops", stats: tuple[str, ...] | None = ("mean", "std_dev"), prototype_match: bool = False, ) -> np.ndarray: """Gets the fingerprint for a structure. Args: structure: A structure. preset: The preset to use when calculating the fingerprint. See :class:`matminer.featurizers.structure.SiteStatsFingerprint`` for more details. stats: The stats to include in fingerprint. See :class:`matminer.featurizers.structure.SiteStatsFingerprint`` for more details. prototype_match: Whether to use distance cutoffs and electron negativity differences when calculating the structure fingerprint. Returns: The structure fingerprint as a :class:`numpy.ndarray`. """ # TODO: Add distance_cutoff option to matminer so we can user preset arg # currently don't use SiteStatsFingerprint.from_preset as we need to pass in # distance_cutoffs param if prototype_match: ssf = SiteStatsFingerprint( CrystalNNFingerprint.from_preset( "ops", cation_anion=False, distance_cutoffs=None, x_diff_weight=None ), stats=stats, ) else: ssf = SiteStatsFingerprint( CrystalNNFingerprint.from_preset("ops", cation_anion=False), stats=stats ) return np.array(ssf.featurize(structure)) def get_fingerprint_distance( structure_a: IStructure | Iterable, structure_b: IStructure | Iterable ) -> float: """Gets the euclidean distance between the fingerprints of two structures. Args: structure_a: The first structure or fingerprint. Can be provided as a structure or a fingerprint. If provided as a structure, the fingerprint will be calculated first, so generally it is quicker to pre-calculate the fingerprint if comparing against multiple structures in turn. structure_b: The second structure or fingerprint. Can be provided as a structure or a fingerprint. If provided as a structure, the fingerprint will be calculated first, so generally it is quicker to pre-calculate the fingerprint if comparing against multiple structures in turn. Returns: The euclidean distance between fingerprints as a :class:`numpy.ndarray`. """ if issubclass(type(structure_a), IStructure): fingerprint_a = get_structure_fingerprint(structure_a) # type: ignore[arg-type] else: fingerprint_a = np.array(structure_a) if issubclass(type(structure_b), IStructure): fingerprint_b = get_structure_fingerprint(structure_b) # type: ignore[arg-type] else: fingerprint_b = np.array(structure_b) return np.linalg.norm(fingerprint_a - fingerprint_b) # type: ignore[return-value] ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8172715 emmet_core-0.87.2/emmet/core/featurization/robocrys/condense/formula_aliases.json.gz0000644000175100017510000144541315240717720030461 0ustar00runnerrunnerpilrL&|+oĿ Rz@$WxOCk mA]W18'T!(P0ҽ? cjE Tы $ j-a6Z !nPO])@Pшu9أIԲl.1 Oʧ3(O=Slm{佗R⽇SR@CNUR>{U/Ql'ѵK Εyw?ϻVX_5&uW v3`.al1u0 po!A9*/RwYVc|8!JH::z97! 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Args: min_db_path : str or Path Path to the Mineral DB """ mineral_db = pd.read_json(min_db_path, compression="gzip", orient="split") mineral_db.structure = mineral_db.structure.apply(Structure.from_dict) return mineral_db class MineralMatcher: """Class to match a structure to a mineral name. Uses a precomputed database of minerals and their fingerprints, extracted from the AFLOW prototype database. For more information on this database see reference [aflow]_: .. [aflow] Mehl, M. J., Hicks, D., Toher, C., Levy, O., Hanson, R. M., Hart, G., & Curtarolo, S. (2017), The AFLOW library of crystallographic prototypes: part 1. Computational Materials Science, 136, S1-S828. doi: 10.1016/j.commatsci.2017.01.017 Args: initial_ltol: The fractional length tolerance used in the AFLOW structure matching. initial_stol : The site coordinate tolerance used in the AFLOW structure matching. initial_angle_tol: The angle tolerance used in the AFLOW structure matching. use_fingerprint_matching: Whether to use the fingerprint distance to match minerals. fingerprint_distance_cutoff: Cutoff to determine how similar a match must be to be returned. The distance is measured between the structural fingerprints in euclidean space. mineral_db : Optional path or pandas .DataFrame object containing the mineral fingerprint database. """ def __init__( self, initial_ltol: float = 0.2, initial_stol: float = 0.3, initial_angle_tol: float = 5.0, use_fingerprint_matching: bool = True, fingerprint_distance_cutoff: float = 0.4, mineral_db: str | Path | pd.DataFrame | None = None, ): mineral_db = mineral_db or _mineral_db_file if isinstance(mineral_db, (str, Path)): self.mineral_db = load_mineral_db_file(mineral_db) else: self.mineral_db = mineral_db self.initial_ltol = initial_ltol self.initial_stol = initial_stol self.initial_angle_tol = initial_angle_tol self.fingerprint_distance_cutoff = fingerprint_distance_cutoff self.use_fingerprint_matching = use_fingerprint_matching self._structure: Structure | None = None self._mineral_db: pd.DataFrame | None = None def get_best_mineral_name(self, structure: Structure) -> dict[str, Any]: """Gets the "best" mineral name for a structure. Uses a combination of AFLOW prototype matching and fingerprinting to get the best mineral name. The AFLOW structure prototypes are detailed in reference [aflow]_. The algorithm works as follows: 1. Check for AFLOW match. If single match return mineral name. 2. If multiple matches, return the one with the smallest fingerprint distance. 3. If no AFLOW match, get fingerprints within tolerance. If there are any matches, take the one with the smallest distance. 4. If no fingerprints within tolerance, check get fingerprints without constraining the number of species types. If any matches, take the best one. Args: structure (Structure): A pymatgen ``Structure`` object to match. Return: (dict): The mineral name information. Stored as a dict with the keys "type", "distance", "n_species_types_match", corresponding to the mineral name, the fingerprint distance between the prototype and known mineral, and whether the number of species types in the structure matches the number in the known prototype, respectively. If no mineral match is determined, the mineral type will be ``None``. If an AFLOW match is found, the distance will be set to -1. """ self._set_distance_matrix(structure) # pre-calculate distance matrix aflow_matches = self.get_aflow_matches(structure) fingerprint_matches = self.get_fingerprint_matches(structure) fingerprint_derived = self.get_fingerprint_matches(structure, match_n_sp=False) distance = -1 n_species_types_match = True if aflow_matches: # mineral db sorted by fingerprint distance so first result always # has a smaller distance mineral = aflow_matches[0]["type"] elif fingerprint_matches and self.use_fingerprint_matching: mineral = fingerprint_matches[0]["type"] distance = fingerprint_matches[0]["distance"] elif fingerprint_derived and self.use_fingerprint_matching: mineral = fingerprint_derived[0]["type"] distance = fingerprint_derived[0]["distance"] n_species_types_match = False else: mineral = None return { "type": mineral, "distance": distance, "n_species_type_match": n_species_types_match, } def get_aflow_matches( self, structure: Structure, ) -> list[dict[str, Any]] | None: """Gets minerals for a structure by matching to AFLOW prototypes. Overrides :class:`pymatgen.analysis.prototypes.AflowPrototypeMatcher` to only return matches to prototypes with known mineral names. The AFLOW tolerance parameters (defined in the init method) are passed to a :class:`pymatgen.analysis.structure_matcher.StructureMatcher` object. The tolerances are gradually decreased until only a single match is found (if possible). The AFLOW structure prototypes are detailed in reference [aflow]_. Args: structure: A pymatgen structure to match. Returns: A :obj:`list` of :obj:`dict`, sorted by how close the match is, with the keys 'type', 'distance', 'structure'. Distance is the euclidean distance between the structure and prototype fingerprints. If no match was found within the tolerances, ``None`` will be returned. """ self._set_distance_matrix(structure) matcher = ReducedAflowPrototypeMatcher( initial_ltol=self.initial_ltol, initial_stol=self.initial_stol, initial_angle_tol=self.initial_angle_tol, mineral_db=self._mineral_db, ) return matcher.get_prototypes(structure) def get_fingerprint_matches( self, structure: Structure, max_n_matches: int | None = None, match_n_sp: bool = True, mineral_name_constraint: str | None = None, ) -> list[dict[str, Any]] | None: """Gets minerals for a structure by matching to AFLOW fingerprints. Only AFLOW prototypes with mineral names are considered. The AFLOW structure prototypes are detailed in reference [aflow]_. Args: structure: A structure to match. max_n_matches: Maximum number of matches to return. Set to ``None`` to return all matches within the cutoff. match_n_sp: Whether the structure and mineral must have the same number of species. Defaults to True. mineral_name_constraint: Whether to limit the matching to a specific mineral name. Returns: A :obj:`list` of :obj:`dict`, sorted by how close the match is, with the keys 'type', 'distance', 'structure'. Distance is the euclidean distance between the structure and prototype fingerprints. If no match was found within the tolerances, ``None`` will be returned. """ self._set_distance_matrix(structure) mineral_db: pd.DataFrame = self._mineral_db # type: ignore[assignment] if mineral_name_constraint: mineral_db = mineral_db[ mineral_db["mineral"].str.lower() == mineral_name_constraint ] if match_n_sp: mineral_db = mineral_db[mineral_db["n_elems"] == structure.n_elems] num_rows = mineral_db.shape[0] max_n_matches = max_n_matches or num_rows max_n_matches = min(max_n_matches, num_rows) minerals = [ _get_row_data(row) for i, row in islice(mineral_db.iterrows(), max_n_matches) if row["distance"] < self.fingerprint_distance_cutoff ] return minerals if minerals else None def _set_distance_matrix(self, structure: Structure) -> None: """Utility func to calculate distance between structure and minerals. First checks to see if the distances have already been calculated for the structure. If not, the distances are stored in a class variable for use by other class methods. Args: structure: A structure. """ if self._structure == structure and self._mineral_db is not None: return data = self.mineral_db.copy() fingerprint = get_structure_fingerprint(structure) if np.linalg.norm(fingerprint) < 0.4: # fingerprint is too small for a reasonable match, indicates very # little bonding or small order parameter matches fingerprint = get_structure_fingerprint(structure, prototype_match=False) data["distance"] = data["fingerprint"].apply( lambda x: get_fingerprint_distance(x, fingerprint) ) self._mineral_db = data.sort_values(by="distance") self._structure = structure def _get_row_data(row: pd.Series) -> dict[str, Any]: """Utility function to extract mineral data from pandas `DataFrame` row.""" return { "type": row["mineral"], "distance": row["distance"], "structure": row["structure"], } class ReducedAflowPrototypeMatcher(AflowPrototypeMatcher): """Match structures to predefined library of reduced, named AFLOW prototypes. Based on `pymatgen.analysis.prototypes.AflowPrototypeMatcher`, which uses the methods of: Mehl, M. J., Hicks, D., Toher, C., Levy, O., Hanson, R. M., Hart, G., & Curtarolo, S. (2017). The AFLOW library of crystallographic prototypes: part 1. Computational Materials Science, 136, S1-S828. https://doi.org/10.1016/j.commatsci.2017.01.017 """ def __init__( self, initial_ltol: float = 0.2, initial_stol: float = 0.3, initial_angle_tol: float = 5, mineral_db: pd.DataFrame | None = None, ) -> None: """ Tolerances as defined in StructureMatcher. Tolerances will be gradually decreased until only a single match is found (if possible). Args: initial_ltol (float): fractional length tolerance. initial_stol (float): site tolerance. initial_angle_tol (float): angle tolerance. mineral_db (pd.DataFrame or None) : precomputed database of prototypes and their fingerprints. 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Some functionality relies on having a working internet connection. """ from __future__ import annotations from pathlib import Path import warnings from monty.serialization import loadfn from pubchempy import BadRequestError, get_compounds # type:ignore[import-untyped] from emmet.core.io.pymatgen import MoleculeGraph, BabelMolAdaptor class MoleculeNamer: name_sources = ("traditional", "iupac") def __init__( self, use_online_pubchem: bool = True, name_preference: tuple[str, ...] = name_sources, ): """Class to match molecule graphs to known molecule names. Args: use_online_pubchem: Whether to try using the Pubchem website for matching molecules if a match is not found in the offline database. Defaults to ``True``. Requires a working internet connection and for the ``pubchempy`` package to be installed. name_preference: The order of preference for determining compound names. Options are "traditional", and "iupac". If the first option is not available, the subsequent options will be used. Should be provided as a tuple of options, from 1st choice to last. """ db_file = (Path(__file__).parent / "molecule_db.json.gz").resolve() self.molecule_db: dict[str, dict[str, str]] = loadfn(str(db_file)) self.matched_molecules: dict[str, str] = {} self.use_online_pubchem = use_online_pubchem # append the sources list to the end in case the user only supplies # a single preference self.name_preference = tuple(list(name_preference) + list(self.name_sources)) def get_name_from_molecule_graph( self, molecule_graph: MoleculeGraph, ) -> str | None: """Gets the name of a molecule from a molecule graph object. Args: molecule_graph: A molecule graph. Returns: The molecule name if a match is found else ``None``. """ smiles = self.molecule_graph_to_smiles(molecule_graph) if not smiles: return None match = None if smiles in self.matched_molecules: match = self.matched_molecules[smiles] elif smiles in self.molecule_db: # we should use the first preference for which there is a match for source in self.name_preference: if ( source in self.molecule_db[smiles] and self.molecule_db[source][smiles] ): match = self.molecule_db[smiles][source] break if not match and self.use_online_pubchem: match = self.get_name_from_pubchem(smiles) return self._process_match(smiles, match) def get_name_from_pubchem(self, smiles: str) -> str | None: """Tries to get the name of a molecule from the Pubchem website. Args: smiles: A SMILES string. Returns: The molecule name if a match is found else ``None``. """ try: comp = get_compounds(smiles, namespace="smiles")[0] except (BadRequestError, IndexError): return None traditional = comp.synonyms[0] if comp.synonyms else None names = {"traditional": traditional, "iupac": comp.iupac_name} match = None for source in self.name_preference: if source in names and names[source]: match = names[source] break if isinstance(match, str): match = match.lower() return self._process_match(smiles, match) @staticmethod def molecule_graph_to_smiles(molecule_graph: MoleculeGraph) -> str | None: """Converts a molecule graph to SMILES string. Args: molecule_graph: A molecule graph. Returns: The SMILES representation of the molecule. """ try: bma = BabelMolAdaptor.from_molecule_graph(molecule_graph) pbmol = bma.pybel_mol return pbmol.write("smi").split()[0] # type: ignore[attr-defined] except RuntimeError: warnings.warn( "Molecule naming requires openbabel to be installed " "with Python bindings. Please get it at http://openbabel.org.", RuntimeWarning, ) return None def _process_match(self, smiles: str, match: str | None) -> str | None: """Utility function to store and process match.""" if isinstance(match, str): match = match.lower() self.matched_molecules[smiles] = match return self.matched_molecules[smiles] return match ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8202715 emmet_core-0.87.2/emmet/core/featurization/robocrys/condense/molecule_db.json.gz0000644000175100017510001346406315240717720027572 0ustar00runnerrunner\smiles_to_names.json{o#I'~aC"3!)^Att=5="x/)N1L1WAew?n/D;U{Ϲ]]G{D?ߖzowi7_vxoΨy|;8o>vViq?wHv0Fx~5~t={7|<~``a,u)<(C}{<~ /îuO/8>|?}p|t"CϮ~N/;:v |[{wHGCIJ,rv뗷=?vz>UBCߏCxXW_N fme0"=?ak{8?}ߛK[ U[Y.hiM7]Xw_tӻϻI jbM&0l900} >װECB$qaV>vJ7t־YNAosXIR gS9aM2a@.-n>Oct?tZo+{coox;:IRi?_gLp}{Wg\M~,,uVL̂; e.VA ;+~9:~_Ό{(p9b@$x˄i,7p_{h&8b!/kADB2v8`n3V8L߹`;!۬~՚=cUp e &+ÎHhEQGo s(?[~@#JVNz[ x)SSħ7De-7Op}l4l_:ր TYa%7\sOۯuХpZ?hOxjGmH<J>H30qިf+*.)O3XaѨ/7 ^lfdkU6Ԏq%&d]PTtmu5s5.Gg'9p{d_:`6d˹n)Q(頀6}Tㆴv`^> (E|+d~Oڻ'z&eX-og&-x|dD&׻dey#=2DJi)*adw) ۏp/>XUnV݇kw2h3i=.ϻˆHG/Qv7{XY]L|ToA"uw2qwE"P'uBG٣j[rK-n8Jv2&: _rdw0E~/ /뱵]qQHa!q_Hu`Npy7R?])ZF0+g3kAgjag2*3\եXC|:5so/9l~ek!N4Ұ.p_mn\70s*:{`dn#"W›pGIla[^^yGn;NL0>}Cyj,Jày3lHNЙ\Nm}AY0Bu8ώ0n~OoJbYWr$. :Bנ-4'rm>(A& n]YoW'xB5/yEЭ {9! @baQ*7:eRi|5>NY* :4A ~c8J+%#ddE#ZzPkx/5[h![pJ,='1 ۊﵽ/xϒ~wl['U0-NjgȅDe7l7  /wm|4 ݃"fs8f[Kh7|sO_5cj8%W&-3#ї]&&y]Axհ:{ؼKA4<%7Ρ< zπ&EQC5~>K(\X;iS UmC]1n(`h/}.`m5 Ao8kn]05|qgH)t?^E++RiI\yLӴ^%WXNX \2/o釪};>=q|A6VBtîp}UVS/i^Bq/wy0qF/_dI?}Q_ǫL@yj2 Eiti o]aԍ;#k  ^Z](aVt,\.x!6qALM9 \'SWyH쵍Y؏(njAF'/+ =$Jo!;ɒ,N69S}@hXMׄWZ$h:nN#(]3#F.2ָS.gS]2ָxn ~sdZCxE]J ʋC|[{"1zxʰ l3o/cտthlP7/{8I?^Gtd͇ !>K TJE Ya=ĂeT A9C|mF|;b.]E_30qZ"c|C>ք3N,FVX{̆8p$G8+<0dFGy@|g.e;{>N~4G]Y}p֡S:%{BKE` ge$Cu3<2#Fsqz8nAu|v5,DnLkp* zͱurEc ~[,$C9!aMDס&NyD0C/jxíB0Engyran8Wt߁⨭x2KHXs̕S`^,ԔNXXSZ ^sP7HIHchќa,vɭ~8^eWXs~ cA}vWݰoQ!K(+YA_oiH*쪙IE*]p]N, y mSb<7nxUl~܊Go>7",mx|<}x_E>O-L`Tu|Яq_gI?fE`7ymZF\:կ)=m~–REݤ%Dc(Q/#[3#֜Lh ~.%%oyαhuЙ=ϮxD'iXrvC!4jDOU/hY?`jҩԩ h(_[?{Dg$"bzuMȐ@94GjYLnkM(]7gb^j3׵:uɞ:mrIc78n~pTbW$ondF1C%u[7FՉߠ uĄA8^ʚEsWepW>;´-^p17Y E"*.  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Todo: * distortion of geometry e.g. elongated along an axis """ from __future__ import annotations from collections import defaultdict from typing import TYPE_CHECKING import numpy as np from emmet.core.io.pymatgen import ( StructureGraph, Composition, get_el_sp, SpacegroupAnalyzer, formula_double_format, get_angle, ) from emmet.core.featurization.robocrys.condense.fingerprint import get_site_fingerprints from emmet.core.featurization.robocrys.util import ( connected_geometries, defaultdict_to_dict, get_el, ) if TYPE_CHECKING: from collections.abc import Sequence from typing import Any class SiteAnalyzer: """Class to extract information on site geometry and bonding. Attributes: symmetry_labels: A :obj:`dict` mapping the site indices to the symmetry label for that site. If two sites are symmetrically equivalent they share the same symmetry label. The numbering begins at 1 for each element in the structure. equivalent_sites: A :obj:`list` of indices mapping each site in the structure to a symmetrically or structurally equivalent site, depending on the value of ``use_symmetry_equivalent_sites``. Args: bonded_structure: A bonded structure with nearest neighbor data included. For example generated using :class:`pymatgen.analysis.local_env.CrystalNN` or :class:`pymatgen.analysis.local_env.VoronoiNN`. use_symmetry_equivalent_sites: Whether to use symmetry to determine if sites are inequivalent. If ``False``, the site geometry and (next) nearest neighbor information will be used. symprec: The tolerance used when determining the symmetry of the structure. The symmetry can used both to determine if multiple sites are symmetrically equivalent and to obtain the symmetry labels for each site. minimum_geometry_op: The minimum geometrical order parameter for a geometry match to be returned. use_iupac_formula (bool, optional): Whether to order formulas by the iupac "electronegativity" series, defined in Table VI of "Nomenclature of Inorganic Chemistry (IUPAC Recommendations 2005)". This ordering effectively follows the groups and rows of the periodic table, except the Lanthanides, Actanides and hydrogen. If set to ``False``, the elements will be ordered according to the electronegativity values. """ def __init__( self, bonded_structure: StructureGraph, use_symmetry_equivalent_sites: bool = False, symprec: float = 0.01, minimum_geometry_op: float = 0.4, use_iupac_formula: bool = True, ): self.bonded_structure = bonded_structure self.use_iupac_formula = use_iupac_formula self.minimum_geometry_op = minimum_geometry_op self.site_fingerprints = get_site_fingerprints(bonded_structure.structure) sga = SpacegroupAnalyzer(bonded_structure.structure, symprec=symprec) equivalent_sites = sga.get_symmetry_dataset().equivalent_atoms if use_symmetry_equivalent_sites: self.equivalent_sites = list(equivalent_sites) else: self.equivalent_sites = self._calculate_equivalent_sites() self.symmetry_labels = self._calculate_symmetry_labels( equivalent_sites.tolist() ) def get_site_geometry(self, site_index: int) -> dict[str, str | float]: """Gets the bonding geometry of a site. For example, "octahedral" or "square-planar". Args: site_index: The site index (zero based). Returns: The site geometry information formatted at as:: {'type': geometry_type, 'likeness': order_parameter} Where ``geometry_type`` is a :obj:`str` corresponding to the geometry type (e.g. octahedral) and ``order_parameter`` is a :obj:`float` indicating whether how close the geometry is to the perfect geometry. If the largest geometrical order parameter falls beneath :attr:`robocrys.site.SiteAnalyzer.minimum_geometry_op`, the geometry type will be returned as "X-coordinate", where X is the coordination number. """ # get fingerprint as a list of tuples, e.g. [("op name", val), ...] site_fingerprint: list[tuple[str, int]] = list( self.site_fingerprints[site_index].items() ) # get coordination number with largest weight, ignore op names with # just the coordination number weight (e.g. containing "wt") parameter = max(site_fingerprint, key=lambda x: x[1] if "wt" not in x[0] else 0) if parameter[1] < self.minimum_geometry_op: # the largest filtered weight is less than the tolerance for determining the # coordination geometry (i.e., we can no longer say the site is octahedral # or tetrahedral etc). Now we don't care about the actual geometry, we just # want the most likely coordination number; take this from the largest # of all weights parameter = max(site_fingerprint, key=lambda x: x[1]) cn = parameter[0].split()[-1].split("_")[-1] geometry = f"{cn}-coordinate" likeness = 1.0 else: # return the geometry type without the CN at the end, e.g. # "square co-planar CN_4" -> "square co-planar" geometry = " ".join(parameter[0].split()[:-1]) geometry = "single-bond" if geometry == "sgl_bd" else geometry likeness = parameter[1] return {"type": geometry, "likeness": likeness} def get_nearest_neighbors( self, site_index: int, inc_inequivalent_site_index: bool = True ) -> list[dict[str, Any]]: """Gets information about the bonded nearest neighbors. Args: site_index: The site index (zero based). inc_inequivalent_site_index: Whether to include the inequivalent site indices in the nearest neighbor information. Returns: For each site bonded to ``site_index``, returns a :obj:`dict` with the format:: {'element': el, 'dist': distance} If ``inc_inequivalent_site_index=True``, the data will have an additional key ``'inequiv_index'`` corresponding to the inequivalent site index. E.g. if two sites are structurally/symmetrically equivalent (depending on the value of ``self.use_symmetry_equivalent_sites`` then they will have the same ``inequiv_index``. """ nn_sites = self.bonded_structure.get_connected_sites(site_index) if inc_inequivalent_site_index: return [ { "element": str(site.site.specie), "inequiv_index": self.equivalent_sites[site.index], "dist": site.dist, } for site in nn_sites ] return [ {"element": str(site.site.specie), "dist": site.dist} for site in nn_sites ] def get_next_nearest_neighbors( self, site_index: int, inc_inequivalent_site_index: bool = True ) -> list[dict[str, Any]]: """Gets information about the bonded next nearest neighbors. Args: site_index: The site index (zero based). inc_inequivalent_site_index: Whether to include the inequivalent site indices. Returns: A list of the next nearest neighbor information. For each next nearest neighbor site, returns a :obj:`dict` with the format:: {'element': el, 'connectivity': con, 'geometry': geom, 'angles': angles, 'distance': distance} The ``connectivity`` property is the connectivity type to the next nearest neighbor, e.g. "face", "corner", or "edge". The ``geometry`` property gives the geometry of the next nearest neighbor site. See the ``get_site_geometry`` method for the format of this data. The ``angles`` property gives the bond angles between the site and the next nearest neighbour. Returned as a :obj:`list` of :obj:`int`. Multiple bond angles are given when the two sites share more than nearest neighbor (e.g. if they are face-sharing or edge-sharing). The ``distance`` property gives the distance between the site and the next nearest neighbor. If ``inc_inequivalent_site_index=True``, the data will have an additional key ``'inequiv_index'`` corresponding to the inequivalent site index. E.g. if two sites are structurally/symmetrically equivalent (depending on the value of ``self.use_symmetry_equivalent_sites`` then they will have the same ``inequiv_index``. """ def get_coords(a_site_index, a_site_image): return np.asarray( self.bonded_structure.structure.lattice.get_cartesian_coords( self.bonded_structure.structure.frac_coords[a_site_index] + a_site_image ) ) nn_sites = self.bonded_structure.get_connected_sites(site_index) next_nn_sites = [ site for nn_site in nn_sites for site in self.bonded_structure.get_connected_sites( nn_site.index, jimage=nn_site.jimage ) ] nn_sites_set = {(site.index, site.jimage) for site in nn_sites} seen_nnn_sites = set() next_nn_summary = [] for nnn_site in next_nn_sites: if ( nnn_site.index == site_index and nnn_site.jimage == (0, 0, 0) or (nnn_site.index, nnn_site.jimage) in seen_nnn_sites ): # skip the nnn site if it is the original atom of interest continue seen_nnn_sites.add((nnn_site.index, nnn_site.jimage)) sites = { (site.index, site.jimage) for site in self.bonded_structure.get_connected_sites( nnn_site.index, jimage=nnn_site.jimage ) } shared_sites = nn_sites_set.intersection(sites) n_shared_atoms = len(shared_sites) if n_shared_atoms == 1: connectivity = "corner" elif n_shared_atoms == 2: connectivity = "edge" else: connectivity = "face" site_coords = get_coords(site_index, (0, 0, 0)) nnn_site_coords = get_coords(nnn_site.index, nnn_site.jimage) nn_site_coords = [ get_coords(nn_site_index, nn_site_image) for nn_site_index, nn_site_image in shared_sites ] # can't just use Structure.get_angles to calculate angles as it # doesn't take into account the site image angles = [ get_angle(site_coords - x, nnn_site_coords - x) for x in nn_site_coords ] distance = np.linalg.norm(site_coords - nnn_site_coords) geometry = self.get_site_geometry(nnn_site.index) summary = { "element": str(nnn_site.site.specie), "connectivity": connectivity, "geometry": geometry, "angles": angles, "distance": distance, } if inc_inequivalent_site_index: summary["inequiv_index"] = self.equivalent_sites[nnn_site.index] next_nn_summary.append(summary) return next_nn_summary def get_site_summary(self, site_index: int) -> dict[str, Any]: """Gets a summary of the site information. Args: site_index: The site index (zero based). Returns: A summary of the site information, formatted as:: { 'element': 'Mo4+', 'geometry': { 'likesness': 0.5544, 'type': 'pentagonal pyramidal' }, 'nn': [2, 2, 2, 2, 2, 2], 'nnn': {'edge': [0, 0, 0, 0, 0, 0]}, 'poly_formula': 'S6', 'sym_labels': (1,) } Where ``element`` is the species string (if the species has oxidation states, these will be included in the string). The ``geometry`` key is the geometry information as produced by :meth:`SiteAnalyzer.get_site_geometry`. The `nn` key lists the site indices of the nearest neighbor bonding sites. Note the inequivalent site index is given for each site. The `nnn` key gives the next nearest neighbor information, broken up by the connectivity to that neighbor. The ``poly_formula`` key gives the formula of the bonded nearest neighbors. ``poly_formula`` will be ``None`` if the site geometry is not in :data:`robocrys.util.connected_geometries`. The ``sym_labels`` key gives the symmetry labels of the site. If two sites are symmetrically equivalent they share the same symmetry label. The numbering begins at 1 for each element in the structure. If :attr:`SiteAnalyzer.use_symmetry_inequivalnt_sites` is ``False``, each site may have more than one symmetry label, as structural features have instead been used to determine the site equivalences, i.e. two sites are symmetrically distinct but share the same geometry, nearest neighbor and next nearest neighbor properties. """ element = str(self.bonded_structure.structure[site_index].specie) geometry = self.get_site_geometry(site_index) nn_sites = self.get_nearest_neighbors( site_index, inc_inequivalent_site_index=True ) nn_indices = [nn_site["inequiv_index"] for nn_site in nn_sites] nnn_sites = self.get_next_nearest_neighbors( site_index, inc_inequivalent_site_index=True ) nnn = defaultdict(list) for nnn_site in nnn_sites: nnn[nnn_site["connectivity"]].append(nnn_site["inequiv_index"]) equiv_sites = [ i for i in range(len(self.equivalent_sites)) if self.equivalent_sites[i] == self.equivalent_sites[site_index] ] sym_labels = tuple({self.symmetry_labels[x] for x in equiv_sites}) poly_formula = self._get_poly_formula(geometry, nn_sites, nnn_sites) return { "element": element, "geometry": geometry, "nn": nn_indices, "nnn": dict(nnn), "poly_formula": poly_formula, "sym_labels": sym_labels, } def get_bond_distance_summary(self, site_index: int) -> dict[int, list[float]]: """Gets the bond distance summary for a site. Args: site_index: The site index (zero based). Returns: The bonding data for the site, formatted as:: {to_site: [dist_1, dist_2, dist_3, ...]} Where ``to_site`` is the index of a nearest neighbor site and ``dist_1`` etc are the bond distances as :obj:`float`. """ bonds = defaultdict(list) for nn_site in self.get_nearest_neighbors(site_index): to_site = nn_site["inequiv_index"] bonds[to_site].append(nn_site["dist"]) return defaultdict_to_dict(bonds) def get_connectivity_angle_summary( self, site_index: int ) -> dict[int, dict[str, list[float]]]: """Gets the connectivity angle summary for a site. The connectivity angles are the angles between a site and its next nearest neighbors. Args: site_index: The site index (zero based). Returns: The connectivity angle data for the site, formatted as:: { to_site: { connectivity_a: [angle_1, angle_2, ...] connectivity_b: [angle_1, angle_2, ...] } } Where ``to_site`` is the index of a next nearest neighbor site, ``connectivity_a`` etc are the bonding connectivity type, e.g. ``'edge'`` or ``'corner'`` (for edge-sharing and corner-sharing connectivity), and ``angle_1`` etc are the bond angles as :obj:`float`. """ connectivities: defaultdict[int, defaultdict[str, list[float]]] = defaultdict( lambda: defaultdict(list) ) for nnn_site in self.get_next_nearest_neighbors( site_index, inc_inequivalent_site_index=True ): to_site = nnn_site["inequiv_index"] connectivity = nnn_site["connectivity"] connectivities[to_site][connectivity].extend(nnn_site["angles"]) return defaultdict_to_dict(connectivities) def get_nnn_distance_summary( self, site_index: int ) -> dict[int, dict[str, list[float]]]: """Gets the next nearest neighbor distance summary for a site. Args: site_index: The site index (zero based). Returns: The connectivity distance data for the site, formatted as:: { to_site: { connectivity_a: [distance_1, distance_2, ...] connectivity_b: [distance_1, distance_2, ...] } } Where ``to_site`` is the index of a next nearest neighbor site, ``connectivity_a`` etc are the bonding connectivity type, e.g. ``'edge'`` or ``'corner'`` (for edge-sharing and corner-sharing connectivity), and ``distance_1`` etc are the bond angles as :obj:`float`. """ connectivities: defaultdict[int, defaultdict[str, list[float]]] = defaultdict( lambda: defaultdict(list) ) for nnn_site in self.get_next_nearest_neighbors( site_index, inc_inequivalent_site_index=True ): to_site = nnn_site["inequiv_index"] connectivity = nnn_site["connectivity"] connectivities[to_site][connectivity].append(nnn_site["distance"]) return defaultdict_to_dict(connectivities) def get_all_site_summaries(self): """Gets the site summaries for all sites. Returns: The site summaries for all sites, formatted as:: { site_index: site_summary } Where ``site_summary`` has the same format as produced by :meth:`SiteAnalyzer.get_site_summary`. """ return { site: self.get_site_summary(site) for site in set(self.equivalent_sites) } def get_all_bond_distance_summaries(self) -> dict[int, dict[int, list[float]]]: """Gets the bond distance summaries for all sites. Returns: The bond distance summaries for all sites, formatted as:: { from_site: { to_site: distances } } Where ``from_site`` and ``to_site`` are site indices and ``distances`` is a :obj:`list` of :obj:`float` of bond distances. """ return { from_site: self.get_bond_distance_summary(from_site) for from_site in set(self.equivalent_sites) } def get_all_connectivity_angle_summaries( self, ) -> dict[int, dict[int, dict[str, list[float]]]]: """Gets the connectivity angle summaries for all sites. The connectivity angles are the angles between a site and its next nearest neighbors. Returns: The connectivity angle summaries for all sites, formatted as:: { from_site: { to_site: { connectivity: angles } } } Where ``from_site`` and ``to_site`` are the site indices of two sites, ``connectivity`` is the connectivity type (e.g. ``'edge'`` or ``'face'``) and ``angles`` is a :obj:`list` of :obj:`float` of connectivity angles. """ return { from_site: self.get_connectivity_angle_summary(from_site) for from_site in set(self.equivalent_sites) } def get_all_nnn_distance_summaries( self, ) -> dict[int, dict[int, dict[str, list[float]]]]: """Gets the next nearest neighbor distance summaries for all sites. Returns: The next nearest neighbor distance summaries for all sites, formatted as:: { from_site: { to_site: { connectivity: distances } } } Where ``from_site`` and ``to_site`` are the site indices of two sites, ``connectivity`` is the connectivity type (e.g. ``'edge'`` or ``'face'``) and ``distances`` is a :obj:`list` of :obj:`float` of distances. """ return { from_site: self.get_nnn_distance_summary(from_site) for from_site in set(self.equivalent_sites) } def get_inequivalent_site_indices(self, site_indices: list[int]) -> list[int]: """Gets the inequivalent site indices from a list of site indices. Args: site_indices: The site indices. Returns: The inequivalent site indices. For example, if a structure has 4 sites where the first two are equivalent and the last two are inequivalent. If ``site_indices=[0, 1, 2, 3]`` the output will be:: [0, 0, 2, 3] """ return [self.equivalent_sites[i] for i in site_indices] def _calculate_equivalent_sites( self, likeness_tol: float = 0.001, bond_dist_tol: float = 0.01, bond_angle_tol: float = 0.1, ) -> list[int]: """Determines the indices of the structurally inequivalent sites. Args: likeness_tol: The tolerance used to determine if two likeness parameters are the same. bond_dist_tol: The tolerance used to determine if two bond lengths are the same. bond_angle_tol: The tolerance used to determine if two bond angles are the same. Two sites are considered equivalent if they are the same element, and have the same geometry and (next) nearest neighbors. Returns: A :obj:`list` of indices mapping each site in the structure to a structurally equivalent site. For example, if the first two sites are equivalent and the last two are both inequivalent, the data will be formatted as:: [0, 0, 2, 3] """ # TODO: Use site fingerprint rather than geometry type. inequiv_sites: dict[int, dict[str, Any]] = {} equivalent_sites: list[int] = [] for site_index, site in enumerate(self.bonded_structure.structure): element = get_el_sp(site.specie) geometry = self.get_site_geometry(site_index) nn_sites = self.get_nearest_neighbors( site_index, inc_inequivalent_site_index=False ) nnn_sites = self.get_next_nearest_neighbors( site_index, inc_inequivalent_site_index=False ) matched = False for inequiv_index, inequiv_site in inequiv_sites.items(): elem_match = element == inequiv_site["element"] geom_match = geometries_match( geometry, inequiv_site["geometry"], likeness_tol=likeness_tol ) nn_match = nn_summaries_match( nn_sites, inequiv_site["nn_sites"], bond_dist_tol=bond_dist_tol ) nnn_match = nnn_summaries_match( nnn_sites, inequiv_site["nnn_sites"], bond_angle_tol=bond_angle_tol ) if elem_match and geom_match and nn_match and nnn_match: equivalent_sites.append(inequiv_index) matched = True break if not matched: # no matches therefore store original site index equivalent_sites.append(site_index) site_data = { "element": element, "geometry": geometry, "nn_sites": nn_sites, "nnn_sites": nnn_sites, } inequiv_sites[site_index] = site_data return equivalent_sites def _calculate_symmetry_labels( self, sym_equivalent_atoms: Sequence[int] ) -> list[int]: """Calculates the symmetry labels for all sites in the structure. The symmetry labels number the sites in the structure. If two sites are symmetrically equivalent they share the same symmetry label. The numbering begins at 1 for each element in the structure. Args: sym_equivalent_atoms: A :obj:`list` of indices mapping each site in the structure to a symmetrically equivalent site. The data should be formatted as given by the ``equivalent_atoms`` key in :meth`SpacegroupAnalyzer.get_symmetry_dataset()`. Returns: A mapping between the site index and symmetry label for that site. """ symmetry_labels = dict() # this way is a little long winded but works if the sites aren't # grouped together by element for specie in self.bonded_structure.structure.species: el_indices = self.bonded_structure.structure.indices_from_symbol( get_el(specie) ) equiv_indices = [sym_equivalent_atoms[x] for x in el_indices] count = 1 equiv_index_to_sym_label: dict[int, int] = {} for el_index, equiv_index in zip(el_indices, equiv_indices): if equiv_index in equiv_index_to_sym_label: symmetry_labels[el_index] = equiv_index_to_sym_label[equiv_index] else: equiv_index_to_sym_label[equiv_index] = count symmetry_labels[el_index] = count count += 1 return [symmetry_labels[i] for i in sorted(symmetry_labels.keys())] def _get_poly_formula( self, geometry: dict[str, Any], nn_sites: list[dict[str, Any]], nnn_sites: list[dict[str, Any]], ) -> str | None: """Gets the polyhedra formula of the nearest neighbor atoms. The polyhedral formula is effectively the sorted nearest neighbor atoms in a reduced format. For example, if the nearest neighbors are 3 I atoms, 2 Br atoms and 1 Cl atom, the polyhedral formula will be "I3Br2Cl". The polyhedral formula will be ``None`` if the site geometry is not in :data:`robocrys.util.connected_geometries`. Args: geometry: The site geometry as produced by :meth:`SiteAnalyzer.get_site_geometry`. nn_sites: The nearest neighbor sites as produced by :meth:`SiteAnalyzer.get_nearest_neighbors`. nnn_sites: The next nearest neighbor sites as produced by :meth:`SiteAnalyzer.get_next_nearest_neighbors`. Returns: The polyhedral formula if the site geometry is in :data:`robocrys.util.connected_geometries` else ``None``. """ def order_elements(el): if self.use_iupac_formula: return [get_el_sp(el).X, el] return [get_el_sp(el).iupac_ordering, el] nnn_geometries = [nnn_site["geometry"] for nnn_site in nnn_sites] poly_formula = "" if geometry["type"] in connected_geometries and any( nnn_geometry["type"] in connected_geometries for nnn_geometry in nnn_geometries ): nn_els = [get_el(nn_site["element"]) for nn_site in nn_sites] comp = Composition("".join(nn_els)) el_amt_dict = comp.get_el_amt_dict() for e in sorted(el_amt_dict.keys(), key=order_elements): poly_formula += e poly_formula += str(formula_double_format(el_amt_dict[e])) return poly_formula or None def geometries_match( geometry_a: dict[str, Any], geometry_b: dict[str, Any], likeness_tol: float = 0.001 ) -> bool: """Determine whether two site geometries match. Geometry data should be formatted the same as produced by :meth:`robocrys.site.SiteAnalyzer.get_site_geometry`. Args: geometry_a: The first set of geometry data. geometry_b: The second set of geometry data. likeness_tol: The tolerance used to determine if two likeness parameters are the same. Returns: Whether the two geometries are the same. """ return ( geometry_a["type"] == geometry_b["type"] and abs(geometry_a["likeness"] - geometry_b["likeness"]) < likeness_tol ) def nn_summaries_match( nn_sites_a: list[dict[str, int | str]], nn_sites_b: list[dict[str, int | str]], bond_dist_tol: float = 0.01, match_bond_dists: bool = True, ) -> bool: """Determine whether two sets of nearest neighbors match. Nearest neighbor data should be formatted the same as produced by :meth:`robocrys.site.SiteAnalyzer.get_nearest_neighbors`. Args: nn_sites_a: The first set of nearest neighbors. nn_sites_b: The second set of nearest neighbors. bond_dist_tol: The tolerance used to determine if two bond lengths are the same. match_bond_dists: Whether to consider bond distances when matching. Returns: Whether the two sets of nearest neighbors match. """ def nn_sites_order(nn_site): return [nn_site["element"], nn_site["dist"]] if len(nn_sites_a) != len(nn_sites_b): return False nn_sites_a = sorted(nn_sites_a, key=nn_sites_order) nn_sites_b = sorted(nn_sites_b, key=nn_sites_order) dists_match = [ ( abs(site_a["dist"] - site_b["dist"]) < bond_dist_tol # type: ignore[operator] if match_bond_dists else True ) for site_a, site_b in zip(nn_sites_a, nn_sites_b) ] elements_match = [ site_a["element"] == site_b["element"] for site_a, site_b in zip(nn_sites_a, nn_sites_b) ] return all(d and e for d, e in zip(dists_match, elements_match)) def nnn_summaries_match( nnn_sites_a: list[dict[str, Any]], nnn_sites_b: list[dict[str, Any]], likeness_tol: float = 0.001, bond_angle_tol: float = 0.1, match_bond_angles: bool = True, ): """Determine whether two sets of next nearest neighbors match. Next nearest neighbor data should be formatted the same as produced by :meth:`robocrys.site.SiteAnalyzer.get_next_nearest_neighbors`. Args: nnn_sites_a: The first set of next nearest neighbors. nnn_sites_b: The second set of next nearest neighbors. likeness_tol: The tolerance used to determine if two likeness parameters are the same. bond_angle_tol: The tolerance used to determine if two bond angles are the same. match_bond_angles: Whether to consider bond angles when matching. Returns: Whether the two sets of next nearest neighbors match. """ def nnn_sites_order(nnn_site): return [ nnn_site["element"], nnn_site["geometry"]["type"], nnn_site["connectivity"], sorted(nnn_site["angles"]), ] if len(nnn_sites_a) != len(nnn_sites_b): return False nnn_sites_a = sorted(nnn_sites_a, key=nnn_sites_order) nnn_sites_b = sorted(nnn_sites_b, key=nnn_sites_order) elements_match = [ site_a["element"] == site_b["element"] for site_a, site_b in zip(nnn_sites_a, nnn_sites_b) ] cons_match = [ site_a["connectivity"] == site_b["connectivity"] for site_a, site_b in zip(nnn_sites_a, nnn_sites_b) ] geoms_match = [ geometries_match( site_a["geometry"], site_b["geometry"], likeness_tol=likeness_tol ) for site_a, site_b in zip(nnn_sites_a, nnn_sites_b) ] angles_match = [ ( all( abs(a_a - a_b) < bond_angle_tol for a_a, a_b in zip(sorted(site_a["angles"]), sorted(site_b["angles"])) ) if match_bond_angles else True ) for site_a, site_b in zip(nnn_sites_a, nnn_sites_b) ] return all( e and c and g and a for e, c, g, a in zip(elements_match, cons_match, geoms_match, angles_match) ) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011028.9699748 emmet_core-0.87.2/emmet/core/featurization/robocrys/describe/0000755000175100017510000000000015240717725023754 5ustar00runnerrunner././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8202715 emmet_core-0.87.2/emmet/core/featurization/robocrys/describe/__init__.py0000644000175100017510000000000015240717720026046 0ustar00runnerrunner././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8215463 emmet_core-0.87.2/emmet/core/featurization/robocrys/describe/adapter.py0000644000175100017510000003274015240717720025747 0ustar00runnerrunner"""This module implements a class to resolve the symbolic references in condensed structure data. """ from __future__ import annotations from collections import defaultdict, namedtuple from typing import TYPE_CHECKING import numpy as np from emmet.core.io.pymatgen import get_el_sp from emmet.core.featurization.robocrys.adapter import BaseAdapter if TYPE_CHECKING: from typing import Any ComponentDetails = namedtuple( "ComponentDetails", [ "formula", "count", "dimensionality", "molecule_name", "orientation", "nsites", "index", ], ) ComponentGroup = namedtuple( "ComponentGroup", ["formula", "dimensionality", "count", "components", "molecule_name", "nsites"], ) SiteGroup = namedtuple("SiteGroup", ["element", "count", "sites"]) NeighborSiteDetails = namedtuple( "NeighborSiteDetails", ["element", "count", "sites", "sym_label"] ) NextNeighborSiteDetails = namedtuple( "NextNeighborSiteDetails", [ "element", "count", "geometry", "sites", "sym_label", "connectivity", "poly_formula", ], ) class DescriptionAdapter(BaseAdapter): """Class to facilitate pulling data from the condensed structure dictionary. Attributes: sym_labels: The symmetry labels as strings. use_iupac_ordering (bool, optional): Whether to order formulas by the iupac "electronegativity" series, defined in Table VI of "Nomenclature of Inorganic Chemistry (IUPAC Recommendations 2005)". This ordering effectively follows the groups and rows of the periodic table, except the Lanthanides, Actanides and hydrogen. If set to ``False``, the elements will be ordered according to the electronegativity values. Args: condensed_structure: The condensed structure data, formatted as produced by :meth:`robocrys.condense.StructureCondenser.condense_structure`. """ def __init__( self, condensed_structure: dict[str, Any], use_iupac_ordering: bool = True ): super().__init__(condensed_structure) self.use_iupac_ordering = use_iupac_ordering self.sym_labels = { site_index: self.get_sym_label(site_index) for site_index in self.sites } def get_nearest_neighbor_details( self, site_index: int, group: bool = False ) -> list[NeighborSiteDetails]: """Gets a summary of all the nearest neighbors to a site. Args: site_index: An inequivalent site index. group: Whether to group all nearest neighbor sites with the same element together. Returns: A :obj:`list` of ``NeighborSiteDetails`` objects, each with the attributes: - ``element`` (``str``): The element of the nearest neighbor site. - ``count`` (``int``): The number of sites of this type. - ``sym_label`` (``str``): The symmetry label. - ``sites`` (``list[int]``): The site indices representing this nearest neighbor. Can be more than one site if ``group_by_element=True``. """ nn_sites = self.sites[site_index]["nn"] nn_dict = defaultdict(list) for nn_site in set(nn_sites): element = self.sites[nn_site]["element"] labels = self.sym_labels[nn_site] identity = (element,) if group else (element, labels) nn_dict[identity].append( {"count": nn_sites.count(nn_site), "labels": labels, "site": nn_site} ) nn_details = [] for identity, nn_group in nn_dict.items(): sites = [nn_site["site"] for nn_site in nn_group] nn_details.append( NeighborSiteDetails( element=identity[0], sites=sites, count=sum([nn_site["count"] for nn_site in nn_group]), sym_label=self.get_sym_label(sites), ) ) return sorted(nn_details, key=self._site_order) def get_next_nearest_neighbor_details( self, site_index: int, group: bool = False ) -> list[NextNeighborSiteDetails]: """Gets a summary of all the next nearest neighbors to a site. We only get the summaries for next nearest neighbor sites that have a geometry type listed in :attr:`robocrys.util.connected_geometries` and have a ``poly_formula``. Args: site_index: An inequivalent site index. group: Whether to group together all next nearest neighbor sites with the same element, connectivity and geometry but different symmetry labels. Returns: A :obj:`list` of ``NextNeighborSiteDetails`` objects, each with the attributes: - ``element`` (``str``): The element of the next nearest neighbor site. - ``connectivity`` (``str``): The connectivity type to this site. - ``geometry`` (``str``): The geometry type of the next nearest neighbor. - ``count`` (``int``): The number of sites of this type. - ``sym_label`` (``str``): The symmetry label. - ``sites`` (``list[int]``): The site indices representing this next nearest neighbor. Can be more than one site if ``group=True``. - ``poly_formula`` (``str``): The polyhedral formula. """ nnn = self.sites[site_index]["nnn"] # get a list of tuples of (nnn_site_index, connectivity) con_data = [ (nnn_site_index, connectivity) for connectivity, sites in nnn.items() for nnn_site_index in set(sites) ] nnn_dict = defaultdict(list) for nnn_site, connectivity in con_data: poly_formula = self.sites[nnn_site]["poly_formula"] if not poly_formula: # only interested in describing the connectivity to other # polyhedral sites of interest. continue element = self.sites[nnn_site]["element"] labels = self.sym_labels[nnn_site] geometry = self.sites[nnn_site]["geometry"]["type"] if group: identity: tuple[Any, ...] = (element, connectivity, geometry) else: identity = (element, connectivity, geometry, labels) nnn_dict[identity].append( { "count": nnn[connectivity].count(nnn_site), "labels": labels, "site": nnn_site, "poly_formula": poly_formula, } ) nnn_details = [] for identity, nnn_group in nnn_dict.items(): sites = [nnn_site["site"] for nnn_site in nnn_group] nnn_details.append( NextNeighborSiteDetails( element=identity[0], connectivity=identity[1], geometry=identity[2], sites=sites, poly_formula=nnn_group[0]["poly_formula"], count=sum([nn_site["count"] for nn_site in nnn_group]), sym_label=self.get_sym_label(sites), ) ) return sorted(nnn_details, key=self._site_order) def get_component_details(self) -> list[ComponentDetails]: """Gets a summary of all components. Returns: A :obj:`list` of ``ComponentDetails`` objects, each with the attributes: - ``count`` (``int``): The number of these components in the structure. - ``formula`` (``str``): The component formula. - ``dimensionality`` (``int``): The component dimensionality. - ``molecule_name`` (``str`` or ``None``): The molecule name if applicable, else ``None``. - ``orientation`` (``tuple[int]``): The component orientation. - ``index`` (``list[int]``): The component inequivalent index. """ component_details = [] for index in set(self.component_makeup): component_details.append( ComponentDetails( count=self.component_makeup.count(index), formula=self.components[index]["formula"], dimensionality=self.components[index]["dimensionality"], molecule_name=self.components[index]["molecule_name"], orientation=self.components[index]["orientation"], nsites=len(self.components[index]["sites"]), index=index, ) ) return sorted(component_details, key=_component_order) def get_component_groups(self) -> list[ComponentGroup]: """Gets a summary of all components groups. Returns: The components, grouped together by formula, dimensionality and molecule name. The data will be returned as a :obj:`list` of ``ComponentGroup`` objects, each with the attributes: - ``count`` (``int``): The total number of components in this group. - ``formula`` (``str``): The formula of the components. - ``dimensionality`` (``int``): The dimensionality of the components. - ``molecule_name`` (``str`` or ``None``): The molecule name if applicable, else ``None``. - ``components`` (``list[ComponentDetails]``): The components in the group. """ component_details = self.get_component_details() grouped_components = defaultdict(list) for component in component_details: identity = ( component.dimensionality, component.formula, component.molecule_name, ) grouped_components[identity].append(component) component_group_details = [] for identity, group in grouped_components.items(): component_group_details.append( ComponentGroup( count=sum(component.count for component in group), dimensionality=identity[0], formula=identity[1], molecule_name=identity[2], components=sorted(group, key=_component_order), nsites=group[0].nsites, ) ) return sorted(component_group_details, key=_component_order) def get_component_site_groups(self, component_index: int) -> list[SiteGroup]: """Gets a summary of the sites in a component. Returns: The sites, grouped together by element. The data will be returned as a :obj:`list` of ``SiteGroup`` objects, each with the attributes: - ``count`` (``int``): The total number of sites in this group. - ``element`` (``str``): The site element. - ``sites`` (``list[int]``): A list of site indices in this group. """ sites = list(set(self.components[component_index]["sites"])) grouped_sites = defaultdict(list) for site_index in sites: grouped_sites[self.elements[site_index]].append(site_index) site_groups = [] for element, group in grouped_sites.items(): site_groups.append( SiteGroup( count=sum(sites.count(site_index) for site_index in group), element=element, sites=group, ) ) return sorted(site_groups, key=self._site_order) def get_sym_label(self, site_indices: int | list[int]) -> str: """Convert site indices into a formatted symmetry label. Args: site_indices: The site indices. Returns: The formatted symmetry label. E.g., if the set of symmetry labels for the sites looks like ``(1, 2)``, the symmetry label will be ``(1,2)``. """ if isinstance(site_indices, (int, np.int32)): # If only one to_site is provided turn it into a list site_indices = [site_indices] all_labels = sorted( [ label for site_index in site_indices for label in self.sites[site_index]["sym_labels"] ] ) return "({})".format(",".join(map(str, sorted(all_labels)))) def _site_order(self, s: SiteGroup | NeighborSiteDetails | NextNeighborSiteDetails): """Utility function to help sort NeighborSiteDetails and SiteGroups.""" specie = get_el_sp(s.element) x = specie.iupac_ordering if self.use_iupac_ordering else specie.X if isinstance(s, NeighborSiteDetails): return [x, s.count, s.sym_label, s.sites] if isinstance(s, NextNeighborSiteDetails): return [ s.connectivity, s.geometry, s.count, x, s.poly_formula, s.sym_label, s.sites, ] return [x, s.count, s.sites] def _component_order(c: ComponentDetails | ComponentGroup): """Utility function to help sort ComponentDetails and ComponentGroups.""" mn = c.molecule_name if c.molecule_name else "z" if isinstance(c, ComponentDetails): ori = c.orientation if c.orientation else (0, 0, 0) return [mn, c.dimensionality, c.formula, ori, c.count] return [mn, c.dimensionality, c.formula, c.count] ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8215463 emmet_core-0.87.2/emmet/core/featurization/robocrys/describe/describer.py0000644000175100017510000007350515240717720026275 0ustar00runnerrunner"""This module provides a class for generating descriptions of condensed structure data. Todo: * Indicate when molecules have been simplified in the mineral description. * Handle distortion in connected polyhedra description. """ from __future__ import annotations from typing import TYPE_CHECKING import inflect from emmet.core.io.pymatgen import htmlify, latexify, latexify_spacegroup, unicodeify from emmet.core.featurization.robocrys.describe.adapter import DescriptionAdapter from emmet.core.featurization.robocrys.util import ( dimensionality_to_shape, geometry_to_polyhedra, get_el, get_formatted_el, htmlify_spacegroup, polyhedra_plurals, unicodeify_spacegroup, ) if TYPE_CHECKING: from typing import Any en = inflect.engine() class StructureDescriber: def __init__( self, describe_mineral: bool = True, describe_component_makeup: bool = True, describe_components: bool = True, describe_symmetry_labels: bool = True, describe_oxidation_states: bool = True, describe_bond_lengths: bool = True, bond_length_decimal_places: int = 2, distorted_tol: float = 0.6, only_describe_cation_polyhedra_connectivity: bool = True, only_describe_bonds_once: bool = True, fmt: str = "raw", return_parts: bool = False, ): """A class to convert condensed structure data into text descriptions. Args: describe_mineral: Whether to describe the crystal mineral data. describe_component_makeup: Whether to describe the component makeup of the structure. describe_components: Whether to describe the component's sites. describe_symmetry_labels: Whether to include the symmetry labels when describing atomic sites. describe_oxidation_states: Whether to include oxidation states in the description. describe_bond_lengths: Whether to describe bond lengths. bond_length_decimal_places: Number of decimal places to round bond lengths. distorted_tol: The value under which the site geometry will be classified as distorted. only_describe_cation_polyhedra_connectivity: Whether to only describe cation polyhedra instead of both cation and anion polyhedra. only_describe_bonds_once: Whether only describe bond lengths once. For example, don't describe the bond lengths from Pb to I and also from I to Pb. fmt: How to format element strings, formulas and spacegroups. Options are: - "raw" (default): Don't apply special formatting (e.g. "SnO2"). - "unicode": Format super/subscripts using unicode characters (e.g. SnO₂). - "latex": Use LaTeX markup for formatting (e.g. "SnO$_2$"). - "html": Use html markup for formatting (e.g. "SnO2"). return_parts: Whether to return the individual parts of the description as a :obj:`dict`, or the whole description as a :obj:`str`. """ self.distorted_tol = distorted_tol self.describe_mineral = describe_mineral self.describe_component_makeup = describe_component_makeup self.describe_components = describe_components self.describe_symmetry_labels = describe_symmetry_labels self.describe_oxidation_state = describe_oxidation_states self.describe_bond_lengths = describe_bond_lengths self.bond_length_decimal_places = bond_length_decimal_places self.cation_polyhedra_only = only_describe_cation_polyhedra_connectivity self.only_describe_bonds_once = only_describe_bonds_once self.fmt = fmt self.return_parts = return_parts self.angle_decimal_places = 0 if fmt == "latex": self.angstrom = r"$\AA$" self.degree = r"$^{\circ}$" else: self.angstrom = "Å" self.degree = "°" self._da: DescriptionAdapter self._seen_bonds: set = set() def describe(self, condensed_structure: dict[str, Any]) -> str | dict[str, str]: """Convert a condensed structure into a text description. Args: condensed_structure: The condensed structure data, formatted as produced by :meth:`StructureCondenser.condense_structure`. Returns: A description of the structure. If :attr:`StructureDescriber.return_parts` is ``False``, the description will be returned as a :obj:`str`. If it is equal to ``True``, the description will be returned as a :obj:`dict` with the keys 'mineral', 'component_makeup' and 'components', each containing the relevant part of the description. """ self._da = DescriptionAdapter(condensed_structure) self._seen_bonds = set() description = {} if self.describe_mineral: description["mineral"] = self.get_mineral_description() if self.describe_component_makeup: description["component_makeup"] = self.get_component_makeup_summary() if self.describe_components: description["components"] = self.get_all_component_descriptions() if not self.return_parts: return " ".join( description[part] for part in ["mineral", "component_makeup", "components"] if part in description and description[part] != "" ) return description def get_mineral_description(self) -> str: """Gets the mineral name and space group description. If the structure is a perfect match for a known prototype (e.g. the distance parameter is -1, the mineral name is the prototype name. If a structure is not a perfect match but similar to a known mineral, "-like" will be added to the mineral name. If the structure is a good match to a mineral but contains a different number of element types than the mineral prototype, "-derived" will be added to the mineral name. Returns: The description of the mineral name. """ spg_symbol = self._da.spg_symbol formula = self._da.formula if self.fmt == "latex": spg_symbol = latexify_spacegroup(self._da.spg_symbol) formula = latexify(formula) elif self.fmt == "unicode": spg_symbol = unicodeify_spacegroup(self._da.spg_symbol) formula = unicodeify(formula) elif self.fmt == "html": spg_symbol = htmlify_spacegroup(self._da.spg_symbol) formula = htmlify(formula) if mineral_name := get_mineral_name(self._da.mineral): # replace latex-like characters with unicode latex_reps = { "$\\mu$": "\u03bc", "\\'{c}": "\u0107", } if self.fmt in {"html", "unicode"} and any( k in mineral_name for k in latex_reps ): for k, v in latex_reps.items(): mineral_name = mineral_name.replace(k, v) desc = f"{formula} is {mineral_name} structured and" else: desc = f"{formula}" desc += " crystallizes in the {} {} space group.".format( self._da.crystal_system, spg_symbol ) return desc def get_component_makeup_summary(self) -> str: """Gets a summary of the makeup of components in a structure. Returns: A description of the number of components and their dimensionalities and orientations. """ component_groups = self._da.get_component_groups() if ( len(component_groups) == 1 and component_groups[0].count == 1 and component_groups[0].dimensionality == 3 ): desc = "" else: if self._da.dimensionality == 3: desc = "The structure consists of " else: desc = ( f"The structure is {en.number_to_words(self._da.dimensionality)}" # type: ignore[arg-type] "-dimensional and consists of " ) component_makeup_summaries = [] nframeworks = len( [ c for g in component_groups for c in g.components if c.dimensionality == 3 ] ) for component_group in component_groups: if nframeworks == 1 and component_group.dimensionality == 3: s_count: str = "a" else: s_count = en.number_to_words(component_group.count) # type: ignore[assignment] dimensionality = component_group.dimensionality if component_group.molecule_name: shape = "atom" if component_group.nsites == 1 else "molecule" shape = en.plural(shape, s_count) formula = component_group.molecule_name else: shape = en.plural(dimensionality_to_shape[dimensionality], s_count) formula = component_group.formula if self.fmt == "latex": formula = latexify(formula) elif self.fmt == "unicode": formula = unicodeify(formula) elif self.fmt == "html": formula = htmlify(formula) comp_desc = f"{s_count} {formula} {shape}" if component_group.dimensionality in [1, 2]: orientations = list( {str(c.orientation) for c in component_group.components} ) s_direction = en.plural("direction", len(orientations)) comp_desc += " oriented in the {} {}".format( en.join(orientations), s_direction ) component_makeup_summaries.append(comp_desc) if nframeworks == 1 and len(component_makeup_summaries) > 1: # when there is a single framework, make the description read # "... and 8 Sn atoms inside a SnO2 framework" instead of # "..., 8 Sn atoms and one SnO2 framework" # This works because the component summaries are sorted by # dimensionality desc += en.join(component_makeup_summaries[:-1]) desc += f" inside {component_makeup_summaries[-1]}." else: desc += en.join(component_makeup_summaries) + "." return desc def get_all_component_descriptions(self) -> str: """Gets the descriptions of all components in the structure. Returns: A description of all components in the structure. """ if len(self._da.components) == 1: return self.get_component_description( self._da.get_component_groups()[0].components[0].index, single_component=True, ) component_groups = self._da.get_component_groups() component_descriptions = [] for group in component_groups: for component in group.components: if group.molecule_name: # don't describe known molecules continue formula = group.formula group_count = group.count component_count = component.count shape = dimensionality_to_shape[group.dimensionality] if self.fmt == "latex": formula = latexify(formula) elif self.fmt == "unicode": formula = unicodeify(formula) elif self.fmt == "html": formula = htmlify(formula) if group_count == component_count: s_filler = "the" if group_count == 1 else "each" else: s_filler = f"{en.number_to_words(component_count)} of the" shape = en.plural(shape) desc = f"In {s_filler} {formula} {shape}, " desc += self.get_component_description(component.index) component_descriptions.append(desc) return " ".join(component_descriptions) def get_component_description( self, component_index: int, single_component: bool = False ) -> str: """Gets the descriptions of all sites in a component. Args: component_index: The index of the component single_component: Whether the structure contains only a single component. Returns: The description for all sites in the components. """ desc = [] first_group = True for site_group in self._da.get_component_site_groups(component_index): if len(site_group.sites) == 1: desc.append(self.get_site_description(site_group.sites[0])) else: element = get_formatted_el( site_group.element, "", use_oxi_state=self.describe_oxidation_state, use_sym_label=False, fmt=self.fmt, ) s_there = "there" if first_group and not single_component else "There" s_count = en.number_to_words(len(site_group.sites)) # type: ignore[arg-type] desc.append(f"{s_there} are {s_count} inequivalent {element} sites.") for i, site in enumerate(site_group.sites): s_ordinal = en.number_to_words(en.ordinal(i + 1)) # type: ignore[arg-type] desc.append(f"In the {s_ordinal} {element} site,") desc.append(self.get_site_description(site)) first_group = False return " ".join(desc) def get_site_description(self, site_index: int) -> str: """Gets a description of the geometry and bonding of a site. If the site likeness (order parameter) is less than ``distorted_tol``, "distorted" will be added to the geometry description. Args: site_index: An inequivalent site index. Returns: A description of the geometry and bonding of a site. """ site = self._da.sites[site_index] if site["poly_formula"] and ( self.cation_polyhedra_only or "+" in site["element"] ): desc = self._get_poly_site_description(site_index) tilt_desc = self.get_octahedral_tilt_description(site_index) if tilt_desc: desc += ". " + tilt_desc else: element = get_formatted_el( site["element"], self._da.sym_labels[site_index], use_oxi_state=self.describe_oxidation_state, use_sym_label=self.describe_symmetry_labels, fmt=self.fmt, ) if site["geometry"]["likeness"] < self.distorted_tol: s_geometry = "distorted " else: s_geometry = "" s_geometry += site["geometry"]["type"] desc = f"{element} is bonded in {en.a(s_geometry)} geometry to " desc += self._get_nearest_neighbor_description(site_index) bond_length_desc = self._get_nearest_neighbor_bond_length_descriptions( site_index ) if bond_length_desc: desc += ". " + bond_length_desc else: desc += "." return desc def _get_poly_site_description(self, site_index: int): """Gets a description of a connected polyhedral site. If the site likeness (order parameter) is less than ``distorted_tol``, "distorted" will be added to the geometry description. Args: site_index: An inequivalent site index. Returns: A description the a polyhedral site, including connectivity. """ site = self._da.sites[site_index] nnn_details = self._da.get_next_nearest_neighbor_details( site_index, group=not self.describe_symmetry_labels ) from_element = get_formatted_el( site["element"], self._da.sym_labels[site_index], use_oxi_state=self.describe_oxidation_state, use_sym_label=self.describe_symmetry_labels, fmt=self.fmt, ) from_poly_formula = site["poly_formula"] if self.fmt == "latex": from_poly_formula = latexify(from_poly_formula) elif self.fmt == "unicode": from_poly_formula = unicodeify(from_poly_formula) elif self.fmt == "html": from_poly_formula = htmlify(from_poly_formula) s_from_poly_formula = get_el(site["element"]) + from_poly_formula if site["geometry"]["likeness"] < self.distorted_tol: s_distorted = "distorted " else: s_distorted = "" s_polyhedra = geometry_to_polyhedra[site["geometry"]["type"]] s_polyhedra = polyhedra_plurals[s_polyhedra] nn_desc = self._get_nearest_neighbor_description(site_index) desc = f"{from_element} is bonded to {nn_desc} to form " # handle the case we were are connected to the same type of polyhedra if ( nnn_details[0].element == site["element"] and len( {(nnn_site.element, nnn_site.poly_formula) for nnn_site in nnn_details} ) ) == 1: connectivities = list({nnn_site.connectivity for nnn_site in nnn_details}) s_mixture = "a mixture of " if len(connectivities) != 1 else "" s_connectivities = en.join(connectivities) desc += "{}{}{}-sharing {} {}".format( s_mixture, s_distorted, s_connectivities, s_from_poly_formula, s_polyhedra, ) return desc # otherwise loop through nnn connectivities and describe individually desc += "{}{} {} that share ".format( s_distorted, s_from_poly_formula, s_polyhedra ) nnn_descriptions = [] for nnn_site in nnn_details: to_element = get_formatted_el( nnn_site.element, nnn_site.sym_label, use_oxi_state=False, use_sym_label=self.describe_symmetry_labels, ) to_poly_formula = nnn_site.poly_formula if self.fmt == "latex": to_poly_formula = latexify(to_poly_formula) elif self.fmt == "unicode": to_poly_formula = unicodeify(to_poly_formula) elif self.fmt == "html": to_poly_formula = htmlify(to_poly_formula) to_poly_formula = to_element + to_poly_formula to_shape = geometry_to_polyhedra[nnn_site.geometry] if len(nnn_site.sites) == 1 and nnn_site.count != 1: s_equivalent = " equivalent " else: s_equivalent = " " if nnn_site.count == 1: s_an = f" {en.an(nnn_site.connectivity)}" else: s_an = "" to_shape = polyhedra_plurals[to_shape] nnn_descriptions.append( "{}{} with {}{}{} {}".format( s_an, en.plural(nnn_site.connectivity, nnn_site.count), en.number_to_words(nnn_site.count), s_equivalent, to_poly_formula, to_shape, ) ) return desc + en.join(nnn_descriptions) def _get_nearest_neighbor_description(self, site_index: int) -> str: """Gets a description of a sites nearest neighbors. Note: This function is intended to be run directly after :meth:`get_site_description`, as the output will not form a complete sentence on its own. Args: site_index: An inequivalent site index. Returns: A description of the nearest neighbors. """ nn_details = self._da.get_nearest_neighbor_details( site_index, group=not self.describe_symmetry_labels ) last_count = 0 nn_descriptions = [] for nn_site in nn_details: element = get_formatted_el( nn_site.element, nn_site.sym_label, use_oxi_state=self.describe_oxidation_state, use_sym_label=self.describe_symmetry_labels, fmt=self.fmt, ) if len(nn_site.sites) == 1 and nn_site.count != 1: s_equivalent = " equivalent " else: s_equivalent = " " nn_descriptions.append( "{}{}{}".format( en.number_to_words(nn_site.count), s_equivalent, element ) ) last_count = nn_site.count s_atoms = "atom" if last_count == 1 else "atoms" return f"{en.join(nn_descriptions)} {s_atoms}" def _get_nearest_neighbor_bond_length_descriptions(self, site_index: int) -> str: """Gets the descriptions of the bond lengths for nearest neighbor sites. Args: site_index: An inequivalent site index. Returns: A description of the nearest neighbor bond lengths. """ if not self.describe_bond_lengths: return "" nn_details = self._da.get_nearest_neighbor_details( site_index, group=not self.describe_symmetry_labels ) bond_descriptions = [] for nn_site in nn_details: bond_descriptions.append( self.get_bond_length_description(site_index, nn_site.sites) ) # filter empty bond length description strings return " ".join(filter(lambda x: x, bond_descriptions)) def get_bond_length_description(self, from_site: int, to_sites: list[int]) -> str: """Gets a description of the bond lengths between two sets of sites. Args: from_site: An inequivalent site index. to_sites: A :obj:`list` of site indices. The site indices should all be for the same element. Returns: A description of the bond lengths or an empty string if :attr:`StructureDescriber.only_describe_bonds_once` is ``True`` and all all bond lengths have already been described. """ if self.only_describe_bonds_once: to_sites = self._filter_seen_bonds(from_site, to_sites) if not to_sites: return "" from_element = get_formatted_el( self._da.elements[from_site], self._da.sym_labels[from_site], use_oxi_state=False, use_sym_label=self.describe_symmetry_labels, ) to_element = get_formatted_el( self._da.elements[to_sites[0]], self._da.get_sym_label(to_sites), use_oxi_state=False, use_sym_label=self.describe_symmetry_labels, ) dists = self._da.get_distance_details(from_site, to_sites) # if only one bond length if len(dists) == 1: return "The {}-{} bond length is {}.".format( from_element, to_element, self._distance_to_string(dists[0]) ) discrete_bond_lengths = self._rounded_bond_lengths(dists) # if multiple bond lengths but they are all the same if len(set(discrete_bond_lengths)) == 1: s_intro = "Both" if len(discrete_bond_lengths) == 2 else "All" return "{} {}-{} bond lengths are {}.".format( s_intro, from_element, to_element, self._distance_to_string(dists[0]) ) # if two sets of bond lengths if len(set(discrete_bond_lengths)) == 2: small = min(discrete_bond_lengths) s_small_count = en.number_to_words(discrete_bond_lengths.count(small)) # type: ignore[arg-type] big = max(discrete_bond_lengths) s_big_count = en.number_to_words(discrete_bond_lengths.count(big)) # type: ignore[arg-type] s_length = en.plural("length", s_big_count) return ( "There {} {} shorter ({}) and {} longer ({}) {}-{} bond {}." ).format( en.plural_verb("is", s_small_count), s_small_count, self._distance_to_string(small), s_big_count, self._distance_to_string(big), from_element, to_element, s_length, ) # otherwise just detail the spread of bond lengths return ("There are a spread of {}-{} bond distances ranging from {}.").format( from_element, to_element, self._distance_range_to_string( min(discrete_bond_lengths), max(discrete_bond_lengths) ), ) def get_octahedral_tilt_description( self, site_index: int, ) -> str: """Gets a description of octahedral tilting angles between two sites. Currently only implemented for corner-sharing octahedra. Will throw an error if the two sites are not next nearest neighbors. Args: site_index: An inequivalent site index. Returns: A description of the octahedral tilting angles. """ nnn_details = self._da.get_next_nearest_neighbor_details( site_index, group=not self.describe_symmetry_labels ) to_sites = [ site for nnn_site in nnn_details for site in nnn_site.sites if nnn_site.geometry == "octahedral" and nnn_site.connectivity == "corner" ] angles = self._da.get_angle_details(site_index, to_sites, "corner") discrete_angles = list(set(self._rounded_angles(angles))) tilts = [abs(180 - angle) for angle in discrete_angles] if not tilts: return "" # if only one bond length if len(tilts) == 1: if tilts[0] == 0: return "The corner-sharing octahedra are not tilted" return "The corner-sharing octahedral tilt angles are {}".format( self._angle_to_string(tilts[0]) ) # otherwise just detail the spread of bond lengths return "The corner-sharing octahedral tilt angles range from {}".format( self._angle_range_to_string(min(tilts), max(tilts)) ) def _filter_seen_bonds(self, from_site: int, to_sites: list[int]) -> list[int]: """Filter the list of to_sites to only include unseen bonds. Args: from_site: An inequivalent site index. to_sites: A :obj:`list` of site indices. The site indices should all be for the same element. Returns: The list of unseen bonds. """ # get a list of tuples of (from_site, to_site) for all to_sites bonds = [(f, t) for f, t in zip([from_site] * len(to_sites), to_sites)] # use frozen set as it is invariant to the order of the sites not_seen = [x for x in bonds if frozenset(x) not in self._seen_bonds] # only describe the bonds between unseen site pairs not_seen_to_sites = [] for from_site, to_site in not_seen: not_seen_to_sites.append(to_site) self._seen_bonds.add(frozenset((from_site, to_site))) return not_seen_to_sites def _rounded_bond_lengths(self, data: list[float]) -> tuple[float, ...]: """Function to round bond lengths to a number of decimal places.""" return tuple( float("{:.{}f}".format(x, self.bond_length_decimal_places)) for x in data ) def _distance_to_string(self, distance: float) -> str: """Utility function to round a distance and add an Angstrom symbol.""" return "{:.{}f} {}".format( distance, self.bond_length_decimal_places, self.angstrom ) def _distance_range_to_string(self, dist_a: float, dist_b: float) -> str: """Utility function to format a range of distances.""" return "{:.{}f}-{:.{}f} {}".format( dist_a, self.bond_length_decimal_places, dist_b, self.bond_length_decimal_places, self.angstrom, ) def _rounded_angles(self, data: list[float]) -> tuple[float, ...]: """Function to round angles to a number of decimal places.""" return tuple( float("{:.{}f}".format(x, self.angle_decimal_places)) for x in data ) def _angle_to_string(self, angle: float) -> str: """Utility function to round a distance and add an Angstrom symbol.""" return "{:.{}f}{}".format(angle, self.angle_decimal_places, self.degree) def _angle_range_to_string(self, angle_a: float, angle_b: float) -> str: """Utility function to format a range of distances.""" return "{:.{}f}-{:.{}f}{}".format( angle_a, self.angle_decimal_places, angle_b, self.angle_decimal_places, self.degree, ) def get_mineral_name(mineral_dict: dict[str, Any]) -> str | None: """Get the mineral name from a mineral dictionary. Args: mineral_dict: The mineral dictionary from the condensed description. Returns: If ``mineral_dict["type"]`` is set, the mineral name will be returned as a string, else ``None`` will be returned. """ if mineral_dict["type"]: if not mineral_dict["n_species_type_match"]: suffix = "-derived" elif mineral_dict["distance"] >= 0: suffix = "-like" else: suffix = "" return "{}{}".format(mineral_dict["type"], suffix) return None ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8215463 emmet_core-0.87.2/emmet/core/featurization/robocrys/util.py0000644000175100017510000001640015240717720023517 0ustar00runnerrunner"""Miscellaneous utility functions and common data. Attributes: common_formulas: A set of common formulas. The keys to the data are strings from :obj:`pymatgen.core.composition.Composition.reduced_formula`. connected_geometries: A list of geometries that are considered "connectable" polyhedra. E.g. Their face-sharing, edge-sharing, etc properties are of interest. geometry_to_polyhedra: A mapping from geometry type (e.g. octahedral) to the plural polyhedra name (e.g. octahedra). dimensionality_to_shape: A mapping from dimensionality to the component shape. """ from __future__ import annotations import gzip import orjson from pathlib import Path import re from collections import defaultdict from typing import TYPE_CHECKING from emmet.core.io.pymatgen import Element, Species, get_el_sp, unicodeify_spacegroup if TYPE_CHECKING: from pathlib import Path __all__ = ["unicodeify_spacegroup"] def _get_common_formulas() -> dict[str, str]: """Retrieve common formula information from stored data.""" with gzip.open( (Path(__file__).parent / "condense" / "formula_aliases.json.gz").resolve(), "rb" ) as f: all_aliases = orjson.loads(f.read()) return { k: all_aliases["alias"][i] if all_aliases["alias"][i] is not None else k for i, k in enumerate(all_aliases["name"]) } common_formulas: dict[str, str] = _get_common_formulas() connected_geometries: list[str] = [ "tetrahedral", "octahedral", "trigonal pyramidal", "square pyramidal", "trigonal bipyramidal", "pentagonal pyramidal", "hexagonal pyramidal", "pentagonal bipyramidal", "hexagonal bipyramidal", "cuboctahedral", ] geometry_to_polyhedra: dict[str, str] = { "octahedral": "octahedra", "tetrahedral": "tetrahedra", "trigonal pyramidal": "trigonal pyramid", "square pyramidal": "square pyramid", "trigonal bipyramidal": "trigonal bipyramid", "pentagonal pyramidal": "pentagonal pyramid", "hexagonal pyramidal": "hexagonal pyramid", "pentagonal bipyramidal": "pentagonal bipyramid", "hexagonal bipyramidal": "hexagonal bipyramid", "cuboctahedral": "cuboctahedra", } polyhedra_plurals: dict[str, str] = { "octahedra": "octahedra", "tetrahedra": "tetrahedra", "trigonal pyramid": "trigonal pyramids", "square pyramid": "square pyramids", "trigonal bipyramid": "trigonal bipyramids", "pentagonal pyramid": "pentagonal pyramids", "hexagonal pyramid": "hexagonal pyramids", "pentagonal bipyramid": "pentagonal bipyramids", "hexagonal bipyramid": "hexagonal bipyramids", "cuboctahedra": "cuboctahedra", } dimensionality_to_shape: dict[int, str] = { 3: "framework", 2: "sheet", 1: "ribbon", 0: "cluster", } def get_el(obj: Element | Species | str | int) -> str: """Utility method to get an element str from a symbol, Element, or Specie. Args: obj: An arbitrary object. Supported objects are Element/Species objects, integers (representing atomic numbers), or strings (element symbols or species strings). Returns: The element as a string. """ if isinstance(obj, str): obj = get_el_sp(obj) if isinstance(obj, Element): return obj.name if isinstance(obj, Species): return obj.element.name if isinstance(obj, int): return Element.from_Z(obj).name raise ValueError(f"Unsupported element type: {type(obj)}.") def get_formatted_el( element: str, sym_label: str, use_oxi_state: bool = True, use_sym_label: bool = True, fmt: str = "raw", ): """Formats an element string. Performs a variety of functions, including: - Changing "Sn+0" to "Sn". - Inserting the symmetry label between the element and oxidation state, if required. - Removing the oxidation state if required. - Latexifying the element and oxidation state. - Unicodeifying the element and oxidation state. - Converting the element and oxidation state to html. Args: element: The element string (possibly including the oxidation state. E.g. "Sn" or "Sn2+". sym_label: The symmetry label. E.g. "(1)" use_oxi_state: Whether to include the oxidation state, if present. use_sym_label: Whether to use the symmetry label. fmt: How to format the element strings. Options are: - "raw" (default): Don't apply special formatting (e.g. "SnO2"). - "unicode": Format super/subscripts using unicode characters (e.g. SnO₂). - "latex": Use LaTeX markup for formatting (e.g. "SnO$_2$"). - "html": Use html markup for formatting (e.g. "SnO2"). Returns: The formatted element string. """ specie = get_el_sp(element) # NB: the `str(specie)` is only to make mypy happy: # `get_el_sp` can return a `DummySpecies` which has # no `name` but has `__str__` formatted_element = specie.name or str(specie) if use_sym_label: formatted_element += sym_label if ( use_oxi_state and isinstance(specie, Species) and (_oxi_state := specie.oxi_state) and _oxi_state != 0 ): sign = "+" if _oxi_state > 0 else "-" if _oxi_state % 1 == 0: oxi_state = f"{int(abs(_oxi_state)):d}{sign}" else: oxi_state = f"{abs(_oxi_state):.2f}{sign}" if fmt == "latex": oxi_state = f"^{{{oxi_state}}}" elif fmt == "unicode": oxi_state = superscript_number(oxi_state) elif fmt == "html": oxi_state = f"{oxi_state}" formatted_element += oxi_state return formatted_element def superscript_number(string): """Converts a string containing numbers to superscript. Will only convert the numbers 0-9, and the + and - characters. Args: string: A string containing the numbers 0-9 or +/- characters. Returns: The superscript string. """ subscript_unicode_map = { 0: "⁰", 1: "¹", 2: "²", 3: "³", 4: "⁴", 5: "⁵", 6: "⁶", 7: "⁷", 8: "⁸", 9: "⁹", "-": "⁻", "+": "⁺", ".": "\u1427", } for original_subscript, subscript_unicode in subscript_unicode_map.items(): string = string.replace(str(original_subscript), subscript_unicode) return string def htmlify_spacegroup(spacegroup_symbol: str) -> str: """Formats a spacegroup using unicode symbols. E.g. P-42_1m -> P4̅21m Args: spacegroup_symbol: A spacegroup symbol. Returns: The html formatted spacegroup symbol. """ overline = "\u0305" # u"\u0304" (macron) is also an option symbol = re.sub(r"_(\d)", r"\1", spacegroup_symbol) symbol = re.sub(r"-(\d)", rf"\1{overline}", symbol) return symbol def defaultdict_to_dict(dictionary: defaultdict) -> dict: """Recursively convert nested :obj:`defaultdict` to :obj:`dict`. Args: dictionary: A defaultdict. Returns: The defaultdict as a :obj:`dict`. """ if isinstance(dictionary, defaultdict): return {k: defaultdict_to_dict(v) for k, v in dictionary.items()} return dictionary ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011028.9703429 emmet_core-0.87.2/emmet/core/feff/0000755000175100017510000000000015240717725016354 5ustar00runnerrunner././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8215463 emmet_core-0.87.2/emmet/core/feff/__init__.py0000644000175100017510000000000015240717720020446 0ustar00runnerrunner././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8218923 emmet_core-0.87.2/emmet/core/feff/task.py0000644000175100017510000000553415240717720017672 0ustar00runnerrunner"""Core definition of a VASP Task Document""" from __future__ import annotations from typing import Any from pydantic import Field from emmet.core.io.pymatgen import XAS, Element, Species from emmet.core.structure import StructureMetadata from emmet.core.types.enums import ValueEnum from emmet.core.types.pymatgen_types.structure_adapter import StructureType from emmet.core.vasp.task_valid import TaskDocument as BaseTaskDocument class CalcType(ValueEnum): """ The type of FEFF Calculation XANES - Just the near-edge region EXAFS - Just the extended region XAFS - Fully stitchted XANES + EXAFS """ XANES = "XANES" EXAFS = "EXAFS" XAFS = "XAFS" class TaskDocument(BaseTaskDocument, StructureMetadata): """Task Document for a FEFF XAS Calculation. Doesn't support EELS for now""" calc_code: str = "FEFF" structure: StructureType input_parameters: dict[str, Any] = Field( {}, description="Input parameters for the FEFF calculation" ) spectrum: list[list[float]] = Field( [[]], description="Raw spectrum data from FEFF xmu.dat or eels.dat" ) absorbing_atom: int = Field( ..., description="Index in the cluster or structure for the absorbing atom" ) spectrum_type: CalcType = Field(..., title="XAS Spectrum Type") edge: str = Field( ..., title="Absorption Edge", description="The interaction edge for XAS" ) # TEMP Stub properties for compatibility with atomate drone @property def absorbing_element(self) -> Element | Species: if isinstance(self.structure[self.absorbing_atom].specie, Element): return self.structure[self.absorbing_atom].specie return self.structure[self.absorbing_atom].specie.element @property def xas_spectrum(self) -> XAS: if not hasattr(self, "_xas_spectrum"): if not all([len(p) == 6 for p in self.spectrum]): raise ValueError( "Spectrum data doesn't appear to be from xmu.dat which holds XAS data" ) energy = [point[0] for point in self.spectrum] # (eV) intensity = [point[3] for point in self.spectrum] # (mu) structure = self.structure absorbing_index = self.absorbing_atom absorbing_element = self.absorbing_element if isinstance(absorbing_element, Species): absorbing_element = absorbing_element.element edge = self.edge spectrum_type = str(self.spectrum_type) self._xas_spectrum = XAS( x=energy, y=intensity, structure=structure, absorbing_element=absorbing_element, absorbing_index=absorbing_index, edge=edge, spectrum_type=spectrum_type, ) return self._xas_spectrum ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8218923 emmet_core-0.87.2/emmet/core/fermi.py0000644000175100017510000000155715240717720017125 0ustar00runnerrunnerfrom pydantic import BaseModel, Field from emmet.core.types.typing import DateTimeType from emmet.core.utils import arrow_incompatible @arrow_incompatible class FermiDoc(BaseModel): """ Fermi surfaces. """ fermi_surfaces: list[dict] | None = Field( None, description="List of IFermi FermiSurface objects.", ) surface_types: list[str] | None = Field( None, description="Type of each fermi surface in the fermi_surfaces list.\ Is either CBM or VBM for semiconductors, or fermi_surface for metals.", ) material_id: str | None = Field( None, description="The Materials Project ID of the material. This comes in the form: mp-******.", ) last_updated: DateTimeType = Field( description="Timestamp for the most recent calculation for this fermi surface document.", ) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8218923 emmet_core-0.87.2/emmet/core/find_structure.py0000644000175100017510000000220315240717720021050 0ustar00runnerrunnerfrom pydantic import BaseModel, Field from emmet.core.mpid import MPculeID from emmet.core.types.typing import IdentifierType class FindStructure(BaseModel): """ Class defining find structure return data """ material_id: IdentifierType | None = Field( None, description="The ID of this material, used as a universal reference across property documents." "This comes in the form: mp-******.", ) normalized_rms_displacement: float | None = Field( None, description="Volume normalized root-mean squared displacement between the structures", ) max_distance_paired_sites: float | None = Field( None, description="Maximum distance between paired sites.", ) class FindMolecule(BaseModel): """ Class defining find molecule return data """ molecule_id: MPculeID | None = Field( None, description="The ID of this molecule, used as a universal reference across property documents.", ) rmsd: float | None = Field( None, description="Root-mean-squared displacement of the molecule compared to a reference", ) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8218923 emmet_core-0.87.2/emmet/core/formula_autocomplete.py0000644000175100017510000000040715240717720022242 0ustar00runnerrunnerfrom pydantic import BaseModel, Field class FormulaAutocomplete(BaseModel): """ Class defining formula autocomplete return data """ formula_pretty: str | None = Field( None, description="Human readable chemical formula.", ) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8218923 emmet_core-0.87.2/emmet/core/grain_boundary.py0000644000175100017510000000552215240717720021022 0ustar00runnerrunnerfrom __future__ import annotations from typing import TYPE_CHECKING from pydantic import BaseModel, Field from emmet.core.io.pymatgen import Structure from emmet.core.types.enums import ValueEnum from emmet.core.types.pymatgen_types.grain_boundary_adapter import GrainBoundaryType from emmet.core.types.pymatgen_types.structure_adapter import StructureType from emmet.core.types.typing import DateTimeType, MaterialIdentifierType if TYPE_CHECKING: from typing import Any from typing_extensions import Self class GBTypeEnum(ValueEnum): """ Grain boundary types """ tilt = "tilt" twist = "twist" class GrainBoundaryDoc(BaseModel): """ Grain boundary energies, work of separation... """ material_id: MaterialIdentifierType | None = Field( None, description="The Materials Project ID of the material. This comes in the form: mp-******.", ) sigma: int | None = Field( None, description="Sigma value of the boundary.", ) type: GBTypeEnum | None = Field( None, description="Grain boundary type.", ) rotation_axis: list[int] | None = Field( None, description="Rotation axis.", ) gb_plane: list[int] | None = Field( None, description="Grain boundary plane.", ) rotation_angle: float | None = Field( None, description="Rotation angle in degrees.", ) gb_energy: float | None = Field( None, description="Grain boundary energy in J/m^2.", ) initial_structure: GrainBoundaryType | None = Field( None, description="Initial grain boundary structure." ) final_structure: GrainBoundaryType | None = Field( None, description="Final grain boundary structure." ) pretty_formula: str | None = Field( None, description="Reduced formula of the material." ) w_sep: float | None = Field(None, description="Work of separation in J/m^2.") structure: StructureType | None = Field(None, description="Structure.") chemsys: str | None = Field( None, description="Dash-delimited string of elements in the material." ) last_updated: DateTimeType = Field( description="Timestamp for the most recent calculation for this Material document.", ) @classmethod def _migrate_schema(cls, config: dict[str, Any]) -> Self: """Pipe legacy CIF data into a pymatgen structure.""" if isinstance(cif_str := config.pop("cif", None), str) and not config.get( "structure" ): config["structure"] = Structure.from_str(cif_str, fmt="cif") return cls(**config) @property def cif(self) -> str | None: """Support accessing legacy CIF from structure field.""" if self.structure: return self.structure.to(fmt="cif") return None ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011028.9706476 emmet_core-0.87.2/emmet/core/io/0000755000175100017510000000000015240717725016055 5ustar00runnerrunner././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8218923 emmet_core-0.87.2/emmet/core/io/__init__.py0000644000175100017510000000005615240717720020162 0ustar00runnerrunner"""Define I/O layers to external packages.""" ././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1787011023.822149 emmet_core-0.87.2/emmet/core/io/pymatgen.py0000644000175100017510000002055015240717720020250 0ustar00runnerrunnerfrom __future__ import annotations from importlib import import_module from typing import TYPE_CHECKING if TYPE_CHECKING: from collections.abc import Sequence from typing import Any _BASE_PACKAGE_NAME = "pymatgen" _core_class_map: dict[str, str] = { "__version__": "core", "Element": "core.periodic_table", "Composition": "core.composition", "CompositionError": "core.composition", "formula_double_format": "util.string", "htmlify": "util.string", "latexify": "util.string", "latexify_spacegroup": "util.string", "unicodeify_spacegroup": "util.string", "unicodeify": "util.string", "Lattice": "core.lattice", "Specie": "core.periodic_table", "Species": "core.periodic_table", "DummySpecies": "core.periodic_table", "get_el_sp": "core.periodic_table", "PeriodicSite": "core.sites", "IStructure": "core.structure", "Structure": "core.structure", "StructureGraph": "core.graphs", "Molecule": "core.structure", "MoleculeGraph": "core.graphs", "BVAnalyzer": "core.bond_valence", "StructureMatcher": "core.structure_matcher", "ElementComparator": "core.structure_matcher", "AbstractComparator": "core.structure_matcher", "DeformStructureTransformation": "transformations.standard_transformations", "MoleculeMatcher": "core.molecule_matcher", "Trajectory": "core.trajectory", "SpacegroupAnalyzer": "symmetry.analyzer", "PointGroupAnalyzer": "symmetry.analyzer", "SymmetryUndeterminedError": "symmetry.analyzer", "Tensor": "core.tensors", "TensorMapping": "core.tensors", "SYMM_DATA": "symmetry.groups", "CifBlock": "io.cif", "CifParser": "io.cif", "HighSymmKpath": "symmetry.bandstructure", "Kpoint": "electronic_structure.bandstructure", "BandStructure": "electronic_structure.bandstructure", "BandStructureSymmLine": "electronic_structure.bandstructure", "Dos": "electronic_structure.dos", "CompleteDos": "electronic_structure.dos", "Spin": "electronic_structure.core", "Orbital": "electronic_structure.core", "OrbitalType": "electronic_structure.core", "PhononBandStructureSymmLine": "phonon.bandstructure", "PhononDos": "phonon.dos", "CompletePhononDos": "phonon.dos", "PhaseDiagram": "analysis.phase_diagram", "PhaseDiagramError": "analysis.phase_diagram", "IRDielectricTensor": "phonon.ir_spectra", "ComputedEntry": "core.entries", "ComputedStructureEntry": "core.entries", "Incar": "io.vasp.inputs", "Kpoints": "io.vasp.inputs", "Poscar": "io.vasp.inputs", "PotcarSingle": "io.vasp", "Potcar": "io.vasp.inputs", "POTCAR_STATS_PATH": "io.vasp.inputs", "Vasprun": "io.vasp.outputs", "BSVasprun": "io.vasp.outputs", "Locpot": "io.vasp.outputs", "Elfcar": "io.vasp.outputs", "Oszicar": "io.vasp.outputs", "Outcar": "io.vasp.outputs", "VolumetricData": "io.vasp.outputs", "Chgcar": "io.vasp.outputs", "VaspInputSet": "io.vasp.sets", "MPStaticSet": "io.vasp.sets", "BalancedReaction": "analysis.reaction_calculator", "GrainBoundary": "core.interface", "oxide_type": "core.structure_analyzer", "QCInput": "io.qchem.inputs", "QCOutput": "io.qchem.outputs", "BaseVolumetricData": "io.common", "bader_analysis_from_path": "command_line.bader_caller", "ChargemolAnalysis": "command_line.chargemol_caller", "BabelMolAdaptor": "io.babel", "NearNeighbors": "core.local_env", "CrystalNN": "core.local_env", "OpenBabelNN": "core.local_env", "metal_edge_extender": "core.local_env", "LocalStructOrderParams": "core.local_env", "CN_OPT_PARAMS": "core.local_env", "get_angle": "util.coord", "weisfeiler_lehman_graph_hash": "util.graph_hashing", "Cohp": "electronic_structure.cohp", "CompleteCohp": "electronic_structure.cohp", "LobsterCompleteDos": "electronic_structure.dos", "TransformedStructure": "alchemy.materials", "Author": "util.provenance", "HistoryNode": "util.provenance", "StructureNL": "util.provenance", "STRUCTURES_DIR": "util.testing", } """Objects which are defined in pymatgen-core. Can use aliases to avoid name collisions.""" _non_core_class_map: dict[str, str] = { "ElasticTensor": "analysis.elasticity", "ElasticTensorExpansion": "analysis.elasticity", "Deformation": "analysis.elasticity.strain", "Strain": "analysis.elasticity.strain", "Stress": "analysis.elasticity.stress", "AbstractElectrode": "apps.battery.battery_abc", "ConversionElectrode": "apps.battery.conversion_battery", "ConversionVoltagePair": "apps.battery.conversion_battery", "InsertionElectrode": "apps.battery.insertion_battery", "InsertionVoltagePair": "apps.battery.insertion_battery", "AflowPrototypeMatcher": "analysis.prototypes", "CollinearMagneticStructureAnalyzer": "analysis.magnetism", "Ordering": "analysis.magnetism.analyzer", "Compatibility": "analysis.compatibility", "MaterialsProject2020Compatibility": "analysis.compatibility", "MaterialsProjectAqueousCompatibility": "analysis.compatibility", "DiffractionPattern": "analysis.diffraction.xrd", "EOS": "analysis.eos", "EOSError": "analysis.eos", "AllCoordinationGeometries": "analysis.chemenv.coordination_environments.coordination_geometries", "LightStructureEnvironments": "analysis.chemenv.coordination_environments.structure_environments", "SimplestChemenvStrategy": "analysis.chemenv.coordination_environments.chemenv_strategies", "LocalGeometryFinder": "analysis.chemenv.coordination_environments.coordination_geometry_finder", "MaterialsProjectDFTMixingScheme": "entries.mixing_scheme", "PiezoTensor": "analysis.piezo", "WAVELENGTHS": "analysis.diffraction.xrd", "XRDCalculator": "analysis.diffraction.xrd", "XAS": "analysis.xas.spectrum", "site_weighted_spectrum": "analysis.xas.spectrum", "get_structure_components": "analysis.dimensionality", } """Objects which are defined only in pymatgen, and not pymatgen-core. Can use aliases to avoid name collisions.""" _add_on_class_map: dict[str, str] = { # pymatgen-analysis-alloys add-on "AlloyMember": "analysis.alloys.core", "AlloyPair": "analysis.alloys.core", "AlloySystem": "analysis.alloys.core", # pymatgen-analysis-diffusion add-on "MigrationGraph": "analysis.diffusion.neb.full_path_mapper", "add_edge_data_from_sc": "analysis.diffusion.utils.edge_data_from_sc", "get_sc_fromstruct": "analysis.diffusion.utils.supercells", "get_start_end_structures": "analysis.diffusion.utils.supercells", # pymatgen-io-validation add-on "REQUIRED_VASP_FILES": "io.validation.validation", "VaspValidator": "io.validation.validation", "LightOutcar": "io.validation.common", "LightVasprun": "io.validation.common", "PotcarSummaryStats": "io.validation.common", "VaspFiles": "io.validation.common", "VaspInputSafe": "io.validation.common", "get_kpoint_divisions_from_kspacing": "io.validation.check_kpoints_kspacing", # pymatgen-analysis-defects add-on "Defect": "analysis.defects.core", # pymatgen-io-lobster add-on "Bandoverlaps": "io.lobster", "Charge": "io.lobster", "Doscar": "io.lobster", "Grosspop": "io.lobster", "Icohplist": "io.lobster", "Lobsterin": "io.lobster", "Lobsterout": "io.lobster", "MadelungEnergies": "io.lobster", "SitePotential": "io.lobster", } """Objects which are defined in pymatgen add-ons. Can use aliases to avoid name collisions.""" _class_map: dict[str, str] = { **_core_class_map, **_non_core_class_map, **_add_on_class_map, } """Dict of object names to their base import string. Can use aliases to avoid name collisions.""" _name_collision_aliases: dict[str, str] = {"BaseVolumetricData": "VolumetricData"} """Aliases for classes to avoid name collisions.""" def __getattr__(name: str) -> Any: """Lazily load objects from pymatgen.""" if name in _class_map: return getattr( import_module(f"{_BASE_PACKAGE_NAME}.{_class_map[name]}"), _name_collision_aliases.get(name, name), ) raise AttributeError(f"module {__name__!r} has no attribute {name!r}") def __dir__() -> Sequence[str]: """List available interfaces to pymatgen objects.""" return sorted( ".".join( (_BASE_PACKAGE_NAME, base_import, _name_collision_aliases.get(name, name)) ) for name, base_import in _class_map.items() ) ././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1787011023.822149 emmet_core-0.87.2/emmet/core/lobster.py0000644000175100017510000013165415240717720017477 0ustar00runnerrunner"""Module defining lobster document schemas.""" from __future__ import annotations import logging import time import json import orjson from pathlib import Path from typing import TYPE_CHECKING import numpy as np from monty.os.path import zpath from monty.io import zopen from monty.dev import requires from monty.json import MontyDecoder, jsanitize from pydantic import BaseModel, Field from emmet.core.io.pymatgen import ( Structure, Cohp, CompleteCohp, LobsterCompleteDos, Bandoverlaps, Charge, Doscar, Grosspop, Icohplist, Lobsterin, Lobsterout, MadelungEnergies, SitePotential, ) from typing_extensions import Self from emmet.core.structure import StructureMetadata from emmet.core.utils import arrow_incompatible from emmet.core.types.typing import DateTimeType try: from lobsterpy.coxx.analyze import Analysis from lobsterpy.coxx.describe import Description from lobsterpy.quality.analyze import LobsterCalcQuality except ImportError as exc: raise ImportError( "`pip install lobsterpy` to use the features in `emmet.core.lobster`" ) from exc if TYPE_CHECKING: from typing import Any, Literal logger = logging.getLogger(__name__) def aggregate_paths( dir_name: Path, ) -> dict[str, Path]: paths = {} for p in dir_name.glob("*.lobster*"): paths[p.name.split(".lobster")[0]] = p for file_name in ( "POTCAR", "CONTCAR", "vasprun.xml", "lobsterin", "lobsterout", ): paths[file_name] = Path(zpath(str(dir_name / file_name))) return paths @arrow_incompatible class LobsteroutModel(BaseModel): """Definition of computational settings from the LOBSTER computation.""" restart_from_projection: bool | None = Field( None, description="Bool indicating if the run has been restarted from a projection", ) lobster_version: str | None = Field(None, description="Lobster version") threads: int | None = Field( None, description="Number of threads that Lobster ran on" ) dft_program: str | None = Field( None, description="DFT program was used for this run" ) charge_spilling: list[float] = Field(description="Absolute charge spilling") total_spilling: list[float] = Field(description="Total spilling") elements: list[str] = Field(description="Elements in structure") basis_type: list[str] = Field(description="Basis set used in Lobster") basis_functions: list[list[str]] = Field(description="basis_functions") timing: dict[str, dict[str, str]] = Field(description="Dict with infos on timing") warning_lines: list | None = Field(None, description="Warnings") info_orthonormalization: list | None = Field( None, description="additional information on orthonormalization" ) info_lines: list | None = Field( None, description="list of strings with additional info lines" ) has_doscar: bool | None = Field( None, description="Bool indicating if DOSCAR is present." ) has_doscar_lso: bool | None = Field( None, description="Bool indicating if DOSCAR.LSO is present." ) has_cohpcar: bool | None = Field( None, description="Bool indicating if COHPCAR is present." ) has_coopcar: bool | None = Field( None, description="Bool indicating if COOPCAR is present." ) has_cobicar: bool | None = Field( None, description="Bool indicating if COBICAR is present." ) has_charge: bool | None = Field( None, description="Bool indicating if CHARGE is present." ) has_madelung: bool | None = Field( None, description="Bool indicating if Site Potentials and Madelung file is present.", ) has_projection: bool | None = Field( None, description="Bool indicating if projection file is present." ) has_bandoverlaps: bool | None = Field( None, description="Bool indicating if BANDOVERLAPS file is present" ) has_fatbands: bool | None = Field( None, description="Bool indicating if Fatbands are present." ) has_grosspopulation: bool | None = Field( None, description="Bool indicating if GROSSPOP file is present." ) has_density_of_energies: bool | None = Field( None, description="Bool indicating if DensityofEnergies is present" ) class LobsterinModel(BaseModel): """Definition of input settings for the LOBSTER computation.""" cohp_start_energy: float = Field(description="Start energy for COHP computation") cohp_end_energy: float = Field(description="End energy for COHP computation") gaussian_smearing_width: float | None = Field( None, description="Set the smearing width in eV,default is 0.2 (eV)", ) use_decimal_places: int | None = Field( None, description="Set the decimal places to print in output files, default is 5", ) cohp_steps: float | None = Field( None, description="Number steps in COHPCAR; similar to NEDOS of VASP", ) basis_set: str = Field(description="basis set of computation") cohp_generator: str = Field( description="Build the list of atom pairs to be analyzed using given distance", ) save_projection_to_file: bool | None = Field( None, description="Save the results of projections" ) lso_dos: bool | None = Field( None, description="Writes DOS output from the orthonormalized LCAO basis" ) basis_functions: list[str] = Field( description="Specify the basis functions for element", ) class Bonding(BaseModel): """Model describing bonding field of BondsInfo.""" integral: float | None = Field( None, description="Integral considering only bonding contributions from COHPs" ) perc: float | None = Field(None, description="Percentage of bonding contribution") class BondsInfo(BaseModel): """Model describing bonds field of SiteInfo.""" ICOHP_mean: str = Field(description="Mean of ICOHPs of relevant bonds") ICOHP_sum: str = Field(description="Sum of ICOHPs of relevant bonds") has_antibdg_states_below_Efermi: bool = Field( # noqa: N815 description="Indicates if antibonding interactions below efermi are detected", ) number_of_bonds: int = Field( description="Number of bonds considered in the analysis" ) bonding: Bonding = Field(description="Model describing bonding contributions") antibonding: Bonding = Field( description="Model describing anti-bonding contributions" ) class SiteInfo(BaseModel): """Outer model describing sites field of Sites model.""" env: str = Field( description="The coordination environment identified from " "the LobsterPy analysis", ) bonds: dict[str, BondsInfo] = Field( description="A dictionary with keys as atomic-specie as key " "and BondsInfo model as values", ) ion: str = Field(description="Ion to which the atom is bonded") charge: float = Field(description="Mulliken charge of the atom") relevant_bonds: list[str] = Field( description="List of bond labels from the LOBSTER files i.e. for e.g. " " from ICOHPLIST.lobster/ COHPCAR.lobster", ) class Sites(BaseModel): """Model describing, sites field of CondensedBondingAnalysis.""" sites: dict[int, SiteInfo] = Field( description="A dictionary with site index as keys and SiteInfo model as values", ) @arrow_incompatible class CohpPlotData(BaseModel): """Model describing the cohp_plot_data field of CondensedBondingAnalysis.""" data: dict[str, Cohp] = Field( description="A dictionary with plot labels from LobsterPy " "automatic analysis as keys and Cohp objects as values", ) class DictIons(BaseModel): """Model describing final_dict_ions field of CondensedBondingAnalysis.""" data: dict[str, dict[str, int]] = Field( description="Dict consisting information on environments of cations " "and counts for them", ) # TODO extricate this from individual dicts @arrow_incompatible # union float | bool type class DictBonds(BaseModel): """Model describing final_dict_bonds field of CondensedBondingAnalysis.""" data: dict[str, dict[str, float | bool]] = Field( description="Dict consisting information on ICOHPs per bond type" ) @arrow_incompatible class CondensedBondingAnalysis(BaseModel): """Definition of condensed bonding analysis data from LobsterPy ICOHP.""" formula: str = Field(description="Pretty formula of the structure") max_considered_bond_length: float = Field( description="Maximum bond length considered in bonding analysis" ) # TODO: limit_icohp is only str for float("inf"), handled by bson limit_icohp: list[str | float] = Field( description="ICOHP range considered in co-ordination environment analysis" ) number_of_considered_ions: int = Field( description="number of ions detected based on Mulliken/Löwdin Charges" ) sites: Sites = Field( description="Bonding information at inequivalent sites in the structure", ) type_charges: str = Field( description="Charge type considered for assigning valences in bonding analysis" ) cutoff_icohp: float = Field( description="Percent limiting the ICOHP values to be considered" " relative to strongest ICOHP", ) summed_spins: bool = Field( description="Bool that states if the spin channels in the " "cohp_plot_data are summed.", ) start: float | None = Field( None, description="Sets the lower limit of energy relative to Fermi for evaluating" " bonding/anti-bonding percentages in the bond" " if set to None, all energies up-to the Fermi is considered", ) cohp_plot_data: CohpPlotData = Field( description="Plotting data for the relevant bonds from LobsterPy analysis", ) which_bonds: str = Field( description="Specifies types of bond considered in LobsterPy analysis", ) final_dict_bonds: DictBonds = Field( description="Dict consisting information on ICOHPs per bond type", ) final_dict_ions: DictIons = Field( description="Model that describes final_dict_ions field", ) run_time: float = Field( description="Time needed to run Lobsterpy condensed bonding analysis" ) @classmethod def from_directory( cls, dir_name: str | Path, save_cohp_plots: bool = False, lobsterpy_kwargs: dict | None = None, plot_kwargs: dict | None = None, which_bonds: Literal["cation-anion", "all"] = "all", ) -> tuple: """Create a task document from a directory containing LOBSTER files. Parameters ---------- dir_name : path or str The path to the folder containing the calculation outputs. save_cohp_plots : bool. Bool to indicate whether automatic cohp plots and jsons from lobsterpy will be generated. lobsterpy_kwargs : dict. kwargs to change default lobsterpy automatic analysis parameters. plot_kwargs : dict. kwargs to change plotting options in lobsterpy. which_bonds: str. mode for condensed bonding analysis: "cation-anion" and "all". """ plot_kwargs = plot_kwargs or {} lobsterpy_kwargs = lobsterpy_kwargs or {} dir_name = Path(dir_name) file_paths = aggregate_paths(dir_name) # Update lobsterpy analysis parameters with user supplied parameters lobsterpy_kwargs_updated = { "are_cobis": False, "are_coops": False, "cutoff_icohp": 0.10, "noise_cutoff": 0.1, "orbital_cutoff": 0.05, "orbital_resolved": False, "start": None, "summed_spins": False, # we will always use spin polarization here "type_charge": "Mulliken", **lobsterpy_kwargs, } try: start = time.time() analyse = Analysis.from_files( structure_path=file_paths["CONTCAR"], icoxxlist_path=file_paths["ICOHPLIST"], coxxcar_path=file_paths["COHPCAR"], charge_path=file_paths["CHARGE"], which_bonds=which_bonds, **lobsterpy_kwargs_updated, ) cba_run_time = time.time() - start # initialize lobsterpy condensed bonding analysis cba = analyse.condensed_bonding_analysis cba_cohp_plot_data = {} # Initialize dict to store plot data seq_cohps = analyse.seq_cohps seq_labels_cohps = analyse.seq_labels_cohps seq_ineq_cations = analyse.seq_ineq_ions struct = analyse.structure for ication, labels, cohps in zip( seq_ineq_cations, seq_labels_cohps, seq_cohps, strict=True ): label_str = f"{struct[ication].specie!s}{ication + 1!s}: " for label, cohp in zip(labels, cohps, strict=True): if label is not None: cba_cohp_plot_data[label_str + label] = cohp describe = Description(analysis_object=analyse) limit_icohp_val = list(cba["limit_icohp"]) # TODO replace this with bson rep of float("inf") _replace_inf_values(limit_icohp_val) condensed_bonding_analysis = CondensedBondingAnalysis( # type: ignore[call-arg] formula=cba["formula"], max_considered_bond_length=cba["max_considered_bond_length"], limit_icohp=limit_icohp_val, number_of_considered_ions=cba["number_of_considered_ions"], sites=Sites(**cba), type_charges=analyse.type_charge, cohp_plot_data=CohpPlotData(data=cba_cohp_plot_data), cutoff_icohp=analyse.cutoff_icohp, summed_spins=lobsterpy_kwargs_updated.get("summed_spins", False), which_bonds=analyse.which_bonds, final_dict_bonds=DictBonds(data=analyse.final_dict_bonds), final_dict_ions=DictIons(data=analyse.final_dict_ions), run_time=cba_run_time, ) if save_cohp_plots: describe.plot_cohps( save=True, filename=dir_name / f"automatic_cohp_plots_{which_bonds}.pdf", hide=True, **plot_kwargs, ) filename = dir_name / f"condensed_bonding_analysis_{which_bonds}" with open(f"{filename}.json", "w") as fp: # fp.write(orjson.dumps(analyse.condensed_bonding_analysis)) fp.write(json.dumps(analyse.condensed_bonding_analysis)) with open(f"{filename}.txt", "w") as fp: fp.write("\n".join(describe.text)) # Read in strongest icohp values sb = _identify_strongest_bonds( analyse=analyse, **{ f"{k.lower()}_path": file_paths.get(k, Path(k)) for k in ("ICOBILIST", "ICOHPLIST", "ICOOPLIST") }, ) except ValueError: return None, None, None else: return condensed_bonding_analysis, describe, sb class DosComparisons(BaseModel): """Model describing the DOS comparisons field in the CalcQualitySummary model.""" tanimoto_orb_s: float | None = Field( None, description="Tanimoto similarity index between s orbital of " "VASP and LOBSTER DOS", ) tanimoto_orb_p: float | None = Field( None, description="Tanimoto similarity index between p orbital of " "VASP and LOBSTER DOS", ) tanimoto_orb_d: float | None = Field( None, description="Tanimoto similarity index between d orbital of " "VASP and LOBSTER DOS", ) tanimoto_orb_f: float | None = Field( None, description="Tanimoto similarity index between f orbital of " "VASP and LOBSTER DOS", ) tanimoto_summed: float | None = Field( None, description="Tanimoto similarity index for summed PDOS between " "VASP and LOBSTER", ) e_range: list[float | None] = Field( description="Energy range used for evaluating the Tanimoto similarity index" ) n_bins: int | None = Field( None, description="Number of bins used for discretizing the VASP and LOBSTER PDOS" "(Affects the Tanimoto similarity index)", ) class ChargeComparisons(BaseModel): """Model describing the charges field in the CalcQualitySummary model.""" bva_mulliken_agree: bool | None = Field( None, description="Bool indicating whether atoms classification as cation " "or anion based on Mulliken charge signs of LOBSTER " "agree with BVA analysis", ) bva_loewdin_agree: bool | None = Field( None, description="Bool indicating whether atoms classification as cations " "or anions based on Loewdin charge signs of LOBSTER " "agree with BVA analysis", ) class BandOverlapsComparisons(BaseModel): """Model describing the Band overlaps field in the CalcQualitySummary model.""" file_exists: bool = Field( description="Boolean indicating whether the bandOverlaps.lobster " "file is generated during the LOBSTER run", ) limit_maxDeviation: float | None = Field( # noqa: N815 None, description="Limit set for maximal deviation in pymatgen parser", ) has_good_quality_maxDeviation: bool | None = Field( # noqa: N815 None, description="Boolean indicating whether the deviation at each k-point " "is within the threshold set using limit_maxDeviation " "for analyzing the bandOverlaps.lobster file data", ) max_deviation: float | None = Field( None, description="Maximum deviation from ideal identity matrix from the observed in " "the bandOverlaps.lobster file", ) percent_kpoints_abv_limit: float | None = Field( None, description="Percent of k-points that show deviations above " "the limit_maxDeviation threshold set in pymatgen parser.", ) class ChargeSpilling(BaseModel): """Model describing the Charge spilling field in the CalcQualitySummary model.""" abs_charge_spilling: float = Field( description="Absolute charge spilling value from the LOBSTER calculation.", ) abs_total_spilling: float = Field( description="Total charge spilling percent from the LOBSTER calculation.", ) @arrow_incompatible class CalcQualitySummary(BaseModel): """Model describing the calculation quality of lobster run.""" minimal_basis: bool = Field( description="Denotes whether the calculation used the minimal basis for the " "LOBSTER computation", ) charge_spilling: ChargeSpilling = Field( description="Model describing the charge spilling from the LOBSTER runs", ) charge_comparisons: ChargeComparisons | None = Field( None, description="Model describing the charge sign comparison results", ) band_overlaps_analysis: BandOverlapsComparisons | None = Field( None, description="Model describing the band overlap file analysis results", ) dos_comparisons: DosComparisons | None = Field( None, description="Model describing the VASP and LOBSTER PDOS comparisons results", ) @classmethod @requires( Analysis, "lobsterpy must be installed to create an CalcQualitySummary from a directory.", ) def from_directory( cls, dir_name: str | Path, calc_quality_kwargs: dict | None = None, ) -> Self: """Make a LOBSTER calculation quality summary from directory with LOBSTER files. Parameters ---------- dir_name : path or str The path to the folder containing the calculation outputs. calc_quality_kwargs : dict kwargs to change calc quality analysis options in lobsterpy. Returns ------- CalcQualitySummary A task document summarizing quality of the lobster calculation. """ dir_name = Path(dir_name) calc_quality_kwargs = calc_quality_kwargs or {} file_paths = aggregate_paths(dir_name) doscar_path = file_paths.get("DOSCAR.LSO") or file_paths.get("DOSCAR") # Update calc quality kwargs supplied by user calc_quality_kwargs_updated = { "e_range": [-20, 0], "dos_comparison": True, "n_bins": 256, "bva_comp": True, **calc_quality_kwargs, } lob_calc_qual = LobsterCalcQuality.from_files( poscar=file_paths["CONTCAR"], charge=file_paths["CHARGE"], doscar=doscar_path, bandoverlaps=file_paths.get("bandOverlaps"), vasprun=file_paths["vasprun.xml"], lobsterin=file_paths["lobsterin"], lobsterout=file_paths["lobsterout"], ) calc_quality_dict = lob_calc_qual.get_calculation_quality_summary( **calc_quality_kwargs_updated, ) return cls(**calc_quality_dict) @arrow_incompatible class StrongestBonds(BaseModel): """Strongest bonds extracted from ICOHPLIST/ICOOPLIST/ICOBILIST from LOBSTER. LobsterPy is used for the extraction. """ which_bonds: str | None = Field( None, description="Denotes whether the information " "is for cation-anion pairs or all bonds", ) strongest_bonds_icoop: dict | None = Field( None, description="Dict with infos on bond strength and bond length based on ICOOP.", ) strongest_bonds_icohp: dict | None = Field( None, description="Dict with infos on bond strength and bond length based on ICOHP.", ) strongest_bonds_icobi: dict | None = Field( None, description="Dict with infos on bond strength and bond length based on ICOBI.", ) @arrow_incompatible class LobsterTaskDocument(StructureMetadata): """Definition of LOBSTER task document.""" structure: Structure = Field(description="The structure used in this task") dir_name: str = Field(description="The directory for this Lobster task") last_updated: DateTimeType charges: Charge | None = Field( None, description="pymatgen Charge obj. Contains atomic charges based on Mulliken " "and Loewdin charge analysis", ) lobsterout: LobsteroutModel = Field(description="Lobster out data") lobsterin: LobsterinModel = Field(description="Lobster calculation inputs") lobsterpy_data: CondensedBondingAnalysis | None = Field( None, description="Model describing the LobsterPy data" ) lobsterpy_text: str | None = Field( None, description="Stores LobsterPy automatic analysis summary text" ) calc_quality_summary: CalcQualitySummary | None = Field( None, description="Model summarizing results of lobster runs like charge spillings, " "band overlaps, DOS comparisons with VASP runs and quantum chemical LOBSTER " "charge sign comparisons with BVA method", ) calc_quality_text: str | None = Field( None, description="Stores calculation quality analysis summary text" ) strongest_bonds: StrongestBonds | None = Field( None, description="Describes the strongest cation-anion ICOOP, ICOBI and ICOHP bonds", ) lobsterpy_data_cation_anion: CondensedBondingAnalysis | None = Field( None, description="Model describing the LobsterPy data" ) lobsterpy_text_cation_anion: str | None = Field( None, description="Stores LobsterPy automatic analysis summary text", ) strongest_bonds_cation_anion: StrongestBonds | None = Field( None, description="Describes the strongest cation-anion ICOOP, ICOBI and ICOHP bonds", ) dos: LobsterCompleteDos | None = Field( None, description="pymatgen pymatgen.io.lobster.Doscar.completedos data" ) lso_dos: LobsterCompleteDos | None = Field( None, description="pymatgen pymatgen.io.lobster.Doscar.completedos data" ) madelung_energies: MadelungEnergies | None = Field( None, description="pymatgen Madelung energies obj. Contains madelung energies" "based on Mulliken and Loewdin charges", ) site_potentials: SitePotential | None = Field( None, description="pymatgen Site potentials obj. Contains site potentials " "based on Mulliken and Loewdin charges", ) gross_populations: Grosspop | None = Field( None, description="pymatgen Grosspopulations obj. Contains gross populations " " based on Mulliken and Loewdin charges ", ) band_overlaps: Bandoverlaps | None = Field( None, description="pymatgen Bandoverlaps obj for each k-point from" " bandOverlaps.lobster file if it exists", ) cohp_data: CompleteCohp | None = Field( None, description="pymatgen CompleteCohp object with COHP data" ) coop_data: CompleteCohp | None = Field( None, description="pymatgen CompleteCohp object with COOP data" ) cobi_data: CompleteCohp | None = Field( None, description="pymatgen CompleteCohp object with COBI data" ) icohp_list: Icohplist | None = Field( None, description="pymatgen Icohplist object with ICOHP data" ) icoop_list: Icohplist | None = Field( None, description="pymatgen Icohplist object with ICOOP data" ) icobi_list: Icohplist | None = Field( None, description="pymatgen Icohplist object with ICOBI data" ) @classmethod @requires( Analysis, "LobsterTaskDocument.from_directory requires lobsterpy.", ) def from_directory( cls, dir_name: str | Path, additional_fields: dict | None = None, add_coxxcar_to_task_document: bool = False, analyze_outputs: bool = True, calc_quality_kwargs: dict | None = None, lobsterpy_kwargs: dict | None = None, plot_kwargs: dict | None = None, store_lso_dos: bool = False, save_cohp_plots: bool = True, save_cba_jsons: str | None = "cba.jsonl.gz", save_computational_data_jsons: str | None = "computational_data.jsonl.gz", ) -> Self: """Create a task document from a directory containing LOBSTER files. Parameters ---------- dir_name : path or str. The path to the folder containing the calculation outputs. additional_fields : dict. Dictionary of additional fields to add to output document. add_coxxcar_to_task_document : bool. Bool to indicate whether to add COHPCAR, COOPCAR, COBICAR data objects to the task document. analyze_outputs: bool. If True, will enable lobsterpy analysis. calc_quality_kwargs : dict. kwargs to change calc quality summary options in lobsterpy. lobsterpy_kwargs : dict. kwargs to change default lobsterpy automatic analysis parameters. plot_kwargs : dict. kwargs to change plotting options in lobsterpy. store_lso_dos : bool. Whether to store the LSO DOS. save_cohp_plots : bool. Bool to indicate whether automatic cohp plots and jsons from lobsterpy will be generated. save_cba_jsons : str | None = "cba.jsonl.gz" If a str, the name of the JSON lines file to save condensed bonding analysis, consisting of outputs from lobsterpy analysis, calculation quality summary, lobster dos, charges and madelung energies save_computational_data_jsons : str | None = "computational_data.jsonl.gz" Name of the JSON lines file to save computational data to. Returns ------- LobsterTaskDocument A task document for the lobster calculation. """ additional_fields = {} if additional_fields is None else additional_fields dir_name = Path(dir_name) # Read in lobsterout and lobsterin file_paths = aggregate_paths(dir_name) lobsterout_doc = Lobsterout(file_paths["lobsterout"].as_posix()).get_doc() lobster_out = LobsteroutModel(**lobsterout_doc) lobster_in_dict = Lobsterin.from_file(file_paths["lobsterin"].as_posix()) # convert keys to snake case lobster_in = LobsterinModel( **{ # type: ignore[arg-type] k: lobster_in_dict.get(k.replace("_", "")) for k in LobsterinModel.model_fields } ) # Do automatic bonding analysis with LobsterPy struct = Structure.from_file(file_paths["CONTCAR"].as_posix()) # will perform two condensed bonding analysis computations condensed_bonding_analysis = None condensed_bonding_analysis_ionic = None sb_all = None sb_ionic = None calc_quality_summary = None calc_quality_text = None describe = None describe_ionic = None if analyze_outputs: if all(file_paths.get(k) for k in ("ICOHPLIST", "COHPCAR", "CHARGE")): ( condensed_bonding_analysis, describe, sb_all, ) = CondensedBondingAnalysis.from_directory( dir_name, save_cohp_plots=save_cohp_plots, plot_kwargs=plot_kwargs, lobsterpy_kwargs=lobsterpy_kwargs, which_bonds="all", ) ( condensed_bonding_analysis_ionic, describe_ionic, sb_ionic, ) = CondensedBondingAnalysis.from_directory( dir_name, save_cohp_plots=save_cohp_plots, plot_kwargs=plot_kwargs, lobsterpy_kwargs=lobsterpy_kwargs, which_bonds="cation-anion", ) # Get lobster calculation quality summary data calc_quality_summary = CalcQualitySummary.from_directory( dir_name, calc_quality_kwargs=calc_quality_kwargs, ) calc_quality_text = LobsterCalcQuality.describe( calc_quality_summary.model_dump() ) # Read in COHPCAR, COBICAR, COOPCAR cohp_data = { "cohp_data": ( CompleteCohp.from_file( fmt="LOBSTER", structure_file=file_paths["CONTCAR"].as_posix(), filename=file_paths["COHPCAR"].as_posix(), are_coops=False, are_cobis=False, ) if file_paths.get("COHPCAR") else None ), "coop_data": ( CompleteCohp.from_file( fmt="LOBSTER", structure_file=file_paths["CONTCAR"].as_posix(), filename=file_paths["COOPCAR"].as_posix(), are_coops=True, are_cobis=False, ) if file_paths.get("COOPCAR") else None ), "cobi_data": ( CompleteCohp.from_file( fmt="LOBSTER", structure_file=file_paths["CONTCAR"].as_posix(), filename=file_paths["COBICAR"].as_posix(), are_coops=False, are_cobis=True, ) if file_paths.get("COBICAR") else None ), } doc = cls.from_structure( structure=struct, meta_structure=struct, dir_name=str(dir_name), lobsterin=lobster_in, lobsterout=lobster_out, # include additional fields for cation-anion lobsterpy_data=condensed_bonding_analysis, lobsterpy_text=" ".join(describe.text) if describe is not None else None, strongest_bonds=sb_all, lobsterpy_data_cation_anion=condensed_bonding_analysis_ionic, lobsterpy_text_cation_anion=( " ".join(describe_ionic.text) if describe_ionic is not None else None ), strongest_bonds_cation_anion=sb_ionic, calc_quality_summary=calc_quality_summary, calc_quality_text=( " ".join(calc_quality_text) if calc_quality_text is not None else None ), dos=( Doscar( doscar=file_paths["DOSCAR"].as_posix(), structure_file=file_paths["CONTCAR"].as_posix(), ).completedos if file_paths.get("DOSCAR") else None ), lso_dos=( Doscar( doscar=file_paths["DOSCAR.LSO"].as_posix(), structure_file=file_paths["CONTCAR"].as_posix(), ).completedos if (store_lso_dos and file_paths.get("DOSCAR.LSO")) else None ), charges=( Charge(filename=file_paths["CHARGE"].as_posix()) if file_paths.get("CHARGE") else None ), madelung_energies=( MadelungEnergies(filename=file_paths["MadelungEnergies"].as_posix()) if file_paths.get("MadelungEnergies") else None ), site_potentials=( SitePotential(filename=file_paths["SitePotentials"].as_posix()) if file_paths.get("SitePotentials") else None ), gross_populations=( Grosspop(filename=file_paths["GROSSPOP"].as_posix()) if file_paths.get("GROSSPOP") else None ), band_overlaps=( Bandoverlaps(filename=file_paths["bandOverlaps"].as_posix()) if file_paths.get("bandOverlaps") else None ), # include additional fields for all bonds **(cohp_data if add_coxxcar_to_task_document else {}), **{ k: Icohplist( filename=file_paths[v].as_posix(), are_coops=(v == "ICOOPLIST"), are_cobis=(v == "ICOBILIST"), ) for k, v in { "icohp_list": "ICOHPLIST", "icoop_list": "ICOOPLIST", "icobi_list": "ICOBILIST", }.items() }, ) if save_cba_jsons and analyze_outputs: if ( doc.lobsterpy_data_cation_anion is not None ): # check if cation-anion analysis failed data: list[dict[str, Any]] = [ { f"{doc.lobsterpy_data_cation_anion.which_bonds.replace('-', '_')}_bonds": { "lobsterpy_data": doc.lobsterpy_data_cation_anion, "lobsterpy_text": [ "".join(doc.lobsterpy_text_cation_anion) ], "strongest_bonds": doc.strongest_bonds_cation_anion, } } ] else: data = [{"cation_anion_bonds": {}}] data.extend( [ { f"{doc.lobsterpy_data.which_bonds}_bonds": { "lobsterpy_data": doc.lobsterpy_data, "lobsterpy_text": ["".join(doc.lobsterpy_text)], "strongest_bonds": doc.strongest_bonds, } }, {"madelung_energies": doc.madelung_energies}, {"charges": doc.charges}, {"calc_quality_summary": doc.calc_quality_summary}, {"calc_quality_text": ["".join(doc.calc_quality_text)]}, {"dos": doc.dos}, {"lso_dos": doc.lso_dos}, ] ) with zopen(dir_name / save_cba_jsons, "wb") as file: # Write the json in iterable format # (Necessary to load large JSON files via ijson) for obj in data: file.write( orjson.dumps( jsanitize( obj, allow_bson=False, strict=True, enum_values=True ) ) + "\n".encode("UTF-8") ) del data if save_computational_data_jsons: fields_to_exclude = [ "nsites", "elements", "nelements", "formula_anonymous", "chemsys", "volume", "density", "density_atomic", "symmetry", ] doc_data = { k: v for k, v in doc.model_dump().items() if k not in fields_to_exclude } # Always add cohp, cobi and coop data to the jsons if files exists for k, v in cohp_data.items(): if doc_data.get(k) is None and v is not None: doc_data[k] = v with zopen( dir_name / save_computational_data_jsons, "wb", ) as file: for k, v in doc_data.items(): # Use monty encoder to automatically convert pymatgen # objects and other data json compatible dict format file.write( orjson.dumps( { k: jsanitize( v, allow_bson=False, strict=True, enum_values=True, ) }, ) + "\n".encode("UTF-8") ) return doc.model_copy(update=additional_fields) def _replace_inf_values(data: dict[Any, Any] | list[Any]) -> None: """ Replace the -inf value in dictionary and with the string representation '-Infinity'. Parameters ---------- data : dict dictionary to recursively iterate over Returns ------- data Dictionary with replaced -inf values. """ if isinstance(data, dict): for key, value in data.items(): if isinstance(value, dict | list): _replace_inf_values( value ) # Recursively process nested dictionaries and lists elif value == float("-inf"): data[key] = "-Infinity" # Replace -inf with a string representation elif isinstance(data, list): for index, item in enumerate(data): if isinstance(item, dict | list): _replace_inf_values( item ) # Recursively process nested dictionaries and lists elif item == float("-inf"): data[index] = "-Infinity" # Replace -inf with a string representation def _identify_strongest_bonds( analyse: Analysis, icobilist_path: Path, icohplist_path: Path, icooplist_path: Path, ) -> StrongestBonds: """ Identify the strongest bonds and convert them into StrongestBonds objects. Parameters ---------- analyse : .Analysis Analysis object from lobsterpy automatic analysis icobilist_path : Path or str Path to ICOBILIST.lobster icohplist_path : Path or str Path to ICOHPLIST.lobster icooplist_path : Path or str Path to ICOOPLIST.lobster Returns ------- StrongestBonds """ data = [ (icohplist_path, False, False, "icohp"), (icobilist_path, True, False, "icobi"), (icooplist_path, False, True, "icoop"), ] model_data = {"which_bonds": analyse.which_bonds} for file, are_cobis, are_coops, prop in data: if file.exists(): icohplist = Icohplist( filename=file, are_cobis=are_cobis, are_coops=are_coops, ) bond_dict = _get_strong_bonds( getattr(icohplist.icohpcollection, "as_dict", dict)(), relevant_bonds=analyse.final_dict_bonds, are_cobis=are_cobis, are_coops=are_coops, ) model_data[f"strongest_bonds_{prop}"] = bond_dict else: model_data[f"strongest_bonds_{prop}"] = {} return StrongestBonds(**model_data) # Don't we have this in pymatgen somewhere? def _get_strong_bonds( bondlist: dict, are_cobis: bool, are_coops: bool, relevant_bonds: dict ) -> dict: """ Identify the strongest bonds from a list of bonds. Parameters ---------- bondlist : dict. dict including bonding information are_cobis : bool. True if these are cobis are_coops : bool. True if these are coops relevant_bonds : dict. Dict include all bonds that are considered. Returns ------- dict Dictionary including the strongest bonds. """ bonds = [] icohp_all = [] lengths = [] for a, b, c, length in zip( bondlist["list_atom1"], bondlist["list_atom2"], bondlist["list_icohp"], bondlist["list_length"], strict=True, ): bonds.append(f"{a.rstrip('0123456789')}-{b.rstrip('0123456789')}") icohp_all.append(sum(c.values())) lengths.append(length) bond_labels_unique = list(set(bonds)) sep_icohp: list[list[float]] = [[]] * len(bond_labels_unique) sep_lengths: list[list[float]] = [[]] * len(bond_labels_unique) for idx, val in enumerate(bond_labels_unique): for j, val2 in enumerate(bonds): if val == val2: sep_icohp[idx].append(icohp_all[j]) sep_lengths[idx].append(lengths[j]) if are_cobis and not are_coops: prop = "icobi" elif not are_cobis and are_coops: prop = "icoop" else: prop = "icohp" bond_dict: dict[str, dict[str, float | str]] = {} for idx, lab in enumerate(bond_labels_unique): label = lab.split("-") label.sort() for rel_bnd in relevant_bonds: rel_bnd_list = rel_bnd.split("-") rel_bnd_list.sort() if label == rel_bnd_list: if prop == "icohp": index = np.argmin(sep_icohp[idx]) bond_dict |= { rel_bnd: { "bond_strength": min(sep_icohp[idx]), "length": sep_lengths[idx][index], } } else: index = np.argmax(sep_icohp[idx]) bond_dict |= { rel_bnd: { "bond_strength": max(sep_icohp[idx]), "length": sep_lengths[idx][index], } } return bond_dict # Should probably live in atomate2 instead, not used here def read_saved_jsonl( filename: str, pymatgen_objs: bool = True, query: str = "structure" ) -> dict[str, Any]: r""" Read the data from \*.jsonl.gz files corresponding to query. Parameters ---------- filename: str. name of the JSON lines file to read pymatgen_objs: bool. if True will convert structure,coop, cobi, cohp and dos to pymatgen objects query: str or None. field name to query from the JSON lines file. If None, all data will be returned. Returns ------- dict Returns a dictionary with lobster task JSON data corresponding to query. """ lobster_data: dict[str, Any] = {} with zopen(filename, "rb") as file: for line in file: data = orjson.loads(line) if query is None: lobster_data.update(data) elif query in data: lobster_data[query] = data[query] if not lobster_data: raise ValueError( "Please recheck the query argument. " f"No data associated to the requested 'query={query}' " f"found in the JSON lines file." ) if pymatgen_objs: for query_key, value in lobster_data.items(): if isinstance(value, dict): lobster_data[query_key] = MontyDecoder().process_decoded(value) elif "lobsterpy_data" in query_key: for field in lobster_data[query_key].__fields__: val = MontyDecoder().process_decoded( getattr(lobster_data[query_key], field) ) setattr(lobster_data[query_key], field, val) return lobster_data ././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1787011023.822149 emmet_core-0.87.2/emmet/core/magnetism.py0000644000175100017510000000621515240717720020003 0ustar00runnerrunnerfrom __future__ import annotations from typing import TYPE_CHECKING from pydantic import Field from emmet.core.io.pymatgen import CollinearMagneticStructureAnalyzer, Structure from emmet.core.material_property import PropertyDoc if TYPE_CHECKING: from emmet.core.types.typing import IdentifierType class MagnetismDoc(PropertyDoc): """ Magnetic data obtain from the calculated structure """ property_name: str = "magnetism" ordering: str | None = Field( None, description="Magnetic ordering.", ) is_magnetic: bool | None = Field( None, description="Whether the material is magnetic.", ) exchange_symmetry: int | None = Field( None, description="Exchange symmetry.", ) num_magnetic_sites: int | None = Field( None, description="The number of magnetic sites.", ) num_unique_magnetic_sites: int | None = Field( None, description="The number of unique magnetic sites.", ) types_of_magnetic_species: list[str] | None = Field( None, description="Magnetic specie elements.", ) magmoms: list[float] | None = Field( None, description="Magnetic moments for each site.", ) total_magnetization: float | None = Field( None, description="Total magnetization in μB.", ) total_magnetization_normalized_vol: float | None = Field( None, description="Total magnetization normalized by volume in μB/ų.", ) total_magnetization_normalized_formula_units: float | None = Field( None, description="Total magnetization normalized by formula unit in μB/f.u. .", ) @classmethod def from_structure( cls, structure: Structure, total_magnetization: float, material_id: IdentifierType | None = None, **kwargs, ): # noqa: E501 struct_has_magmoms = "magmom" in structure.site_properties total_magnetization = abs( total_magnetization ) # not necessarily == sum(magmoms) msa = CollinearMagneticStructureAnalyzer( structure, round_magmoms=True, threshold_nonmag=0.2, threshold=0 ) magmoms = msa.magmoms.tolist() d = { "ordering": msa.ordering.value if struct_has_magmoms else "Unknown", "is_magnetic": msa.is_magnetic, "exchange_symmetry": msa.get_exchange_group_info()[1], "num_magnetic_sites": msa.number_of_magnetic_sites, "num_unique_magnetic_sites": msa.number_of_unique_magnetic_sites(), "types_of_magnetic_species": [str(t) for t in msa.types_of_magnetic_specie], "magmoms": magmoms, "total_magnetization": total_magnetization, "total_magnetization_normalized_vol": total_magnetization / structure.volume, "total_magnetization_normalized_formula_units": total_magnetization / (structure.composition.get_reduced_composition_and_factor()[1]), } return super().from_structure( meta_structure=structure, material_id=material_id, **d, **kwargs ) ././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1787011023.822149 emmet_core-0.87.2/emmet/core/material.py0000644000175100017510000001416615240717720017621 0ustar00runnerrunner"""Core definition of a Materials Document""" from __future__ import annotations from typing import TYPE_CHECKING from pydantic import BaseModel, Field from emmet.core.io.pymatgen import Structure, Molecule from emmet.core.base import EmmetBaseModel from emmet.core.mpid import MPID, MPculeID from emmet.core.structure import MoleculeMetadata, StructureMetadata from emmet.core.types.enums import DeprecationMessage from emmet.core.types.pymatgen_types.structure_adapter import ( MoleculeType, StructureType, ) from emmet.core.types.typing import DateTimeType, IdentifierType, MaterialIdentifierType if TYPE_CHECKING: from typing_extensions import Self class PropertyOrigin(BaseModel): """ Provenance document for the origin of properties in a material document """ name: str = Field(..., description="The property name") task_id: IdentifierType = Field( ..., description="The calculation ID this property comes from." ) last_updated: DateTimeType = Field( # type: ignore description="The timestamp when this calculation was last updated", ) class BasePropertyMetadata(StructureMetadata, EmmetBaseModel): """ Base model definition for a single material property. This may contain any amount of structure metadata for the purpose of search. This is intended to be inherited and extended, not used directly """ material_id: MaterialIdentifierType | None = Field( None, description="The Materials Project ID of the material, used as a universal reference across property documents." "This comes in the form: mp-******.", ) deprecated: bool = Field( True, description="Whether this property document is deprecated.", ) deprecation_reasons: list[DeprecationMessage | str] | None = Field( None, description="List of deprecation tags detailing why this document isn't valid.", ) last_updated: DateTimeType = Field( description="Timestamp for the most recent calculation update for this property.", ) origins: list[PropertyOrigin] | None = Field( None, description="Dictionary for tracking the provenance of properties." ) warnings: list[str] = Field( [], description="Any warnings related to this property." ) structure: StructureType | None = Field( ..., description="The structure of the this material.", ) @classmethod def from_structure( # type: ignore[override] cls, meta_structure: Structure, material_id: IdentifierType | None = None, **kwargs, ) -> Self: """ Builds a materials document using a minimal amount of information. Note that structure is stored as a private attr, and will not be included in `PropertyDoc().model_dump()` """ return super().from_structure( meta_structure=meta_structure, structure=meta_structure, material_id=material_id, **kwargs, ) # type: ignore class MaterialsDoc(BasePropertyMetadata): """ Definition for a core Materials Document """ initial_structures: list[StructureType] = Field( [], description="Initial structures used in the DFT optimizations corresponding to this material.", ) task_ids: list[IdentifierType] = Field( [], description="List of Calculations IDs used to make this Materials Document.", ) deprecated_tasks: list[str] = Field([], title="Deprecated Tasks") calc_types: dict[str, str] | None = Field( None, description="Calculation types for all the calculations that make up this material.", ) created_at: DateTimeType = Field( description="Timestamp for when this material document was first created.", ) class CoreMoleculeDoc(MoleculeMetadata, EmmetBaseModel): """ Definition for a core Molecule Document """ # Only molecule_id is required for all documents molecule_id: MPculeID = Field( ..., description="The ID of this molecule, used as a universal reference across property documents." "This comes in the form of an MPID (or int) or MPculeID (or str)", ) molecule: MoleculeType = Field( ..., description="The best (typically meaning lowest in energy) structure for this molecule", ) deprecated: bool = Field( True, description="Whether this molecule document is deprecated.", ) # TODO: Why might a molecule be deprecated? deprecation_reasons: list[str] | None = Field( None, description="List of deprecation tags detailing why this molecules document isn't valid", ) initial_molecules: list[MoleculeType] = Field( [], description="Initial molecules used in the DFT geometry optimizations corresponding to this molecule", ) task_ids: list[MPID | MPculeID] = Field( [], title="Calculation IDs", description="List of Calculations IDs used to make this Molecule Document", ) # TODO: Should this be MPID? deprecated_tasks: list[str] = Field([], title="Deprecated Tasks") calc_types: dict[str, str] | None = Field( None, description="Calculation types for all the tasks that make up this molecule", ) last_updated: DateTimeType = Field( description="Timestamp for when this document was last updated", ) created_at: DateTimeType = Field( description="Timestamp for when this document was first created", ) origins: list[PropertyOrigin] | None = Field( None, description="Dictionary for tracking the provenance of properties" ) warnings: list[str] = Field([], description="Any warnings related to this molecule") @classmethod def from_molecule( cls, molecule: Molecule, molecule_id: MPculeID, **kwargs ) -> Self: # type: ignore[override] """ Builds a molecule document using the minimal amount of information """ return super().from_molecule( # type: ignore meta_molecule=molecule, molecule_id=molecule_id, molecule=molecule, **kwargs ) ././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1787011023.822149 emmet_core-0.87.2/emmet/core/material_property.py0000644000175100017510000000135015240717720021554 0ustar00runnerrunner"""Core definition of a Materials Document""" from __future__ import annotations from pydantic import Field from pydantic.json_schema import SkipJsonSchema from emmet.core.material import BasePropertyMetadata from emmet.core.types.pymatgen_types.structure_adapter import StructureType class PropertyDoc(BasePropertyMetadata): """ Base model definition for any singular materials property. This may contain any amount of structure metadata for the purpose of search. This is intended to be inherited and extended, not used directly """ property_name: str structure: SkipJsonSchema[StructureType | None] = Field( None, description="The structure associated with this property.", exclude=True ) ././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1787011023.822149 emmet_core-0.87.2/emmet/core/math.py0000644000175100017510000000134215240717720016744 0ustar00runnerrunnerVector3D = tuple[float, float, float] Matrix3D = tuple[Vector3D, Vector3D, Vector3D] Vector6D = tuple[float, float, float, float, float, float] MatrixVoigt = tuple[Vector6D, Vector6D, Vector6D, Vector6D, Vector6D, Vector6D] Tensor3R = list[list[list[float]]] Tensor4R = list[list[list[list[float]]]] ListVector3D = list[float] ListMatrix3D = list[ListVector3D] VOIGT_INDICES = [(0, 0), (1, 1), (2, 2), (1, 2), (0, 2), (0, 1)] def matrix_3x3_to_voigt(matrix: Matrix3D) -> list[float]: """Convert a 3x3 symmetric matrix to its Voigt vector representation. Parameters ----------- matrix : Matrix3D Returns ----------- list of float """ return [matrix[idx[0]][idx[1]] for idx in VOIGT_INDICES] ././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1787011023.822149 emmet_core-0.87.2/emmet/core/ml.py0000644000175100017510000001152715240717720016431 0ustar00runnerrunner"""Define schemas for ML calculations and training data.""" from __future__ import annotations from pydantic import ( Field, field_validator, ) from emmet.core.io.pymatgen import ElasticTensor from emmet.core.elasticity import ( BulkModulus, ElasticityDoc, ElasticTensorDoc, ShearModulus, ) from emmet.core.types.pymatgen_types.structure_adapter import StructureType class MLDoc(ElasticityDoc): """Schema for machine learning interatomic potential calculations. Attributes: - metadata - material_id (str): MP ID - structure (Structure): pymatgen Structure object - deprecated (bool): whether this material is deprecated in MP - calculator (str): name of model used as ML potential. - version (str): version of matcalc used to generate this document - relaxation - final_structure: relaxed pymatgen Structure object - energy (float): final energy in eV - volume (float): final volume in Angstrom^3 - lattice parameters (float): a, b, c, alpha, beta, gamma - equation of state - eos (dict[str, list[float]]): with keys energies and volumes - bulk_modulus_bm (float): Birch-Murnaghan bulk modulus in GPa - phonon - temperatures (list[float]): temperatures in K - free_energy (list[float]): Helmholtz energies at those temperatures in eV - entropy (list[float]): entropies at those temperatures in eV/K - heat_capacities (list[float]): heat capacities at constant volume in eV/K - elasticity - elastic_tensor (ElasticTensorDoc): pydantic model from emmet.core.elasticity - shear_modulus (ShearModulus): Voigt-Reuss-Hill shear modulus (single float in eV/Angstrom^3) - bulk_modulus (BulkModulus): Voigt-Reuss-Hill bulk modulus (single float in eV/Angstrom^3) - youngs_modulus (float): Young's modulus (single float in eV/Angstrom^3) """ property_name: str = "ml" # metadata structure: StructureType = Field(description="Original structure") model: str | None = Field(None, description="Name of model used as ML potential.") version: str | None = Field( None, description="Version of model used as ML potential" ) # relaxation attributes final_structure: StructureType | None = Field( None, description="ML-potential-relaxed structure" ) energy: float | None = Field(None, description="Final energy in eV") volume: float | None = Field(None, description="Final volume in Angstrom^3") a: float | None = Field(None, description="Lattice length a in Angstrom") b: float | None = Field(None, description="Lattice length b in Angstrom") c: float | None = Field(None, description="Lattice length c in Angstrom") alpha: float | None = Field(None, description="Lattice angle alpha in degrees") beta: float | None = Field(None, description="Lattice angle beta in degrees") gamma: float | None = Field(None, description="Lattice angle gamma in degrees") # equation of state attributes eos: dict[str, list[float]] | None = Field( None, description="dict with keys energies and volumes" ) bulk_modulus_bm: float | None = Field(None, description="bm.b0_GPa") # phonons attributes temperatures: list[float] | None = Field(None, description="list of temperatures") free_energy: list[float] | None = Field( None, description="list of Helmholtz free energies at corresponding temperatures", ) entropy: list[float] | None = Field( None, description="list of entropies at corresponding temperatures in eV/K" ) heat_capacity: list[float] | None = Field( None, description="list of heat capacities at constant volume at corresponding " "temperatures in eV/K", ) # elasticity attributes # all inherited from ElasticityDoc @field_validator("elastic_tensor", mode="before") def elastic_tensor(cls, val) -> ElasticTensorDoc: """ElasticTensorDoc from MSONable dict of ElasticTensor, or list (specifying the Voigt array) or the ElasticTensor class itself. """ if isinstance(val, dict): tensor = ElasticTensor.from_dict(val) elif isinstance(val, (list, tuple)): tensor = ElasticTensor(val) else: tensor = val return ElasticTensorDoc(raw=tensor.voigt.tolist()) @field_validator("bulk_modulus", mode="before") def bulk_vrh_no_suffix(cls, new_key, values): """Map field bulk_modulus_vrh to bulk_modulus.""" val = values.get("bulk_modulus_vrh", new_key) return BulkModulus(vrh=val) @field_validator("shear_modulus", mode="before") def shear_vrh_no_suffix(cls, new_key, values): """Map field shear_modulus_vrh to shear_modulus.""" val = values.get("shear_modulus_vrh", new_key) return ShearModulus(vrh=val) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011028.9710243 emmet_core-0.87.2/emmet/core/mobility/0000755000175100017510000000000015240717725017276 5ustar00runnerrunner././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1787011023.822149 emmet_core-0.87.2/emmet/core/mobility/__init__.py0000644000175100017510000000000015240717720021370 0ustar00runnerrunner././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8226638 emmet_core-0.87.2/emmet/core/mobility/migrationgraph.py0000644000175100017510000006502615240717720022667 0ustar00runnerrunner"""Define schemas for ion migration.""" from __future__ import annotations import copy import logging from typing import TYPE_CHECKING, Literal import numpy as np from pydantic import BaseModel, Field from emmet.core.io.pymatgen import ( StructureMatcher, Element, Structure, ComputedEntry, ComputedStructureEntry, ) from emmet.core.base import EmmetBaseModel from emmet.core.neb import NebPathwayResult from emmet.core.types.enums import ValueEnum from emmet.core.types.typing import DateTimeType from emmet.core.utils import arrow_incompatible try: from emmet.core.io.pymatgen import ( MigrationGraph, add_edge_data_from_sc, get_sc_fromstruct, get_start_end_structures, ) except ImportError: raise ImportError( "pip install pymatgen-analysis-diffusion to use MigrationGraphDoc" ) if TYPE_CHECKING: from typing import Any from typing_extensions import Self class HopState(ValueEnum): SUCCESS = "successful" FAILED = "failed" ERROR = "error" UNMATCHED = "unmatched" class HopSummary(BaseModel): """Store high-level migration data.""" hop_label: int | None = Field(None, description="The label of this hop.") hop_key: str | None = Field( None, description="The pair of indices representing this hop." ) hop_distance: float | None = Field( None, description="The distance traversed by the migrating ion." ) cost: float | None = Field(None, description="The penalty in this migration.") match_state: HopState | None = Field( None, description="Whether the hop could be matched to a migration graph hop." ) @arrow_incompatible class MigrationGraphDoc(EmmetBaseModel): """ MigrationGraph Doc. Stores MigrationGraph and info such as ComputedStructureEntries (ComputedEntry can be used for working ion) and cutoff distance that are used to generated the object. Note: this doc is not self-contained within pymatgen, as it has dependence on pymatgen.analysis.diffusion, a namespace package aka pymatgen-diffusion. """ battery_id: str | None = Field( None, description="The battery id for this MigrationGraphDoc" ) last_updated: DateTimeType = Field( description="Timestamp for the most recent calculation for this MigrationGraph document.", ) warnings: list[str] = Field( [], description="Any warnings related to this property." ) deprecated: bool = Field( False, description="Indicates whether a migration graph fails to be constructed from the provided entries. Defaults to False, indicating mg can be constructed from entries.", # noqa: E501 ) hop_cutoff: float | None = Field( None, description="The numerical value in angstroms used to cap the maximum length of a hop.", ) entries_for_generation: list[ComputedStructureEntry] | None = Field( None, description="A list of ComputedStructureEntries used to generate the structure with all working ion sites.", ) working_ion_entry: ComputedEntry | ComputedStructureEntry | None = Field( None, description="The ComputedStructureEntry of the working ion." ) migration_graph: MigrationGraph | None = Field( None, description="The MigrationGraph object as defined in pymatgen.analysis.diffusion.", ) populate_sc_fields: bool = Field( True, description="Flag indicating whether this document has populated the supercell fields", ) sc_gen_schema: str | None = Field( None, description="The schema used to generate supercell fields. " "hops_only: contains only endpoints for sc hops; complete: contains all transformed uc sites.", ) min_length_sc: float | None = Field( None, description="The minimum length used to generate supercell using pymatgen.", ) minmax_num_atoms: tuple[int, int] | None = Field( None, description="The min/max number of atoms used to genreate supercell using pymatgen.", ) matrix_supercell_structure: Structure | None = Field( None, description=( "The matrix suprcell structure that does not contain the " "mobile ions for the purpose of migration analysis." ), ) conversion_matrix: list[list[int | float]] | None = Field( None, description="The conversion matrix used to convert unit cell to supercell.", ) inserted_ion_coords: list[dict[str, list[float] | str | int]] | None = Field( None, description="A dictionary containing all mobile ion fractional coordinates in terms of supercell.", ) insert_coords_combo: list[str] | None = Field( None, description=( "A list of combinations 'a+b' to designate hops in the supercell. " "Each combo should correspond to one unique hop in MigrationGraph." ), ) paths_summary: dict[int, list[HopSummary]] | None = Field( None, description=( "A dictionary of ranked intercalation pathways given cost " "(e.g. migration barrier from transition state calcs) of each unique_hop." ), ) migration_graph_w_cost: MigrationGraph | None = Field( None, description="MigrationGraph instance that contains cost (e.g. from NEB) information", ) @classmethod def from_entries_and_distance( cls, battery_id: str, grouped_entries: list[ComputedStructureEntry], working_ion_entry: ComputedEntry | ComputedStructureEntry, hop_cutoff: float, populate_sc_fields: bool = True, sc_gen_schema: Literal["complete", "hops_only"] = "complete", ltol: float = 0.2, stol: float = 0.3, angle_tol: float = 5, min_length_sc: float | None = None, minmax_num_atoms: tuple[int, int] | None = None, **kwargs, ) -> Self: """ Take a list of ComputedStructureEntry and generate a MigrationGraphDoc. Note: Can use a ComputedEntry for the working ion. Then a MigrationGraph object is generated with with_distance() method with a designated cutoff. If populate_sc_fields set to True, this method will populate the supercell related fields. Required kwargs are min_length_sc and minmax_num_atoms. """ ranked_structures = MigrationGraph.get_structure_from_entries( entries=grouped_entries, migrating_ion_entry=working_ion_entry ) max_sites_struct = ranked_structures[0] migration_graph = MigrationGraph.with_distance( structure=max_sites_struct, migrating_specie=working_ion_entry.composition.chemical_system, max_distance=hop_cutoff, ) if populate_sc_fields: if min_length_sc is None or minmax_num_atoms is None: raise TypeError( "Ensure that min_length_sc and minmax_num_atoms are set when using populate_sc_fields = True." ) sm = StructureMatcher(ltol, stol, angle_tol) ( host_sc, sc_mat, min_length_sc, minmax_num_atoms, coords_list, combo, ) = cls.generate_sc_fields( mg=migration_graph, min_length_sc=min_length_sc, minmax_num_atoms=minmax_num_atoms, sm=sm, sc_gen_schema=sc_gen_schema, ) return cls( battery_id=battery_id, hop_cutoff=hop_cutoff, entries_for_generation=grouped_entries, working_ion_entry=working_ion_entry, migration_graph=migration_graph, matrix_supercell_structure=host_sc, conversion_matrix=sc_mat, inserted_ion_coords=coords_list, insert_coords_combo=combo, sc_gen_schema=sc_gen_schema, **kwargs, ) return cls( battery_id=battery_id, hop_cutoff=hop_cutoff, entries_for_generation=grouped_entries, working_ion_entry=working_ion_entry, migration_graph=migration_graph, **kwargs, ) @classmethod def augment_from_mgd_and_npr( cls, mgd: MigrationGraphDoc, npr: NebPathwayResult, barrier_type: Literal["max_barrier", "energy_range"], ) -> MigrationGraphDoc: """ Takes an existing MigrationGraphDoc and augment it. Specifically, the `paths_summary` and `migration_graph_w_cost` fields will be populated with transition state data from NebPathwayResult. `barrier_type` can be set to 'barrier' or 'energy_range'. See docstring of get_paths_summary_with_neb_res for detail. """ mgd_w_cost = copy.deepcopy(mgd) if not mgd_w_cost.migration_graph: raise ValueError("A MigrationGraph must be provided to augment it.") paths_summary, mg_new = cls.get_paths_summary_with_neb_res( mg=mgd_w_cost.migration_graph, npr=npr, barrier_type=barrier_type ) mgd_w_cost.paths_summary = paths_summary mgd_w_cost.migration_graph_w_cost = mg_new return mgd_w_cost @staticmethod def generate_sc_fields( mg: MigrationGraph, min_length_sc: float, minmax_num_atoms: tuple[int, int], sm: StructureMatcher, sc_gen_schema: Literal["hops_only", "complete"], ): if sc_gen_schema not in ["hops_only", "complete"]: raise ValueError( f"Invalid sc_gen_schema: {sc_gen_schema}. Specify 'hops_only' or 'complete'." ) min_length_sc = min_length_sc minmax_num_atoms = minmax_num_atoms sc_mat = get_sc_fromstruct( base_struct=mg.structure, min_atoms=minmax_num_atoms[0], max_atoms=minmax_num_atoms[1], min_length=min_length_sc, ) sc_mat = sc_mat.tolist() # type: ignore[attr-defined] host_sc = mg.host_structure * sc_mat working_ion = mg.only_sites[0].species_string if sc_gen_schema == "hops_only": coords_list, combo = MigrationGraphDoc.get_sc_info_hops_only(mg, sc_mat) if sc_gen_schema == "complete": coords_list = MigrationGraphDoc.ordered_sc_site_list(mg.only_sites, sc_mat) combo, coords_list = MigrationGraphDoc.get_hop_sc_combo( mg.unique_hops, sc_mat, sm, host_sc, working_ion, coords_list ) return host_sc, sc_mat, min_length_sc, minmax_num_atoms, coords_list, combo @staticmethod def get_sc_info_hops_only(mg: MigrationGraph, sc_mat: list[list[int | float]]): coords_list = [] combo = [] base_struct = mg.host_structure for one_hop in mg.unique_hops.values(): migration_hop = one_hop["hop"] isite, iindex = migration_hop.isite, one_hop["iindex"] esite, eindex = migration_hop.esite, one_hop["eindex"] start_structure, end_structure, _ = get_start_end_structures( isite=isite, esite=esite, base_struct=base_struct, sc_mat=sc_mat, vac_mode=False, ) sc_ini_info = { "uc_site_type": iindex, "site_frac_coords": tuple(start_structure[0].frac_coords), } if sc_ini_info not in coords_list: coords_list.append(sc_ini_info) combo_ini = coords_list.index(sc_ini_info) sc_fin_info = { "uc_site_type": eindex, "site_frac_coords": tuple(end_structure[0].frac_coords), } if sc_fin_info not in coords_list: coords_list.append(sc_fin_info) combo_fin = coords_list.index(sc_fin_info) combo.append(f"{combo_ini}+{combo_fin}") for coords in coords_list: coords["site_frac_coords"] = list(coords["site_frac_coords"]) return coords_list, combo @staticmethod def ordered_sc_site_list(uc_sites_only: Structure, sc_mat: list[list[int]]): uc_no_site = uc_sites_only.copy() uc_no_site.remove_sites(range(len(uc_sites_only))) working_ion = uc_sites_only[0].species_string sc_site_dict = {} # type: dict for i, e in enumerate(uc_sites_only): uc_one_set = uc_no_site.copy() uc_one_set.insert(0, working_ion, e.frac_coords) sc_one_set = uc_one_set * sc_mat for index in range(len(sc_one_set)): sc_site_dict[len(sc_site_dict) + 1] = { "uc_site_type": i, "site_frac_coords": list(sc_one_set[index].frac_coords), # "extra_site": False } ordered_site_list = [ e for i, e in enumerate( sorted( sc_site_dict.values(), key=lambda v: float(np.linalg.norm(v["site_frac_coords"])), ) ) ] return ordered_site_list @staticmethod def get_hop_sc_combo( unique_hops: dict, sc_mat: list[list[int]], sm: StructureMatcher, host_sc: Structure, working_ion: str, ordered_sc_site_list: list, ) -> tuple[list[str], list[dict[str, Any]]]: combo = [] unique_hops = {k: v for k, v in sorted(unique_hops.items())} for one_hop in unique_hops.values(): added = False sc_isite_set = { k: v for k, v in enumerate(ordered_sc_site_list) if v["uc_site_type"] == one_hop["iindex"] } sc_esite_set = { k: v for k, v in enumerate(ordered_sc_site_list) if v["uc_site_type"] == one_hop["eindex"] } for sc_iindex, sc_isite in sc_isite_set.items(): for sc_eindex, sc_esite in sc_esite_set.items(): sc_check = host_sc.copy() sc_check.insert(0, working_ion, sc_isite["site_frac_coords"]) sc_check.insert(1, working_ion, sc_esite["site_frac_coords"]) if MigrationGraphDoc.compare_sc_one_hop( one_hop, sc_mat, sm, host_sc, sc_check, working_ion, (sc_isite["uc_site_type"], sc_esite["uc_site_type"]), ): combo.append(f"{sc_iindex}+{sc_eindex}") added = True break if added: break if not added: new_combo, ordered_sc_site_list = MigrationGraphDoc.append_new_site( host_sc, ordered_sc_site_list, one_hop, sc_mat, working_ion ) combo.append(new_combo) return combo, ordered_sc_site_list @staticmethod def compare_sc_one_hop( one_hop: dict, sc_mat: list, sm: StructureMatcher, host_sc: Structure, sc_check: Structure, working_ion: str, uc_site_types: tuple[int, int], ) -> bool: sc_mat_inv = np.linalg.inv(sc_mat) convert_sc_icoords = np.dot(one_hop["ipos"], sc_mat_inv) convert_sc_ecoords = np.dot(one_hop["epos"], sc_mat_inv) convert_sc = host_sc.copy() convert_sc.insert(0, working_ion, convert_sc_icoords) convert_sc.insert(1, working_ion, convert_sc_ecoords) if sm.fit(convert_sc, sc_check): one_hop_dis = one_hop["hop"].length sc_check_hop_dis = np.linalg.norm(sc_check[0].coords - sc_check[1].coords) if np.isclose(one_hop_dis, sc_check_hop_dis, atol=1e-5): if ( one_hop["iindex"] == uc_site_types[0] and one_hop["eindex"] == uc_site_types[1] ): return True return False @staticmethod def append_new_site( host_sc: Structure, ordered_sc_site_list: list, one_hop: dict, sc_mat: list[list[int]], working_ion: str, ) -> tuple[str, list[dict[str, Any]]]: sc_mat_inv = np.linalg.inv(sc_mat) sc_ipos = np.dot(one_hop["ipos"], sc_mat_inv) sc_epos = np.dot(one_hop["epos"], sc_mat_inv) sc_iindex, sc_eindex = None, None host_sc_insert = host_sc.copy() for k, v in enumerate(ordered_sc_site_list): if np.allclose(sc_ipos, v["site_frac_coords"], rtol=0.1, atol=0.1): sc_iindex = k if np.allclose(sc_epos, v["site_frac_coords"], rtol=0.1, atol=0.1): sc_eindex = k if sc_iindex is None: host_sc_insert.insert(0, working_ion, sc_ipos) ordered_sc_site_list.append( { "uc_site_type": one_hop["iindex"], "site_frac_coords": list(host_sc_insert[0].frac_coords), "extra_site": True, } ) sc_iindex = len(ordered_sc_site_list) - 1 if sc_eindex is None: host_sc_insert.insert(0, working_ion, sc_epos) ordered_sc_site_list.append( { "uc_site_type": one_hop["eindex"], "site_frac_coords": list(host_sc_insert[0].frac_coords), "extra_site": True, } ) sc_eindex = len(ordered_sc_site_list) - 1 return f"{sc_iindex}+{sc_eindex}", ordered_sc_site_list @staticmethod def get_distinct_hop_sites( inserted_ion_coords: list[dict[str, list[float]]], insert_coords_combo: list[str], ) -> tuple[list[list[float]], list[str], dict[str, str]]: """ Expand the list of inserted coordinates and hop indices. Parameters ----------- inserted_ion_coords: list[dict[str, list[float]]] List of dict's containing the working ion coordinates. insert_coords_combo: list[str] List of hop indices of the form "+" Returns ----------- list of list of float List of working ion coordinates list of str List of hop keys dict of str to str Dict mapping of hop keys in different formats """ dis_sites_list: list[list[float]] = [] dis_combo_list: list[str] = [] mgdoc_sites_mapping: dict[int, int] = {} combo_mapping = {} for one_combo in insert_coords_combo: ini, end = list(map(int, one_combo.split("+"))) if ini in mgdoc_sites_mapping.keys(): dis_ini = mgdoc_sites_mapping[ini] else: dis_sites_list.append( list(inserted_ion_coords[ini]["site_frac_coords"]) ) dis_ini = len(dis_sites_list) - 1 mgdoc_sites_mapping[ini] = dis_ini if end in mgdoc_sites_mapping.keys(): dis_end = mgdoc_sites_mapping[end] else: dis_sites_list.append( list(inserted_ion_coords[end]["site_frac_coords"]) ) dis_end = len(dis_sites_list) - 1 mgdoc_sites_mapping[end] = dis_end dis_combo = f"{dis_ini}+{dis_end}" dis_combo_list.append(dis_combo) combo_mapping[dis_combo] = one_combo return dis_sites_list, dis_combo_list, combo_mapping @staticmethod def get_paths_summary_with_neb_res( mg: MigrationGraph, npr: NebPathwayResult, barrier_type: Literal["max_barrier", "energy_range"], zero_short_hop_cost: bool = True, verbose: bool = False, ) -> tuple[dict[int, list[HopSummary]], MigrationGraph]: """ This is a post-processing function that matches the results of transition state cals (NEB or ApproxNEB) and unique_hops in the MigrationGraph, and then outputs a ranked list according to the calculated barrier Parameters ---------- mgd: MigrationGraph The MigrationGraph to be matched and get paths from npr: NebPathwayResult The doc used to get transition state calc info barrier_type: str The type of barrier used to assign cost. Currently supporting 'barrier': max of forward & reverse barrier (max energy - ep energy) 'energy_range': max of energies - min of energies """ if barrier_type not in ["max_barrier", "energy_range"]: raise ValueError( f"Invalid barrier_type: {barrier_type}. Specify 'max_barrier' or 'energy_range'." ) energy_struct_info = MigrationGraphDoc._get_energy_struct_info(npr) mg_new = copy.deepcopy(mg) for info in energy_struct_info.values(): try: add_edge_data_from_sc( mg_new, i_sc=info["input_endpoints"][0], e_sc=info["input_endpoints"][-1], data_array=info, # type: ignore[arg-type] key="energy_struct_info", ) except (RuntimeError, ValueError) as e: if verbose: logging.warning(f"{e} occured to during matching") unmatched_uhops = [] failed_neb_uhops = [] for k, v in mg_new.unique_hops.items(): # if a unique_hop failed to match with an NEB hops calc (no symmetry match, missing calc, etc.) # the cost is set to infinity. # if a unique_hops matches to an NEB hop in the FAILED state # cost is set to the insertion energy diff (barrier from NebResult) # but the failed state is explicitly stated in paths_summary if "energy_struct_info" not in v: cost = float("inf") hop_key = "" state = "unmatched" unmatched_uhops.append(k) else: state = v["energy_struct_info"]["state"] if state == HopState.FAILED: failed_neb_uhops.append(k) cost, hop_key = ( v["energy_struct_info"][barrier_type], v["energy_struct_info"]["hop_key"], ) # for short hops with low barrier, set cost to 0 if zero_short_hop_cost: cutoff = 2 * MigrationGraphDoc._get_wi_ionic_radius(mg_new) if MigrationGraphDoc._check_short_hop( v, current_cost=cost, length_cutoff=cutoff ): cost = 0 mg_new.add_data_to_similar_edges( target_label=v["hop_label"], data={"cost": cost, "hop_key": hop_key, "match_state": state}, ) if verbose: if unmatched_uhops: logging.warning( f"The following unique hops have not matched: {unmatched_uhops}" ) if failed_neb_uhops: logging.warning( f"The following unique_hops matched but the corresponding calculation " f"is in the FAILED state: {failed_neb_uhops}" ) paths = list(mg_new.get_path()) paths_summary = {} for one_path in paths: path_summary = [] for one_hop in one_path[1]: hop_summary = { k: one_hop.get(k, None) for k in ( "hop_label", "hop_key", "hop_distance", "cost", "match_state", ) } if isinstance(hs := hop_summary.get("match_state"), str): hop_summary["match_state"] = HopState(hs) path_summary.append(HopSummary(**hop_summary)) paths_summary[one_path[0]] = path_summary return paths_summary, mg_new @staticmethod def _get_energy_struct_info(npr: NebPathwayResult) -> dict[str, dict[str, Any]]: """ Convert results in an NebPathWayResult object to a dict. This method exists primarily to populate MigrationGraph.unique_hops. """ energy_struct_info = {} for hop_key, data in npr.hops.items(): energy_struct_info[hop_key] = { "hop_key": hop_key, "max_barrier": ( npr.max_barriers.get(hop_key) if npr.max_barriers else None ), "energy_range": data.barrier_energy_range, "energies": data.energies, "state": data.state, "calc_fail_info": data.failure_reasons, "input_endpoints": ( [data.initial_images[0], data.initial_images[-1]] if data.initial_images else [] ), "output_structs": data.images, } return energy_struct_info @staticmethod def _get_wi_ionic_radius(mg: MigrationGraph) -> float: """ Guess the ionic radius of the working ion from a MigrationGraph. """ wi = Element(mg.only_sites[0].species.elements[0].name) # type: ignore[arg-type] if ( wi_oxi_state_guess := mg.structure.composition.oxi_state_guesses()[0][ wi.name ] ) in wi.ionic_radii: return wi.ionic_radii[wi_oxi_state_guess] # type: ignore[index] return wi.ionic_radii[min(wi.ionic_radii)] @staticmethod def _check_short_hop( unique_hop: dict[str, Any], current_cost: float, length_cutoff: float, barrier_cutoff: float = 0.02, ) -> bool: """ Check whether a migration hop is within distance and barrier cutoffs. Parameters ----------- unique_hop : dict of str to Any Representation of the hop, including distance current_cost : float Penalty associated with the migration event length_cutoff : float Distance cutoff for a single hop barrier_cutoff : float, default 0.02 Cutoff for the energy barrier of the hop. """ hop_length = unique_hop["hop_distance"] if hop_length < length_cutoff and current_cost < barrier_cutoff: return True return False ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011028.9731448 emmet_core-0.87.2/emmet/core/molecules/0000755000175100017510000000000015240717725017436 5ustar00runnerrunner././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8226638 emmet_core-0.87.2/emmet/core/molecules/__init__.py0000644000175100017510000000111315240717720021536 0ustar00runnerrunnerfrom __future__ import annotations from pydantic import BaseModel, Field from emmet.core.mpid import MPID, MPculeID from emmet.core.types.typing import DateTimeType class MolPropertyOrigin(BaseModel): """Provenance for molecular properties, uses legacy MPID.""" name: str = Field(..., description="The property name") task_id: MPID | MPculeID = Field( ..., description="The calculation ID this property comes from." ) last_updated: DateTimeType = Field( # type: ignore description="The timestamp when this calculation was last updated", ) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8228872 emmet_core-0.87.2/emmet/core/molecules/atomic.py0000644000175100017510000001362415240717720021265 0ustar00runnerrunnerfrom hashlib import blake2b from pydantic import Field from emmet.core.molecules import MolPropertyOrigin from emmet.core.molecules.molecule_property import PropertyDoc from emmet.core.mpid import MPculeID from emmet.core.qchem.task import TaskDocument __author__ = "Evan Spotte-Smith " CHARGES_METHODS = ["nbo", "resp", "critic2", "mulliken"] SPINS_METHODS = ["nbo", "mulliken"] class PartialChargesDoc(PropertyDoc): """Atomic partial charges of a molecule""" property_name: str = "partial_charges" method: str = Field( ..., description="Method used to compute atomic partial charges" ) partial_charges: list[float] = Field( ..., description="Atomic partial charges for the molecule" ) @classmethod def from_task( cls, task: TaskDocument, molecule_id: MPculeID, preferred_methods: list[str], deprecated: bool = False, **kwargs, ): # type: ignore[override] """ Determine partial charges from a task document :param task: task document from which partial charges can be extracted :param molecule_id: MPculeID :param preferred_methods: list of methods; by default, NBO7, RESP, Critic2, and Mulliken, in that order :param kwargs: to pass to PropertyDoc :return: """ charges = None method = None if task.output.optimized_molecule is not None: mol = task.output.optimized_molecule else: mol = task.output.initial_molecule for m in preferred_methods: if m == "nbo" and task.output.nbo is not None: method = m charges = [ float(task.output.nbo["natural_populations"][0]["Charge"][str(i)]) for i in range(len(mol)) ] break elif m == "resp" and task.output.resp is not None: method = m charges = [float(i) for i in task.output.resp] break elif m == "critic2" and task.critic2 is not None: method = m charges = list([float(i) for i in task.critic2["processed"]["charges"]]) break elif m == "mulliken" and task.output.mulliken is not None: method = m if isinstance(task.output.mulliken[0], list): charges = [float(mull[0]) for mull in task.output.mulliken] else: charges = [float(i) for i in task.output.mulliken] break id_string = ( f"partial_charges-{molecule_id}-{task.task_id}-{task.lot_solvent}-{method}" ) h = blake2b() h.update(id_string.encode("utf-8")) property_id = h.hexdigest() if charges is None: raise Exception("No valid partial charge information!") return super().from_molecule( meta_molecule=mol, property_id=property_id, molecule_id=molecule_id, level_of_theory=task.level_of_theory, solvent=task.solvent, lot_solvent=task.lot_solvent, partial_charges=charges, method=method, origins=[MolPropertyOrigin(name="partial_charges", task_id=task.task_id)], # type: ignore[call-arg] deprecated=deprecated, **kwargs, ) class PartialSpinsDoc(PropertyDoc): """Atomic partial charges of a molecule""" property_name: str = "partial_spins" method: str = Field(..., description="Method used to compute atomic partial spins") partial_spins: list[float] = Field( ..., description="Atomic partial spins for the molecule" ) @classmethod def from_task( cls, task: TaskDocument, molecule_id: MPculeID, preferred_methods: list[str], deprecated: bool = False, **kwargs, ): # type: ignore[override] """ Determine partial spins from a task document :param task: task document from which partial spins can be extracted :param molecule_id: MPculeID :param preferred_methods: list of methods; by default, NBO7 and Mulliken, in that order :param kwargs: to pass to PropertyDoc :return: """ spins = None method = None if task.output.optimized_molecule is not None: mol = task.output.optimized_molecule else: mol = task.output.initial_molecule if mol.spin_multiplicity == 1: raise Exception("Closed-shell molecule has no partial spins!") for m in preferred_methods: if m == "nbo" and task.output.nbo is not None: method = m spins = [ float(task.output.nbo["natural_populations"][0]["Density"][str(i)]) for i in range(len(mol)) ] break elif m == "mulliken" and task.output.mulliken is not None: method = m spins = [float(mull[1]) for mull in task.output.mulliken] break id_string = ( f"partial_spins-{molecule_id}-{task.task_id}-{task.lot_solvent}-{method}" ) h = blake2b() h.update(id_string.encode("utf-8")) property_id = h.hexdigest() if spins is None: raise Exception("No valid partial spin information!") return super().from_molecule( meta_molecule=mol, property_id=property_id, molecule_id=molecule_id, level_of_theory=task.level_of_theory, solvent=task.solvent, lot_solvent=task.lot_solvent, partial_spins=spins, method=method, origins=[MolPropertyOrigin(name="partial_spins", task_id=task.task_id)], # type: ignore[call-arg] deprecated=deprecated, **kwargs, ) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8228872 emmet_core-0.87.2/emmet/core/molecules/bonds.py0000644000175100017510000003453615240717720021123 0ustar00runnerrunnerfrom __future__ import annotations import copy from hashlib import blake2b from typing import TYPE_CHECKING, Any import networkx as nx from pydantic import Field from emmet.core.io.pymatgen import MoleculeGraph, Molecule from emmet.core.molecules import MolPropertyOrigin from emmet.core.molecules.molecule_property import PropertyDoc from emmet.core.mpid import MPculeID from emmet.core.qchem.task import TaskDocument from emmet.core.types.pymatgen_types.structure_graph_adapter import MoleculeGraphType from emmet.core.utils import make_mol_graph if TYPE_CHECKING: from typing import Any __author__ = "Evan Spotte-Smith " metals = [ "Li", "Be", "Na", "Mg", "Al", "K", "Ca", "Sc", "Ti", "V", "Cr", "Mn", "Fe", "Co", "Ni", "Cu", "Zn", "Ga", "Rb", "Sr", "Y", "Zr", "Nb", "Mo", "Tc", "Ru", "Rh", "Pd", "Ag", "Cd", "In", "Sn", "Cs", "Ba", "Hf", "Ta", "W", "Re", "Os", "Ir", "Pt", "Au", "Hg", "Tl", "Pb", "Bi", ] BOND_METHODS = ["nbo", "critic2", "OpenBabelNN + metal_edge_extender"] def fix_C_Li_bonds(critic: dict) -> dict: """ Adjust C-Li coordinate bonding for Critic2 calculations. :param critic: Critic2 output dictionary :return: critic: modified Critic2 output dictionary """ for key in critic["bonding"]: if critic["bonding"][key]["atoms"] == ["Li", "C"] or critic["bonding"][key][ "atoms" ] == ["C", "Li"]: if ( critic["bonding"][key]["field"] <= 0.02 and critic["bonding"][key]["field"] > 0.012 and critic["bonding"][key]["distance"] < 2.5 ): critic["processed"]["bonds"].append( [int(entry) - 1 for entry in critic["bonding"][key]["atom_ids"]] ) return critic def _bonds_hybridization(nbo: dict[str, Any], index: int): """ Extract bonds from "hybridization_character" NBO output """ bonds = set() warnings = set() if len(nbo["hybridization_character"]) > index: for bond_ind in nbo["hybridization_character"][index].get("type", list()): if nbo["hybridization_character"][index]["type"][bond_ind] != "BD": continue from_ind = ( int(nbo["hybridization_character"][index]["atom 1 number"][bond_ind]) - 1 ) to_ind = ( int(nbo["hybridization_character"][index]["atom 2 number"][bond_ind]) - 1 ) if ( nbo["hybridization_character"][index]["atom 1 symbol"][bond_ind] in metals ): m_contrib = float( nbo["hybridization_character"][index]["atom 1 polarization"][ bond_ind ] ) elif ( nbo["hybridization_character"][index]["atom 2 symbol"][bond_ind] in metals ): m_contrib = float( nbo["hybridization_character"][index]["atom 2 polarization"][ bond_ind ] ) else: m_contrib = None if m_contrib is None: bond_type = "covalent" elif m_contrib >= 30.0: bond_type = "covalent" warnings.add("Contains covalent bond with metal atom") else: bond_type = "electrostatic" bonds.add((from_ind, to_ind, bond_type)) return bonds, warnings def _bonds_peturbation( nbo: dict[str, Any], index: int, poss_coord: dict[int | None, list[int | None]], energy_cutoff: float, metal_indices: list[int], ): """ Extract bonds from "perturbation_energy" NBO output """ bonds = set() # type: ignore # No metals, so don't need to use perturbation analysis to get bonds if len(metal_indices) == 0: return bonds if len(nbo["perturbation_energy"]) > index: for inter_ind in nbo["perturbation_energy"][index].get("donor type", list()): coord = False m_ind: int | None = None x_ind: int | None = None if ( int( nbo["perturbation_energy"][index]["acceptor atom 1 number"][ inter_ind ] ) - 1 in metal_indices ): if ( nbo["perturbation_energy"][index]["donor type"][inter_ind] == "LP" and nbo["perturbation_energy"][index]["acceptor type"][inter_ind] == "LV" ): coord = True m_ind = ( int( nbo["perturbation_energy"][index]["acceptor atom 1 number"][ inter_ind ] ) - 1 ) x_ind = ( int( nbo["perturbation_energy"][index]["donor atom 1 number"][ inter_ind ] ) - 1 ) elif ( nbo["perturbation_energy"][index]["donor type"][inter_ind] == "LP" and nbo["perturbation_energy"][index]["acceptor type"][inter_ind] == "RY*" ): coord = True m_ind = ( int( nbo["perturbation_energy"][index]["acceptor atom 1 number"][ inter_ind ] ) - 1 ) x_ind = ( int( nbo["perturbation_energy"][index]["donor atom 1 number"][ inter_ind ] ) - 1 ) elif ( nbo["perturbation_energy"][index]["donor atom 1 number"][inter_ind] - 1 in metal_indices ): if ( nbo["perturbation_energy"][index]["donor type"][inter_ind] == "LP" and nbo["perturbation_energy"][index]["acceptor type"][inter_ind] == "LV" ): coord = True m_ind = ( int( nbo["perturbation_energy"][index]["donor atom 1 number"][ inter_ind ] ) - 1 ) x_ind = ( int( nbo["perturbation_energy"][index]["acceptor atom 1 number"][ inter_ind ] ) - 1 ) if not coord: continue elif x_ind not in poss_coord[m_ind]: continue energy = float( nbo["perturbation_energy"][index]["perturbation energy"][inter_ind] ) if energy >= energy_cutoff: bonds.add((x_ind, m_ind, "electrostatic")) return bonds def nbo_molecule_graph(mol: Molecule, nbo: dict[str, Any]): """ Construct a molecule graph from NBO data. :param mol: molecule to be analyzed :param nbo: Output from NBO7 :return: """ mg = MoleculeGraph.from_empty_graph(mol) alpha_bonds, warnings = _bonds_hybridization(nbo, 1) beta_bonds, new_warnings = _bonds_hybridization(nbo, 3) warnings = warnings.union(new_warnings) distance_cutoff = 3.0 energy_cutoff = 3.0 metal_indices = [i for i, e in enumerate(mol.species) if str(e) in metals] poss_coord: dict[int | None, list[int | None]] = dict() dist_mat = mol.distance_matrix for i in metal_indices: poss_coord[i] = list() row = dist_mat[i] for j, val in enumerate(row): if i != j and val < distance_cutoff: poss_coord[i].append(j) new_alpha_bonds = _bonds_peturbation( nbo, 0, poss_coord, energy_cutoff, metal_indices ) alpha_bonds = alpha_bonds.union(new_alpha_bonds) if mol.spin_multiplicity != 1: new_beta_bonds = _bonds_peturbation( nbo, 1, poss_coord, energy_cutoff, metal_indices ) beta_bonds = beta_bonds.union(new_beta_bonds) sorted_alpha = set([tuple(sorted([a[0], a[1]])) for a in alpha_bonds]) sorted_beta = set([tuple(sorted([b[0], b[1]])) for b in beta_bonds]) if sorted_alpha != sorted_beta: warnings.add("Difference in bonding between alpha and beta electrons") for bond in alpha_bonds.union(beta_bonds): if (bond[0], bond[1]) not in mg.graph.edges() and ( bond[1], bond[0], ) not in mg.graph.edges(): if bond[0] < bond[1]: mg.add_edge(bond[0], bond[1], edge_properties={"type": bond[2]}) else: mg.add_edge(bond[1], bond[0], edge_properties={"type": bond[2]}) mg_copy = copy.deepcopy(mg) mg_copy.remove_nodes(metal_indices) try: if not nx.is_connected(mg_copy.graph.to_undirected()): warnings.add("Metal-centered complex") except nx.exception.NetworkXPointlessConcept: if len(mg.molecule) == 1: warnings.add("Single-atom; no bonds") return (mg, list(warnings)) class MoleculeBondingDoc(PropertyDoc): """Representation of molecular bonding.""" property_name: str = "bonding" molecule_graph: MoleculeGraphType = Field(..., description="Molecule graph") method: str = Field(..., description="Method used to compute molecule graph") bond_types: dict[str, list[float]] = Field( dict(), description="Dictionary of bond types to their length, e.g. C-O to " "a list of the lengths of C-O bonds in Angstrom.", ) bonds: list[tuple[int, int]] = Field( [], description="List of bonds in the form (a, b), where a and b are 0-indexed atom indices", ) bonds_nometal: list[tuple[int, int]] = Field( [], description="List of bonds in the form (a, b), where a and b are 0-indexed atom indices, " "with all metal ions removed", ) @classmethod def from_task( cls, task: TaskDocument, molecule_id: MPculeID, preferred_methods: list[str], deprecated: bool = False, **kwargs, ): # type: ignore[override] """ Determine bonding from a task document Method preferences are as follows: - NBO7 - OpenBabelNN + metal_edge_extender in pymatgen - Critic2 (really OpenBabelNN + metal_edge_extender + Critic2) :param task: task document from which bonding properties can be extracted :param molecule_id: MPculeID :param preferred_methods: list of methods; by default, NBO7, Critic2, and the combination of OpenBabelNN and metal_edge_extender in pymatgen, in that order :param kwargs: to pass to PropertyDoc :return: """ mg_made = False method = None warnings = list() if task.output.optimized_molecule is not None: mol = task.output.optimized_molecule else: mol = task.output.initial_molecule for m in preferred_methods: if mg_made: break if ( m == "nbo" and task.output.nbo is not None and ( task.orig["rem"].get("run_nbo6", False) or task.orig["rem"].get("nbo_external", False) ) ): method = "nbo" mg, warnings = nbo_molecule_graph(mol, task.output.nbo) mg_made = True elif m == "critic2" and task.critic2 is not None: method = "critic2" critic = fix_C_Li_bonds(task.critic2) critic_bonds = critic["processed"]["bonds"] mg = make_mol_graph(mol, critic_bonds=critic_bonds) mg_made = True else: method = "OpenBabelNN + metal_edge_extender" mg = make_mol_graph(mol) mg_made = True bonds = list() for bond in mg.graph.edges(): bonds.append(sorted([bond[0], bond[1]])) # Calculate bond lengths bond_types = dict() for u, v in mg.graph.edges(): species_u = str(mg.molecule.species[u]) species_v = str(mg.molecule.species[v]) if species_u < species_v: species = f"{species_u}-{species_v}" else: species = f"{species_v}-{species_u}" dist = mg.molecule.get_distance(u, v) if species not in bond_types: bond_types[species] = [dist] else: bond_types[species].append(dist) m_inds = [e for e in range(len(mol)) if str(mol.species[e]) in metals] bonds_nometal = list() for bond in bonds: if not any([m in bond for m in m_inds]): bonds_nometal.append(bond) id_string = f"bonding-{molecule_id}-{task.task_id}-{task.lot_solvent}-{method}" h = blake2b() h.update(id_string.encode("utf-8")) property_id = h.hexdigest() return super().from_molecule( meta_molecule=mol, property_id=property_id, molecule_id=molecule_id, level_of_theory=task.level_of_theory, solvent=task.solvent, lot_solvent=task.lot_solvent, method=method, warnings=warnings, molecule_graph=mg, bond_types=bond_types, bonds=bonds, bonds_nometal=bonds_nometal, origins=[MolPropertyOrigin(name="bonding", task_id=task.task_id)], # type: ignore[call-arg] deprecated=deprecated, **kwargs, ) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8228872 emmet_core-0.87.2/emmet/core/molecules/electric.py0000644000175100017510000001502515240717720021600 0ustar00runnerrunnerfrom hashlib import blake2b from pydantic import Field from emmet.core.math import Vector3D from emmet.core.molecules import MolPropertyOrigin from emmet.core.molecules.molecule_property import PropertyDoc from emmet.core.mpid import MPculeID from emmet.core.qchem.task import TaskDocument __author__ = "Evan Spotte-Smith " class ElectricMultipoleDoc(PropertyDoc): """Electric multipole (dipole, etc.) properties of a molecule""" property_name: str = "multipole_moments" total_dipole: float = Field( ..., description="Total molecular dipole moment (Debye)", ) dipole_moment: Vector3D = Field( ..., description="Molecular dipole moment vector (Debye)", ) resp_total_dipole: float | None = Field( None, description="Total dipole moment, calculated via restrained electrostatic potential (RESP) (Debye)", ) resp_dipole_moment: Vector3D | None = Field( None, description="Molecular dipole moment vector, calculated via RESP (Debye)", ) quadrupole_moment: dict[str, float] | None = Field( None, description="Quadrupole moment components (Debye Ang)", ) octopole_moment: dict[str, float] | None = Field( None, description="Octopole moment components (Debye Ang^2)", ) hexadecapole_moment: dict[str, float] | None = Field( None, description="Hexadecapole moment tensor components (Debye Ang^2)", ) @classmethod def from_task( cls, task: TaskDocument, molecule_id: MPculeID, deprecated: bool = False, **kwargs, ): # type: ignore[override] """ Construct an electric multipole doc """ if task.output.optimized_molecule is not None: mol = task.output.optimized_molecule else: mol = task.output.initial_molecule task_type = task.task_type.value # For SP and force calcs, dipoles stored in output dipoles = task.output.dipoles if dipoles is None: if task_type in ["Single Point", "Force"]: raise Exception("No dipoles in task!") total_dipole = None dipole_moment = None resp_total_dipole = None resp_dipole_moment = None else: total_dipole = dipoles.get("total") dipole_moment = dipoles.get("dipole") resp_total_dipole = dipoles.get("RESP_total") resp_dipole_moment = dipoles.get("RESP_dipole") # Look for multipole moment in calcs_reversed calcs_reversed = task.calcs_reversed # FFOpt/FFTSOpt: grab final multipole moments from final optimization if task_type in [ "Frequency Flattening Geometry Optimization", "Frequency Flattening Transition State Geometry Optimization", ]: calc = calcs_reversed[1] grab_index = -1 # Frequency calc: grab first multipole moments # This is necessary if finite difference is used, in which case multipole moments will be generated for each # perturbation elif task_type == "Frequency Analysis": calc = calcs_reversed[0] grab_index = 0 # Otherwise, grab final available multipole moments else: calc = calcs_reversed[0] grab_index = -1 dipoles = calc.get("dipoles", dict()) if total_dipole is None: total_dipole = dipoles.get("total") if isinstance(total_dipole, list): total_dipole = total_dipole[grab_index] if dipole_moment is None: dipole_moment = dipoles.get("dipole") if isinstance(dipole_moment, list) and len(dipole_moment) == 0: dipole_moment = None elif isinstance(dipole_moment[0], list): dipole_moment = dipole_moment[grab_index] if resp_total_dipole is None: resp_total_dipole = dipoles.get("RESP_total") if isinstance(resp_total_dipole, list): resp_total_dipole = resp_total_dipole[grab_index] if resp_dipole_moment is None: resp_dipole_moment = dipoles.get("RESP_dipole") if isinstance(resp_dipole_moment, list) and len(resp_dipole_moment) == 0: resp_dipole_moment = None elif isinstance(resp_dipole_moment[0], list): resp_dipole_moment = resp_dipole_moment[grab_index] if total_dipole is None or dipole_moment is None: raise Exception("Total dipole or dipole moment vector missing!") multipoles = calc.get("multipoles", dict()) quadrupoles = multipoles.get("quadrupole") octopoles = multipoles.get("octopole") hexadecapoles = multipoles.get("hexadecapole") quadrupole_moment = None octopole_moment = None hexadecapole_moment = None if quadrupoles is not None: if isinstance(quadrupoles, dict): quadrupole_moment = quadrupoles else: quadrupole_moment = quadrupoles[grab_index] if octopoles is not None: if isinstance(octopoles, dict): octopole_moment = octopoles else: octopole_moment = octopoles[grab_index] if hexadecapoles is not None: if isinstance(hexadecapoles, dict): hexadecapole_moment = hexadecapoles else: hexadecapole_moment = hexadecapoles[grab_index] id_string = f"electricmultipole-{molecule_id}-{task.task_id}-{task.lot_solvent}" h = blake2b() h.update(id_string.encode("utf-8")) property_id = h.hexdigest() return super().from_molecule( meta_molecule=mol, property_id=property_id, molecule_id=molecule_id, level_of_theory=task.level_of_theory, solvent=task.solvent, lot_solvent=task.lot_solvent, molecule=mol, total_dipole=total_dipole, dipole_moment=dipole_moment, resp_total_dipole=resp_total_dipole, resp_dipole_moment=resp_dipole_moment, quadrupole_moment=quadrupole_moment, octopole_moment=octopole_moment, hexadecapole_moment=hexadecapole_moment, warnings=list(), # No warnings currently available origins=[MolPropertyOrigin(name="multipole_moments", task_id=task.task_id)], # type: ignore[call-arg] deprecated=deprecated, **kwargs, ) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8228872 emmet_core-0.87.2/emmet/core/molecules/metal_binding.py0000644000175100017510000004113315240717720022601 0ustar00runnerrunner# mypy: ignore-errors from __future__ import annotations from typing import TYPE_CHECKING from hashlib import blake2b from pydantic import BaseModel, Field from emmet.core.io.pymatgen import Element, Species from scipy.stats import describe from emmet.core.molecules.atomic import PartialChargesDoc, PartialSpinsDoc from emmet.core.molecules.bonds import MoleculeBondingDoc from emmet.core.molecules.molecule_property import PropertyDoc from emmet.core.molecules.thermo import MoleculeThermoDoc from emmet.core.mpid import MPculeID from emmet.core.qchem.molecule import MoleculeDoc from emmet.core.utils import arrow_incompatible if TYPE_CHECKING: from typing_extensions import Self __author__ = "Evan Spotte-Smith " METAL_BINDING_METHODS = ["nbo", "mulliken-OB-mee"] @arrow_incompatible class MetalBindingData(BaseModel): """ Metal binding information for one metal or ion in a molecule """ metal_molecule_id: MPculeID = Field( ..., description="The MPculeID of the metal atom or ion being bound" ) nometal_molecule_id: MPculeID = Field( ..., description="The MPculeID of the molecule with the metal atom/ion removed" ) metal_index: int | None = Field( None, description="Index of the metal in this Molecule (in case of a molecule with multiple identical " "metal atoms/ions)", ) metal_element: str | Species | Element | None = Field( None, description="The metal bound to the molecule" ) metal_partial_charge: float | None = Field( None, description="The exact calculated partial charge of the metal" ) metal_partial_spin: float | None = Field( None, description="The exact calculated partial spin on the metal" ) metal_assigned_charge: float | None = Field( None, description="The integral charge assigned to this metal based on partial charge/spin data", ) metal_assigned_spin: float | None = Field( None, description="The integral spin multiplicity assigned to this metal based on partial spin data", ) number_coordinate_bonds: int | None = Field( None, description="The number of atoms neighboring the metal atom or ion of interest", ) coordinating_atoms: list[str] | None = Field( None, description="The elements/species coordinating the metal." ) coordinate_bond_lengths: dict[str, dict[str, float | list[float]]] | None = Field( None, description="Bond lengths and statistics broken down by the coordinating atoms", ) binding_energy: float | None = Field( None, description="The electronic energy change (∆E) of binding (units: eV)" ) binding_enthalpy: float | None = Field( None, description="The enthalpy change (∆H) of binding (units: eV)" ) binding_entropy: float | None = Field( None, description="The entropy change (∆S) of binding (units: eV/K)" ) binding_free_energy: float | None = Field( None, description="The free energy change (∆G) of binding (units: eV)" ) metal_thermo_property_id: str | None = Field( None, description="ID of MoleculeThermoDoc used to obtain the thermochemistry of the metal atom/ion", ) nometal_thermo_property_id: str | None = Field( None, description="ID of MoleculeThermoDoc used to obtain the thermochemistry of of the molecule with the " "metal atom/ion removed", ) def get_id_string(self): """ Return a string representation of the binding data for this atom in the molecule """ id_str = f"{self.metal_element}-{self.metal_index}-" id_str += f"{self.metal_thermo_property_id}-{self.nometal_thermo_property_id}" return id_str @arrow_incompatible class MetalBindingDoc(PropertyDoc): """Metal binding properties of a molecule""" property_name: str = "metal_binding" method: str = Field( ..., description="Method used to determine the charge, spin, and coordination environment of a metal", ) binding_partial_charges_property_id: str | None = Field( None, description="ID of PartialChargesDoc used to estimate metal charge", ) binding_partial_spins_property_id: str | None = Field( None, description="ID of PartialSpinsDoc used to estimate metal spin", ) binding_partial_charges_lot_solvent: str | None = Field( None, description="Combination of level of theory and solvent used to calculate atomic partial charges", ) binding_partial_spins_lot_solvent: str | None = Field( None, description="Combination of level of theory and solvent used to calculate atomic partial spins", ) binding_charge_spin_method: str | None = Field( None, description="The method used for partial charges and spins (must be the same).", ) binding_bonding_property_id: str | None = Field( None, description="ID of MoleculeBondingDoc used to detect bonding in this molecule", ) binding_bonding_lot_solvent: str | None = Field( None, description="Combination of level of theory and solvent used to determine the coordination environment " "of the metal atom or ion", ) binding_bonding_method: str | None = Field( None, description="The method used for to define bonding." ) binding_thermo_property_id: str | None = Field( None, description="ID of MoleculeThermoDoc used to obtain this molecule's thermochemistry", ) binding_thermo_lot_solvent: str | None = Field( None, description="Combination of level of theory and solvent used for uncorrected thermochemistry", ) binding_thermo_correction_lot_solvent: str | None = Field( None, description="Combination of level of theory and solvent used to correct the electronic energy", ) binding_thermo_combined_lot_solvent: str | None = Field( None, description="Combination of level of theory and solvent used for molecular thermochemistry, combining " "both the frequency calculation and (potentially) the single-point energy correction.", ) binding_data: list[MetalBindingData] | None = Field( None, description="Binding data for each metal atom or ion in the molecule" ) @classmethod def from_docs( cls: Self, method: str, metal_indices: list[int], base_molecule_doc: MoleculeDoc, partial_charges: PartialChargesDoc, partial_spins: PartialSpinsDoc | None, bonding: MoleculeBondingDoc, base_thermo: MoleculeThermoDoc, metal_thermo: dict[int, MoleculeThermoDoc], nometal_thermo: dict[int, MoleculeThermoDoc], **kwargs, ) -> Self: # type: ignore[override] """ Construct a document describing the binding energy of a metal atom or ion to a molecule from MoleculeThermoDocs (for thermochemistry), PartialChargesDocs and PartialSpinsDocs (to assess the oxidation state and spin state of the metal), and MoleculeBondingDocs (to assess the coordination environment of the metal). :param method: What method was used to construct this document? :param metal_indices: List of indices in the Molecule corresponding to metals :param base_molecule_doc: MoleculeDoc used for basic ID, species, structure information :param partial_charges: PartialChargesDoc used to determine the oxidation state of the metal of interest :param partial_spins: PartialSpinsDoc used to determine the spin state of the metal of interest :param bonding: MoleculeBondingDoc used to determine the coordination environment :param base_thermo: MoleculeThermoDoc for the molecule of interest. :param metal_thermo: dict[int, MoleculeThermodoc], where the keys are the indices of the metal ions or atoms in this molecule and the values are the MoleculeThermoDocs corresponding to the appropriate metal (with the correct charge and spin) :param nometal_thermo: dict[int, MoleculeThermodoc], where the keys are the indices of the metal ions or atoms in this molecule and the values are the MoleculeThermoDocs corresponding to the appropriate metal (with the correct charge and spin) :param kwargs: To be passed to PropertyDoc :return: """ # Sanity checks for i, doc in metal_thermo.items(): if not doc.lot_solvent == base_thermo.lot_solvent: raise ValueError( "All MoleculeThermoDocs must use the same lot_solvent!" ) for i, doc in nometal_thermo.items(): if not doc.lot_solvent == base_thermo.lot_solvent: raise ValueError( "All MoleculeThermoDocs must use the same lot_solvent!" ) if partial_spins is not None: if not ( partial_charges.method == partial_spins.method and partial_charges.lot_solvent == partial_spins.lot_solvent ): raise ValueError( "Partial charges and partial spins must use the same method and lot_solvent!" ) if not ( base_thermo.solvent == partial_charges.solvent and base_thermo.solvent == bonding.solvent ): raise ValueError("All documents must use the same solvent!") partial_charges_property_id = partial_charges.property_id partial_charges_lot_solvent = partial_charges.lot_solvent if partial_spins is not None: partial_spins_property_id = partial_spins.property_id partial_spins_lot_solvent = partial_spins.lot_solvent else: partial_spins_property_id = None partial_spins_lot_solvent = None bonding_property_id = bonding.property_id bonding_lot_solvent = bonding.lot_solvent thermo_property_id = base_thermo.property_id thermo_lot_solvent = base_thermo.lot_solvent thermo_correction_lot_solvent = base_thermo.correction_lot_solvent thermo_combined_lot_solvent = base_thermo.combined_lot_solvent level_of_theory = base_thermo.level_of_theory solvent = base_thermo.solvent lot_solvent = base_thermo.lot_solvent molecule = base_molecule_doc.molecule dist_mat = molecule.distance_matrix species = base_molecule_doc.species binding_data = list() for metal_index in metal_indices: metal_element = species[metal_index] this_metal_thermo = metal_thermo.get(metal_index) this_nometal_thermo = nometal_thermo.get(metal_index) if this_metal_thermo is None or this_nometal_thermo is None: continue # Partial charges and spins charge = partial_charges.partial_charges[metal_index] if partial_spins is not None: spin = partial_spins.partial_spins[metal_index] else: spin = None # Coordinate bonding relevant_bonds = [b for b in bonding.bonds if metal_index in b] number_coordinate_bonds = len(relevant_bonds) # Binding is not meaningful if there are no coordinate bonds if number_coordinate_bonds == 0: return None coordinating_atoms = list() coordinate_bond_lengths = dict() # type: ignore for bond in relevant_bonds: other_ids = [i for i in bond if i != metal_index] # Edge case - possible if we (later) account for 3-center bonds or hyperbonds if len(other_ids) != 1: continue other_index = other_ids[0] other_species = str(species[other_index]) coordinating_atoms.append(other_species) if other_species not in coordinate_bond_lengths: coordinate_bond_lengths[other_species] = { "lengths": list(), } this_length = dist_mat[metal_index][other_index] coordinate_bond_lengths[other_species]["lengths"].append(this_length) for s, data in coordinate_bond_lengths.items(): stats = describe(data["lengths"], nan_policy="omit") coordinate_bond_lengths[s]["min"] = stats.minmax[0] coordinate_bond_lengths[s]["max"] = stats.minmax[1] coordinate_bond_lengths[s]["mean"] = stats.mean coordinate_bond_lengths[s]["variance"] = stats.variance coordinating_atoms = sorted(coordinating_atoms) # Thermo binding_e = None binding_h = None binding_s = None binding_g = None if this_metal_thermo is not None and this_nometal_thermo is not None: thermos = [base_thermo, this_metal_thermo, this_nometal_thermo] binding_e = ( thermos[1].electronic_energy + thermos[2].electronic_energy ) - thermos[0].electronic_energy if all([x.total_enthalpy is not None for x in thermos]): binding_h = ( thermos[1].total_enthalpy + thermos[2].total_enthalpy ) - thermos[0].total_enthalpy if all([x.total_entropy is not None for x in thermos]): binding_s = ( thermos[1].total_entropy + thermos[2].total_entropy ) - thermos[0].total_entropy if all([x.free_energy is not None for x in thermos]): binding_g = ( thermos[1].free_energy + thermos[2].free_energy ) - thermos[0].free_energy binding_data.append( MetalBindingData( metal_molecule_id=this_metal_thermo.molecule_id, nometal_molecule_id=this_nometal_thermo.molecule_id, metal_index=metal_index, metal_element=metal_element, metal_partial_charge=charge, metal_partial_spin=spin, metal_assigned_charge=this_metal_thermo.charge, metal_assigned_spin=this_metal_thermo.spin_multiplicity, number_coordinate_bonds=number_coordinate_bonds, coordinating_atoms=coordinating_atoms, coordinate_bond_lengths=coordinate_bond_lengths, binding_energy=binding_e, binding_enthalpy=binding_h, binding_entropy=binding_s, binding_free_energy=binding_g, metal_thermo_property_id=this_metal_thermo.property_id, nometal_thermo_property_id=this_nometal_thermo.property_id, ) ) # Property ID id_string = f"binding-{base_molecule_doc.molecule_id}-{lot_solvent}-" id_string += f"{partial_charges_property_id}-{partial_spins_property_id}-" id_string += f"{bonding.property_id}-" id_string += f"{thermo_property_id}" for d in binding_data: id_string += f"-{d.get_id_string()}" h = blake2b() h.update(id_string.encode("utf-8")) property_id = h.hexdigest() return super().from_molecule( meta_molecule=base_molecule_doc.molecule, property_id=property_id, molecule_id=base_molecule_doc.molecule_id, level_of_theory=level_of_theory, solvent=solvent, lot_solvent=lot_solvent, method=method, binding_partial_charges_property_id=partial_charges_property_id, binding_partial_spins_property_id=partial_spins_property_id, binding_partial_charges_lot_solvent=partial_charges_lot_solvent, binding_partial_spins_lot_solvent=partial_spins_lot_solvent, binding_charge_spin_method=partial_charges.method, binding_bonding_property_id=bonding_property_id, binding_bonding_lot_solvent=bonding_lot_solvent, binding_bonding_method=bonding.method, binding_thermo_property_id=thermo_property_id, binding_thermo_lot_solvent=thermo_lot_solvent, binding_thermo_correction_lot_solvent=thermo_correction_lot_solvent, binding_thermo_combined_lot_solvent=thermo_combined_lot_solvent, binding_data=binding_data, deprecated=False, origins=[], **kwargs, ) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8228872 emmet_core-0.87.2/emmet/core/molecules/molecule_property.py0000644000175100017510000000553015240717720023557 0ustar00runnerrunner"""Core definition of a Materials Document""" # mypy: ignore-errors from __future__ import annotations from datetime import datetime from typing import TYPE_CHECKING from pydantic import Field from emmet.core.io.pymatgen import Molecule from emmet.core.molecules import MolPropertyOrigin from emmet.core.mpid import MPculeID from emmet.core.qchem.calc_types import LevelOfTheory from emmet.core.structure import MoleculeMetadata from emmet.core.utils import utcnow if TYPE_CHECKING: from typing_extensions import Self __author__ = "Evan Spotte-Smith " class PropertyDoc(MoleculeMetadata): """ Base model definition for any singular molecule property. This may contain any amount of molecule metadata for the purpose of search This is intended to be inherited and extended not used directly """ property_name: str property_id: str = Field( ..., description="The unique identifier of this property document." ) molecule_id: MPculeID = Field( ..., description="The ID of the molecule, used as a reference across property documents." "This comes in the form of an MPculeID (or appropriately formatted string)", ) deprecated: bool = Field( ..., description="Whether this property document is deprecated.", ) deprecation_reasons: list[str] | None = Field( None, description="List of deprecation tags detailing why this document isn't valid", ) level_of_theory: LevelOfTheory | None = Field( None, description="Level of theory used to generate this property document." ) solvent: str | None = Field( None, description="String representation of the solvent " "environment used to generate this property document.", ) lot_solvent: str | None = Field( None, description="String representation of the level of theory and solvent " "environment used to generate this property document.", ) last_updated: datetime = Field( description="Timestamp for the most recent calculation update for this property", default_factory=utcnow, ) origins: list[MolPropertyOrigin] = Field( [], description="Dictionary for tracking the provenance of properties" ) warnings: list[str] = Field([], description="Any warnings related to this property") @classmethod def from_molecule( # type: ignore[override] cls: Self, meta_molecule: Molecule, property_id: str, molecule_id: MPculeID, **kwargs, ) -> Self: """ Builds a molecule document using the minimal amount of information """ return super().from_molecule( meta_molecule=meta_molecule, property_id=property_id, molecule_id=molecule_id, **kwargs, ) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8228872 emmet_core-0.87.2/emmet/core/molecules/orbitals.py0000644000175100017510000011301615240717720021624 0ustar00runnerrunnerfrom __future__ import annotations import re from hashlib import blake2b from typing import TYPE_CHECKING from monty.json import MSONable from pydantic import Field from emmet.core.molecules import MolPropertyOrigin from emmet.core.molecules.molecule_property import PropertyDoc from emmet.core.mpid import MPculeID from emmet.core.qchem.task import TaskDocument from emmet.core.utils import set_msonable_type_adapter if TYPE_CHECKING: from typing import Any __author__ = "Evan Spotte-Smith " @set_msonable_type_adapter class NaturalPopulation(MSONable): def __init__( self, atom_index: int, core_electrons: float, valence_electrons: float, rydberg_electrons: float, total_electrons: float, ): """ Basic description of an atomic electron population. :param atom_index (int): :param core_electrons (float): Number of core electrons on this atom :param valence_electrons (float): Number of valence electrons on this atom :param rydberg_electrons (float): Number of Rydberg electrons on this atom :param total_electrons (float): Total number of electrons on this atom """ self.atom_index = int(atom_index) self.core_electrons = float(core_electrons) self.valence_electrons = float(valence_electrons) self.rydberg_electrons = float(rydberg_electrons) self.total_electrons = float(total_electrons) @set_msonable_type_adapter class LonePair(MSONable): def __init__( self, index: int, number: int, atom_index: int, s_character: float, p_character: float, d_character: float, f_character: float, occupancy: float, type_code: str, ): """ Basic description of a lone pair (LP) natural bonding orbital. :param index (int): Lone pair index from NBO. 1-indexed :param number (int): Another index, for cases where there are multiple lone pairs on an atom. 1-indexed :param atom_index (int): 0-indexed :param s_character (float): What fraction of this orbital is s-like in nature. :param p_character (float): What fraction of this orbital is p-like in nature. :param d_character (float): What fraction of this orbital is d-like in nature. :param f_character (float): What fraction of this orbital is f-like in nature. :param occupancy (float): Total electron occupancy of this lone pair :param type_code (str): Description of this lone pair (ex: "LV" for "lone valence") """ self.index = int(index) self.number = int(number) self.atom_index = int(atom_index) self.s_character = float(s_character) self.p_character = float(p_character) self.d_character = float(d_character) self.f_character = float(f_character) self.occupancy = float(occupancy) self.type_code = type_code @set_msonable_type_adapter class Bond(MSONable): def __init__( self, index: int, number: int, atom1_index: int, atom2_index: int, atom1_s_character: float, atom2_s_character: float, atom1_p_character: float, atom2_p_character: float, atom1_d_character: float, atom2_d_character: float, atom1_f_character: float, atom2_f_character: float, atom1_polarization: float, atom2_polarization: float, atom1_polarization_coeff: float, atom2_polarization_coeff: float, occupancy: float, type_code: str, ): """ Basic description of a bond (BD) natural bonding orbital. :param index: Bond orbital index from NBO. 1-indexed. :param number: Another index, for cases where there are multiple bonds between two atoms. 1-indexed :param atom1_index: Index of first atom involved in this orbital. 0-indexed :param atom2_index: Index of second atom involved in this orbital. 0-indexed :param atom1_s_character: What fraction of this orbital comes from atom 1 s electrons :param atom2_s_character: What fraction of this orbital comes from atom 2 s electrons :param atom1_p_character: What fraction of this orbital comes from atom 1 p electrons :param atom2_p_character: What fraction of this orbital comes from atom 2 p electrons :param atom1_d_character: What fraction of this orbital comes from atom 1 d electrons :param atom2_d_character: What fraction of this orbital comes from atom 2 d electrons :param atom1_f_character: What fraction of this orbital comes from atom 1 f electrons :param atom2_f_character: What fraction of this orbital comes from atom 2 f electrons :param atom1_polarization: Percentage of polarization from atom 1 :param atom2_polarization: Percentage of polarization from atom 2 :param atom1_polarization_coeff: Polarization coefficient of atom 1 :param atom2_polarization_coeff: Polarization coefficient of atom 2 :param occupancy: Total electron occupancy of this orbital :param type_code: Description of this bonding orbital (ex: BD for bonding, BD* for anti-bonding) """ self.index = int(index) self.number = int(number) self.atom1_index = int(atom1_index) self.atom2_index = int(atom2_index) self.atom1_s_character = float(atom1_s_character) self.atom2_s_character = float(atom2_s_character) self.atom1_p_character = float(atom1_p_character) self.atom2_p_character = float(atom2_p_character) self.atom1_d_character = float(atom1_d_character) self.atom2_d_character = float(atom2_d_character) self.atom1_f_character = float(atom1_f_character) self.atom2_f_character = float(atom2_f_character) self.atom1_polarization = float(atom1_polarization) self.atom2_polarization = float(atom2_polarization) self.atom1_polarization_coeff = float(atom1_polarization_coeff) self.atom2_polarization_coeff = float(atom2_polarization_coeff) self.occupancy = float(occupancy) self.type_code = type_code @set_msonable_type_adapter class ThreeCenterBond(MSONable): def __init__( self, index: int, number: int, atom1_index: int, atom2_index: int, atom3_index: int, atom1_s_character: float, atom2_s_character: float, atom3_s_character: float, atom1_p_character: float, atom2_p_character: float, atom3_p_character: float, atom1_d_character: float, atom2_d_character: float, atom3_d_character: float, atom1_f_character: float, atom2_f_character: float, atom3_f_character: float, atom1_polarization: float, atom2_polarization: float, atom3_polarization: float, atom1_polarization_coeff: float, atom2_polarization_coeff: float, atom3_polarization_coeff: float, occupancy: float, type_code: str, ): """ Basic description of a three-center bond (3C) natural bonding orbital. :param index: Bond orbital index from NBO. 1-indexed. :param number: Another index, for cases where there are multiple bonds between two atoms. 1-indexed :param atom1_index: Index of first atom involved in this orbital. 0-indexed :param atom2_index: Index of second atom involved in this orbital. 0-indexed :param atom3_index: Index of third atom involved in this orbital. 0-indexed :param atom1_s_character: What fraction of this orbital comes from atom 1 s electrons :param atom2_s_character: What fraction of this orbital comes from atom 2 s electrons :param atom3_s_character: What fraction of this orbital comes from atom 3 s electrons :param atom1_p_character: What fraction of this orbital comes from atom 1 p electrons :param atom2_p_character: What fraction of this orbital comes from atom 2 p electrons :param atom3_p_character: What fraction of this orbital comes from atom 3 p electrons :param atom1_d_character: What fraction of this orbital comes from atom 1 d electrons :param atom2_d_character: What fraction of this orbital comes from atom 2 d electrons :param atom3_d_character: What fraction of this orbital comes from atom 3 d electrons :param atom1_f_character: What fraction of this orbital comes from atom 1 f electrons :param atom2_f_character: What fraction of this orbital comes from atom 2 f electrons :param atom3_f_character: What fraction of this orbital comes from atom 3 f electrons :param atom1_polarization: Percentage of polarization from atom 1 :param atom2_polarization: Percentage of polarization from atom 2 :param atom3_polarization: Percentage of polarization from atom 3 :param atom1_polarization_coeff: Polarization coefficient of atom 1 :param atom2_polarization_coeff: Polarization coefficient of atom 2 :param atom3_polarization_coeff: Polarization coefficient of atom 3 :param occupancy: Total electron occupancy of this orbital :param type_code: Description of this bonding orbital (ex: 3C for three-center bond, 3C* for three-center anti-bond) """ self.index = int(index) self.number = int(number) self.atom1_index = int(atom1_index) self.atom2_index = int(atom2_index) self.atom3_index = int(atom3_index) self.atom1_s_character = float(atom1_s_character) self.atom2_s_character = float(atom2_s_character) self.atom3_s_character = float(atom3_s_character) self.atom1_p_character = float(atom1_p_character) self.atom2_p_character = float(atom2_p_character) self.atom3_p_character = float(atom3_p_character) self.atom1_d_character = float(atom1_d_character) self.atom2_d_character = float(atom2_d_character) self.atom3_d_character = float(atom3_d_character) self.atom1_f_character = float(atom1_f_character) self.atom2_f_character = float(atom2_f_character) self.atom3_f_character = float(atom3_f_character) self.atom1_polarization = float(atom1_polarization) self.atom2_polarization = float(atom2_polarization) self.atom3_polarization = float(atom3_polarization) self.atom1_polarization_coeff = float(atom1_polarization_coeff) self.atom2_polarization_coeff = float(atom2_polarization_coeff) self.atom3_polarization_coeff = float(atom3_polarization_coeff) self.occupancy = float(occupancy) self.type_code = type_code @set_msonable_type_adapter class Hyperbond(MSONable): def __init__( self, index: int, atom1_index: int, atom2_index: int, atom3_index: int, fraction_12: float, fraction_23: float, occupancy: float, bond_index_12: int, lp_index_3: int, hybrid_index_1: int, hybrid_index_2: int, hybrid_index_3: int, ): """ Basic description of a three-center, four-electron hyperbond (3CHB). :param index: Bond orbital index from NBO. 1-indexed. :param atom1_index: Index of first atom involved in this orbital. 0-indexed :param atom2_index: Index of second atom involved in this orbital. 0-indexed :param atom3_index: Index of third atom involved in this orbital. 0-indexed :param fraction_12: What fraction of this hyperbond comes from the bond between atoms 1 and 2 :param fraction_23: What fraction of this hyperbond comes from the bond between atoms 2 and 3 :param occupancy: Total electron occupancy of this orbital :param bond_index_12: Index of the bond orbital between atoms used 1 and 2 used to make this hyperbond :param lp_index_3: Index of the LP on atom 3 used to make this hyperbond :hybrid_index_1: Index of the natural hybrid orbital (NHO) on atom 1 used to make this hyperbond :hybrid_index_2: Index of the natural hybrid orbital (NHO) on atom 2 used to make this hyperbond :hybrid_index_3: Index of the natural hybrid orbital (NHO) on atom 3 used to make this hyperbond """ self.index = int(index) self.atom1_index = int(atom1_index) self.atom2_index = int(atom2_index) self.atom3_index = int(atom3_index) self.fraction_12 = float(fraction_12) self.fraction_23 = float(fraction_23) self.occupancy = float(occupancy) self.bond_index_12 = int(bond_index_12) self.lp_index_3 = int(lp_index_3) self.hybrid_index_1 = int(hybrid_index_1) self.hybrid_index_2 = int(hybrid_index_2) self.hybrid_index_3 = int(hybrid_index_3) @set_msonable_type_adapter class Interaction(MSONable): def __init__( self, perturbation_energy: float, energy_difference: float, fock_element: float, donor_index: int, acceptor_index: int, donor_type: str, acceptor_type: str, donor_atom1_index: int, acceptor_atom1_index: int, donor_atom2_index: int | None = None, acceptor_atom2_index: int | None = None, donor_atom3_index: int | None = None, acceptor_atom3_index: int | None = None, ): """ Description of an interaction between two orbitals :param perturbation_energy: second-order perturbation energy, in kcal/mol :param energy_difference: difference in energy between the interacting orbitals, in Ha :param fock_element: Fock matrix element corresponding to this interaction, in a.u. :donor_index: Index of the donating orbital :acceptor_index: Index of the accepting orbital :donor_type: Type code for the donating orbital :acceptor_type: Type code for the accepting orbital :donor_atom1_index: Index of the first atom involved in the donor orbital :acceptor_atom1_index: Index of the first atom involved in the acceptor orbital :donor_atom2_index: Index of the second atom involved in the donor orbital :acceptor_atom2_index: Index of the second atom involved in the acceptor orbital :donor_atom3_index: Index of the third atom involved in the donor orbital :acceptor_atom3_index: Index of the third atom involved in the acceptor orbital """ self.donor_index = int(donor_index) self.acceptor_index = int(acceptor_index) self.donor_type = donor_type self.acceptor_type = acceptor_type if isinstance(donor_atom2_index, int): if donor_atom2_index < 0: donor2 = None else: donor2 = int(donor_atom2_index) else: donor2 = None if isinstance(acceptor_atom2_index, int): if acceptor_atom2_index < 0: acceptor2 = None else: acceptor2 = int(acceptor_atom2_index) else: acceptor2 = None if isinstance(donor_atom3_index, int): # Not sure if this is actual possible if donor_atom3_index < 0: donor3 = None else: donor3 = int(donor_atom3_index) else: donor3 = None if isinstance(acceptor_atom3_index, int): # Similarly, not sure if this is important if acceptor_atom3_index < 0: acceptor3 = None else: acceptor3 = int(acceptor_atom3_index) else: acceptor3 = None self.donor_atom_indices = [ index for index in [int(donor_atom1_index), donor2, donor3] if index is not None ] self.acceptor_atom_indices = [ index for index in [int(acceptor_atom1_index), acceptor2, acceptor3] if index is not None ] self.perturbation_energy = float(perturbation_energy) self.energy_difference = float(energy_difference) self.fock_element = float(fock_element) def as_dict(self): return { "@module": self.__class__.__module__, "@class": self.__class__.__name__, "donor_index": self.donor_index, "acceptor_index": self.acceptor_index, "donor_type": self.donor_type, "acceptor_type": self.acceptor_type, "donor_atom_indices": self.donor_atom_indices, "acceptor_atom_indices": self.acceptor_atom_indices, "perturbation_energy": self.perturbation_energy, "energy_difference": self.energy_difference, "fock_element": self.fock_element, } @classmethod def from_dict(cls, d): donor_inds = d["donor_atom_indices"] acceptor_inds = d["acceptor_atom_indices"] if len(donor_inds) < 2: donor_inds += [None, None] elif len(donor_inds) < 3: donor_inds += [None] if len(acceptor_inds) < 2: acceptor_inds += [None, None] elif len(acceptor_inds) < 3: acceptor_inds += [None] return cls( d["perturbation_energy"], d["energy_difference"], d["fock_element"], d["donor_index"], d["acceptor_index"], d["donor_type"], d["acceptor_type"], donor_inds[0], acceptor_inds[0], donor_inds[1], acceptor_inds[1], donor_inds[2], acceptor_inds[2], ) class OrbitalDoc(PropertyDoc): property_name: str = "natural bonding orbitals" # Always populated - closed-shell and open-shell open_shell: bool = Field( ..., description="Is this molecule open-shell (spin multiplicity != 1)?" ) nbo_population: list[NaturalPopulation] = Field( ..., description="Natural electron populations of the molecule" ) # Populated for closed-shell molecules nbo_lone_pairs: list[LonePair] | None = Field( None, description="Lone pair orbitals of a closed-shell molecule" ) nbo_bonds: list[Bond] | None = Field( None, description="Bond-like orbitals of a closed-shell molecule" ) nbo_three_center_bonds: list[ThreeCenterBond] | None = Field( None, description="Three-center bond-like orbitals of a closed-shell molecule" ) nbo_hyperbonds: list[Hyperbond] | None = Field( None, description="3-center hyperbond-like orbitals of a closed-shell molecule" ) nbo_interactions: list[Interaction] | None = Field( None, description="Orbital-orbital interactions of a closed-shell molecule" ) # Populated for open-shell molecules alpha_population: list[NaturalPopulation] | None = Field( None, description="Natural electron populations of the alpha electrons of an " "open-shell molecule", ) beta_population: list[NaturalPopulation] | None = Field( None, description="Natural electron populations of the beta electrons of an " "open-shell molecule", ) alpha_lone_pairs: list[LonePair] | None = Field( None, description="Alpha electron lone pair orbitals of an open-shell molecule" ) beta_lone_pairs: list[LonePair] | None = Field( None, description="Beta electron lone pair orbitals of an open-shell molecule" ) alpha_bonds: list[Bond] | None = Field( None, description="Alpha electron bond-like orbitals of an open-shell molecule" ) beta_bonds: list[Bond] | None = Field( None, description="Beta electron bond-like orbitals of an open-shell molecule" ) alpha_three_center_bonds: list[ThreeCenterBond] | None = Field( None, description="Alpha electron three-center bond-like orbitals of an open-shell molecule", ) beta_three_center_bonds: list[ThreeCenterBond] | None = Field( None, description="Beta electron three-center bond-like orbitals of an open-shell molecule", ) alpha_hyperbonds: list[Hyperbond] | None = Field( None, description="Alpha electron hyperbond-like orbitals of an open-shell molecule", ) beta_hyperbonds: list[Hyperbond] | None = Field( None, description="Beta electron hyperbond-like orbitals of an open-shell molecule", ) alpha_interactions: list[Interaction] | None = Field( None, description="Alpha electron orbital-orbital interactions of an open-shell molecule", ) beta_interactions: list[Interaction] | None = Field( None, description="Beta electron orbital-orbital interactions of an open-shell molecule", ) @staticmethod def get_populations(nbo: dict[str, Any], indices: list[int]): """ Helper function to extract natural population information from NBO output :param nbo: Dictionary of NBO output data :param indices: Data subsets from which to extract natural populations :return: population_sets (list of lists of NaturalPopulation) """ population_sets = list() for pop_ind in indices: pops = nbo["natural_populations"][pop_ind] population = list() for ind, atom_num in pops["No"].items(): population.append( NaturalPopulation( atom_num - 1, pops["Core"][ind], pops["Valence"][ind], pops["Rydberg"][ind], pops["Total"][ind], ) ) population_sets.append(population) return population_sets @staticmethod def get_lone_pairs(nbo: dict[str, Any], indices: list[int]): """ Helper function to extract lone pair information from NBO output :param nbo: Dictionary of NBO output data :param indices: Data subsets from which to extract lone pair information :return: lone_pairs (list of LonePairs) """ lone_pair_sets = list() for lp_ind in indices: lps = nbo["hybridization_character"][lp_ind] lone_pairs = list() for ind, orb_ind in lps.get("bond index", dict()).items(): this_lp = LonePair( orb_ind, lps["orbital index"][ind], int(lps["atom number"][ind]) - 1, lps["s"][ind], lps["p"][ind], lps["d"][ind], lps["f"][ind], lps["occupancy"][ind], lps["type"][ind], ) lone_pairs.append(this_lp) lone_pair_sets.append(lone_pairs) return lone_pair_sets @staticmethod def get_bonds(nbo: dict[str, Any], indices: list[int]): """ Helper function to extract bonding information from NBO output :param nbo: Dictionary of NBO output data :param indices: Data subsets from which to extract bonds :return: bonds (list of Bonds) """ bond_sets = list() for bd_ind in indices: bds = nbo["hybridization_character"][bd_ind] bonds = list() for ind, orb_ind in bds.get("bond index", dict()).items(): this_bond = Bond( orb_ind, bds["orbital index"][ind], int(bds["atom 1 number"][ind]) - 1, int(bds["atom 2 number"][ind]) - 1, bds["atom 1 s"][ind], bds["atom 2 s"][ind], bds["atom 1 p"][ind], bds["atom 2 p"][ind], bds["atom 1 d"][ind], bds["atom 2 d"][ind], bds["atom 1 f"][ind], bds["atom 2 f"][ind], bds["atom 1 polarization"][ind], bds["atom 2 polarization"][ind], bds["atom 1 pol coeff"][ind], bds["atom 2 pol coeff"][ind], bds["occupancy"][ind], bds["type"][ind], ) bonds.append(this_bond) bond_sets.append(bonds) return bond_sets @staticmethod def get_three_center_bonds(nbo: dict[str, Any], indices: list[int]): """ Helper function to extract bonding information from NBO output :param nbo: Dictionary of NBO output data :param indices: Data subsets from which to extract bonds :return: threec_bonds (list of ThreeCenterBonds) """ if len(indices) == 0: return None threec_sets = list() for tc_ind in indices: tcs = nbo["hybridization_character"][tc_ind] threecs = list() for ind, orb_ind in tcs.get("bond index", dict()).items(): this_threec = ThreeCenterBond( orb_ind, tcs["orbital index"][ind], int(tcs["atom 1 number"][ind]) - 1, int(tcs["atom 2 number"][ind]) - 1, int(tcs["atom 3 number"][ind]) - 1, tcs["atom 1 s"][ind], tcs["atom 2 s"][ind], tcs["atom 3 s"][ind], tcs["atom 1 p"][ind], tcs["atom 2 p"][ind], tcs["atom 3 p"][ind], tcs["atom 1 d"][ind], tcs["atom 2 d"][ind], tcs["atom 3 d"][ind], tcs["atom 1 f"][ind], tcs["atom 2 f"][ind], tcs["atom 3 f"][ind], tcs["atom 1 polarization"][ind], tcs["atom 2 polarization"][ind], tcs["atom 3 polarization"][ind], tcs["atom 1 pol coeff"][ind], tcs["atom 2 pol coeff"][ind], tcs["atom 3 pol coeff"][ind], tcs["occupancy"][ind], tcs["type"][ind], ) threecs.append(this_threec) threec_sets.append(threecs) return threec_sets @staticmethod def get_hyperbonds(nbo: dict[str, Any], indices: list[int]): """ Helper function to extract hyperbond information form NBO output :param nbo: Dictionary of NBO output data :param indices: Data subsets from which to extract interactions :return: interactions (list of Hyperbonds) """ hyperbond_sets = list() # No hyperbonds present if "hyperbonds" not in nbo or len(indices) == 0 or len(nbo["hyperbonds"]) == 0: return None for hb_ind in indices: # For other types of bonds, all indices must be present # That is, we always expect one set of orbitals for closed-shell molecules, # And we always expect two sets (alpha and beta) for open-shell molecules # Hyperbonds are different. # Since these are (up to) 4-electron configurations, you could have a partially occupied # hyperbond with only two electrons # And in this case, for an open-shell molecule, you may only have one set of hyperbonds hyperbonds = list() try: hbds = nbo["hyperbonds"][hb_ind] except IndexError: hbds = dict() for ind, orb_ind in hbds.get("hyperbond index", dict()).items(): this_hyperbond = Hyperbond( orb_ind, int(hbds["bond atom 1 index"][ind]) - 1, int(hbds["bond atom 2 index"][ind]) - 1, int(hbds["bond atom 3 index"][ind]) - 1, hbds["pctA-B"][ind], hbds["pctB-C"][ind], hbds["occ"][ind], hbds["BD(A-B)"][ind], hbds["LP(C)"][ind], hbds["h(A)"][ind], hbds["h(B)"][ind], hbds["h(C)"][ind], ) hyperbonds.append(this_hyperbond) hyperbond_sets.append(hyperbonds) return hyperbond_sets @staticmethod def get_interactions(nbo: dict[str, Any], indices: list[int]): """ Helper function to extract orbital interaction information from NBO output :param nbo: Dictionary of NBO output data :param indices: Data subsets from which to extract interactions :return: interactions (list of Interactions) """ interaction_sets = list() for pert_ind in indices: perts = nbo["perturbation_energy"][pert_ind] interactions = list() for ind in perts.get("donor bond index", list()): donor_atom2 = perts["donor atom 2 number"].get(ind) if donor_atom2 == "info_is_from_3C": # There's a pretty horrible hack in the current pymatgen NBO parsers # To prevent a dramatic increase in storage space, atom indices and element symbols for 3C bonds # are stored using the same keys as those used for lone pairs and conventional two-center bonds donor_atom1_index = ( int(re.sub(r"\D", "", perts["donor atom 1 symbol"][ind])) - 1 ) donor_atom2_index = ( int(re.sub(r"\D", "", perts["donor atom 1 number"][ind])) - 1 ) donor_atom3_index = ( int(re.sub(r"\D", "", perts["donor atom 2 symbol"][ind])) - 1 ) else: donor_atom1_index = int(perts["donor atom 1 number"][ind]) - 1 donor_atom3_index = None if donor_atom2 is None: donor_atom2_index = None else: donor_atom2_index = int(donor_atom2) - 1 acceptor_atom2 = perts["acceptor atom 2 number"].get(ind) if acceptor_atom2 == "info_is_from_3C": acceptor_atom1_index = ( int(re.sub(r"\D", "", perts["acceptor atom 1 symbol"][ind])) - 1 ) acceptor_atom2_index = ( int(re.sub(r"\D", "", perts["acceptor atom 1 number"][ind])) - 1 ) acceptor_atom3_index = ( int(re.sub(r"\D", "", perts["acceptor atom 2 symbol"][ind])) - 1 ) else: acceptor_atom1_index = int(perts["acceptor atom 1 number"][ind]) - 1 acceptor_atom3_index = None if acceptor_atom2 is None: acceptor_atom2_index = None else: acceptor_atom2_index = int(acceptor_atom2) - 1 this_inter = Interaction( perts["perturbation energy"][ind], perts["energy difference"][ind], perts["fock matrix element"][ind], int(perts["donor bond index"][ind]), int(perts["acceptor bond index"][ind]), perts["donor type"][ind], perts["acceptor type"][ind], donor_atom1_index, acceptor_atom1_index, donor_atom2_index, acceptor_atom2_index, donor_atom3_index, acceptor_atom3_index, ) interactions.append(this_inter) interaction_sets.append(interactions) return interaction_sets @classmethod def from_task( cls, task: TaskDocument, molecule_id: MPculeID, deprecated: bool = False, **kwargs, ): # type: ignore[override] """ Construct an orbital document from a task :param task: document from which vibrational properties can be extracted :param molecule_id: MPculeID :param deprecated: bool. Is this document deprecated? :param kwargs: to pass to PropertyDoc :return: """ if task.output.nbo is None: raise ValueError("No NBO output in task {}!".format(task.task_id)) elif not ( task.orig["rem"].get("run_nbo6", False) or task.orig["rem"].get("nbo_external", False) ): raise ValueError("Only NBO7 is allowed!") nbo = task.output.nbo if task.output.optimized_molecule is not None: mol = task.output.optimized_molecule else: mol = task.output.initial_molecule spin = mol.spin_multiplicity # Closed-shell if int(spin) == 1: pops_inds = [0] lps_inds = [0] bds_inds = [1] if "hyperbonds" in nbo: # New parser, with three-center bonds tc_inds = [2] else: # Old parser - no three-center bonds tc_inds = list() hbds_inds = [0] perts_inds = [0] # Open-shell else: pops_inds = [0, 1, 2] if len(nbo.get("hybridization_character", [0, 0, 0, 0])) == 4: # Old parser - no three-center bonds lps_inds = [0, 2] bds_inds = [1, 3] tc_inds = list() else: # New parser - with three-center bonds lps_inds = [0, 3] bds_inds = [1, 4] tc_inds = [2, 5] hbds_inds = [0, 1] perts_inds = [0, 1] for dset, inds in [ ("natural_populations", pops_inds), ("hybridization_character", lps_inds + bds_inds + tc_inds), ("perturbation_energy", perts_inds), ]: if len(nbo.get(dset, list())) <= inds[-1]: return population_sets = cls.get_populations(nbo, pops_inds) lone_pair_sets = cls.get_lone_pairs(nbo, lps_inds) bond_sets = cls.get_bonds(nbo, bds_inds) threec_sets = cls.get_three_center_bonds(nbo, tc_inds) hyperbond_sets = cls.get_hyperbonds(nbo, hbds_inds) interaction_sets = cls.get_interactions(nbo, perts_inds) if not ( task.orig["rem"].get("run_nbo6") or task.orig["rem"].get("nbo_external", False) ): warnings = ["Using NBO5"] else: warnings = list() id_string = ( f"natural_bonding_orbitals-{molecule_id}-{task.task_id}-{task.lot_solvent}" ) h = blake2b() h.update(id_string.encode("utf-8")) property_id = h.hexdigest() if int(spin) == 1: if threec_sets is None: threec = None else: threec = threec_sets[0] if hyperbond_sets is None: hyperbond = None else: hyperbond = hyperbond_sets[0] return super().from_molecule( meta_molecule=mol, property_id=property_id, molecule_id=molecule_id, level_of_theory=task.level_of_theory, solvent=task.solvent, lot_solvent=task.lot_solvent, open_shell=False, nbo_population=population_sets[0], nbo_lone_pairs=lone_pair_sets[0], nbo_bonds=bond_sets[0], nbo_three_center_bonds=threec, nbo_hyperbonds=hyperbond, nbo_interactions=interaction_sets[0], origins=[ MolPropertyOrigin( # type: ignore[call-arg] name="natural_bonding_orbitals", task_id=task.task_id ) ], warnings=warnings, deprecated=deprecated, **kwargs, ) else: if threec_sets is None: threec_alpha = None threec_beta = None else: threec_alpha = threec_sets[0] threec_beta = threec_sets[1] if hyperbond_sets is None: hyperbond_alpha = None hyperbond_beta = None else: hyperbond_alpha = hyperbond_sets[0] hyperbond_beta = hyperbond_sets[1] return super().from_molecule( meta_molecule=mol, property_id=property_id, molecule_id=molecule_id, level_of_theory=task.level_of_theory, solvent=task.solvent, lot_solvent=task.lot_solvent, open_shell=True, nbo_population=population_sets[0], alpha_population=population_sets[1], beta_population=population_sets[2], alpha_lone_pairs=lone_pair_sets[0], beta_lone_pairs=lone_pair_sets[1], alpha_bonds=bond_sets[0], beta_bonds=bond_sets[1], alpha_three_center_bonds=threec_alpha, beta_three_center_bonds=threec_beta, alpha_hyperbonds=hyperbond_alpha, beta_hyperbonds=hyperbond_beta, alpha_interactions=interaction_sets[0], beta_interactions=interaction_sets[1], origins=[ MolPropertyOrigin( # type: ignore[call-arg] name="natural bonding orbitals", task_id=task.task_id ) ], warnings=warnings, deprecated=deprecated, **kwargs, ) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8228872 emmet_core-0.87.2/emmet/core/molecules/redox.py0000644000175100017510000002413115240717720021125 0ustar00runnerrunnerfrom __future__ import annotations from hashlib import blake2b from typing import TYPE_CHECKING from pydantic import Field from emmet.core.molecules import MolPropertyOrigin from emmet.core.molecules.molecule_property import PropertyDoc from emmet.core.molecules.thermo import MoleculeThermoDoc, get_free_energy from emmet.core.mpid import MPID, MPculeID from emmet.core.qchem.molecule import MoleculeDoc from emmet.core.qchem.task import TaskDocument if TYPE_CHECKING: from typing import Any __author__ = "Evan Spotte-Smith " reference_potential = 4.44 class RedoxDoc(PropertyDoc): """ Molecular properties related to reduction and oxidation, including vertical ionization energies and electron affinities, as well as reduction and oxidation potentials """ property_name: str = "redox" base_property_id: str = Field( description="Property ID for the thermodynamic data of the " "base molecule" ) electron_affinity: float | None = Field( None, description="Vertical electron affinity (units: eV)" ) ea_task_id: MPID | None = Field( None, description="Task ID for the electron affinity calculation" ) ionization_energy: float | None = Field( None, description="Vertical ionization energy (units: eV)" ) ie_task_id: MPID | None = Field( None, description="Task ID for the ionization energy calculation" ) reduction_energy: float | None = Field( None, description="Adiabatic electronic energy of reduction (units: eV)" ) reduction_free_energy: float | None = Field( None, description="Adiabatic free energy of reduction (units: eV)" ) red_molecule_id: MPculeID | None = Field( None, description="Molecule ID for adiabatic reduction" ) red_property_id: str | None = Field( None, description="Property ID for the thermodynamic data of the " "reduced molecule", ) oxidation_energy: float | None = Field( None, description="Adiabatic electronic energy of oxidation (units: eV)" ) oxidation_free_energy: float | None = Field( None, description="Adiabatic free energy of oxidation (units: eV)" ) ox_molecule_id: MPculeID | None = Field( None, description="Molecule ID for adiabatic oxidation" ) ox_property_id: str | None = Field( None, description="Property ID for the thermodynamic data of the " "oxidized molecule", ) reduction_potential: float | None = Field( None, description="Reduction potential referenced to the standard hydrogen electrode (SHE) (units: V)", ) oxidation_potential: float | None = Field( None, description="Oxidation potential referenced to the standard hydrogen electrode (SHE) (units: V)", ) @classmethod def _g_or_e(cls, entry: dict[str, Any]) -> float: """ Single atoms may not have free energies like more complex molecules do. This function returns the free energy of a TaskDocument entry if possible, and otherwise returns the electronic energy. :param entry: dict representation of a TaskDocument :return: """ try: result = get_free_energy( entry["output"]["final_energy"], entry["output"]["enthalpy"], entry["output"]["entropy"], ) # Single atoms won't have enthalpy and entropy except TypeError: result = entry["output"]["final_energy"] return result @classmethod def from_docs( cls, base_molecule_doc: MoleculeDoc, base_thermo_doc: MoleculeThermoDoc, red_doc: MoleculeThermoDoc | None = None, ox_doc: MoleculeThermoDoc | None = None, ea_doc: TaskDocument | None = None, ie_doc: TaskDocument | None = None, deprecated: bool = False, **kwargs, ): # type: ignore[override] """ Construct a document describing molecular redox properties from MoleculeThermoDocs (for adiabatic redox potentials and thermodynamics) and TaskDocs (for vertical ionization energies and electron affinities) :param base_molecule_doc: MoleculeDoc of interest :param base_thermo_doc: MoleculeThermoDoc for the molecule of interest. All properties will be calculated in reference to this document :param red_doc: MoleculeThermoDoc for the reduced molecule. This molecule will have the same (covalent) bonding as base_thermo_doc but will differ in charge by -1 :param ox_doc: MoleculeThermoDoc for the oxidized molecule. This molecule will have the same (covalent) bonding as the base_thermo_doc but will differ in charge by +1 :param ea_doc: A TaskDocument performed at the same structure as base_thermo_doc, but at a charge that differs by -1. This document will be used to calculate the electron affinity of the molecule :param ie_doc: A TaskDocument performed at the same structure as base_thermo_doc, but at a charge that differs by +1. This document will be used to calculate the ionization energy of the molecule :param kwargs: To be passed to PropertyDoc :return: """ if all([x is None for x in [red_doc, ox_doc, ea_doc, ie_doc]]): # No redox properties can be extracted return None base_has_g = base_thermo_doc.free_energy is not None base_property_id = base_thermo_doc.property_id red_molecule_id = None red_property_id = None reduction_energy = None reduction_free_energy = None reduction_potential = None ox_molecule_id = None ox_property_id = None oxidation_energy = None oxidation_free_energy = None oxidation_potential = None ea_task_id = None electron_affinity = None ie_task_id = None ionization_energy = None id_string = ( f"redox-{base_molecule_doc.molecule_id}-{base_thermo_doc.lot_solvent}-" f"{base_thermo_doc.property_id}" ) origins = list() # Adiabatic reduction properties if red_doc is not None: red_molecule_id = red_doc.molecule_id red_property_id = red_doc.property_id id_string += f"-{red_doc.property_id}" reduction_energy = ( red_doc.electronic_energy - base_thermo_doc.electronic_energy ) if ( base_has_g and red_doc.free_energy is not None and base_thermo_doc.free_energy is not None ): reduction_free_energy = ( red_doc.free_energy - base_thermo_doc.free_energy ) else: reduction_free_energy = None red = reduction_free_energy or reduction_energy reduction_potential = -1 * red - reference_potential # Adiabatic oxidation properties if ox_doc is not None: ox_molecule_id = ox_doc.molecule_id ox_property_id = ox_doc.property_id id_string += f"-{ox_doc.property_id}" oxidation_energy = ( ox_doc.electronic_energy - base_thermo_doc.electronic_energy ) if ( base_has_g and ox_doc.free_energy is not None and base_thermo_doc.free_energy is not None ): oxidation_free_energy = ox_doc.free_energy - base_thermo_doc.free_energy else: oxidation_free_energy = None ox = oxidation_free_energy or oxidation_energy oxidation_potential = ox - reference_potential # Electron affinity if ea_doc is not None: ea_task_id = ea_doc.task_id id_string += f"-{ea_task_id}" origins.append( MolPropertyOrigin(name="electron_affinity", task_id=ea_task_id) # type: ignore[call-arg] ) electron_affinity = ( ( ea_doc.output.final_energy * 27.2114 - base_thermo_doc.electronic_energy ) if ea_doc.output.final_energy else None ) # Ionization energy if ie_doc is not None: ie_task_id = ie_doc.task_id id_string += f"-{ie_task_id}" origins.append( MolPropertyOrigin(name="ionization_energy", task_id=ie_task_id) # type: ignore[call-arg] ) ionization_energy = ( ( ie_doc.output.final_energy * 27.2114 - base_thermo_doc.electronic_energy ) if ie_doc.output.final_energy else None ) h = blake2b() h.update(id_string.encode("utf-8")) property_id = h.hexdigest() return super().from_molecule( meta_molecule=base_molecule_doc.molecule, property_id=property_id, molecule_id=base_molecule_doc.molecule_id, base_property_id=base_property_id, level_of_theory=base_thermo_doc.level_of_theory, solvent=base_thermo_doc.solvent, lot_solvent=base_thermo_doc.lot_solvent, red_molecule_id=red_molecule_id, red_property_id=red_property_id, reduction_energy=reduction_energy, reduction_free_energy=reduction_free_energy, reduction_potential=reduction_potential, ox_molecule_id=ox_molecule_id, ox_property_id=ox_property_id, oxidation_energy=oxidation_energy, oxidation_free_energy=oxidation_free_energy, oxidation_potential=oxidation_potential, ea_task_id=ea_task_id, electron_affinity=electron_affinity, ie_task_id=ie_task_id, ionization_energy=ionization_energy, deprecated=deprecated, origins=origins, **kwargs, ) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8228872 emmet_core-0.87.2/emmet/core/molecules/summary.py0000644000175100017510000005326115240717720021507 0ustar00runnerrunnerfrom __future__ import annotations from hashlib import blake2b from typing import TYPE_CHECKING from pydantic import BaseModel, Field from emmet.core.molecules.metal_binding import MetalBindingData from emmet.core.molecules.molecule_property import PropertyDoc from emmet.core.mpid import MPID, MPculeID from emmet.core.qchem.calc_types import CalcType, LevelOfTheory, TaskType from emmet.core.types.enums import ValueEnum from emmet.core.types.pymatgen_types.structure_adapter import MoleculeType from emmet.core.utils import arrow_incompatible if TYPE_CHECKING: from typing import Any from typing_extensions import Self __author__ = "Evan Spotte-Smith " class HasProps(ValueEnum): """ Enum of possible hasprops values. """ molecules = "molecules" bonding = "bonding" metal_binding = "metal_binding" multipole_moments = "multipole_moments" orbitals = "orbitals" partial_charges = "partial_charges" partial_spins = "partial_spins" redox = "redox" thermo = "thermo" vibration = "vibration" class ThermoComposite(BaseModel): """ Summary information obtained from MoleculeThermoDocs """ property_id: str | None = Field( None, description="Property ID map for this MoleculeThermoDoc", ) level_of_theory: str | None = Field( None, description="Level of theory for this MoleculeThermoDoc.", ) electronic_energy: float | None = Field( None, description="Electronic energy of the molecule (units: eV)" ) zero_point_energy: float | None = Field( None, description="Zero-point energy of the molecule (units: eV)" ) total_enthalpy: float | None = Field( None, description="Total enthalpy of the molecule at 298.15K (units: eV)" ) total_entropy: float | None = Field( None, description="Total entropy of the molecule at 298.15K (units: eV/K)" ) free_energy: float | None = Field( None, description="Gibbs free energy of the molecule at 298.15K (units: eV)" ) class VibrationComposite(BaseModel): """ Summary information obtained from VibrationDocs """ property_id: str | None = Field( None, description="Property ID for this VibrationDoc.", ) level_of_theory: str | None = Field( None, description="Level of theory for this VibrationDoc.", ) frequencies: list[float] | None = Field( None, description="List of molecular vibrational frequencies" ) class OrbitalComposite(BaseModel): """ Summary information obtained from OrbitalDocs """ property_id: str | None = Field( None, description="Property ID for this OrbitalDoc.", ) level_of_theory: str | None = Field( None, description="Level of theory for this OrbitalDoc.", ) open_shell: bool | None = Field( None, description="Is this molecule open-shell (spin multiplicity != 1)?" ) class PartialChargesComposite(BaseModel): """ Summary information obtained from PartialChargesDocs """ property_id: str | None = Field( None, description="Property ID for this PartialChargesDoc.", ) level_of_theory: str | None = Field( None, description="Level of theory for this PartialChargesDoc.", ) partial_charges: list[float] | None = Field( None, description="Atomic partial charges for the molecule", ) class PartialSpinsComposite(BaseModel): """ Summary information obtained from PartialSpinsDocs """ property_id: str | None = Field( None, description="Property ID for this PartialSpinsDoc.", ) level_of_theory: str | None = Field( None, description="Level of theory for this PartialSpinsDoc.", ) partial_spins: list[float] | None = Field( None, description="Atomic partial spins for the molecule", ) class BondingComposite(BaseModel): """ Summary information obtained from MoleculeBondingDocs """ property_id: str | None = Field( None, description="Property ID for this MoleculeBondingDoc.", ) level_of_theory: str | None = Field( None, description="Level of theory for this MoleculeBondingDoc.", ) bond_types: dict[str, list[float]] | None = Field( None, description="Dictionaries of bond types to their length, e.g. C-O to a list of the lengths of C-O bonds in " "Angstrom.", ) bonds: list[tuple[int, int]] | None = Field( None, description="List of bonds. Each bond takes the form (a, b), where a and b are 0-indexed atom indices", ) bonds_nometal: list[tuple[int, int]] | None = Field( None, description="List of bonds with all metal ions removed. Each bond takes the form in the form (a, b), where a " "and b are 0-indexed atom indices.", ) class MultipolesComposite(BaseModel): """ Summary information obtained from ElectricMultipoleDocs """ property_id: str | None = Field( None, description="Property ID for this ElectricMultipoleDoc.", ) level_of_theory: str | None = Field( None, description="Level of theory for this ElectricMultipoleDoc.", ) total_dipole: float | None = Field( None, description="Total molecular dipole moment (Debye)", ) resp_total_dipole: float | None = Field( None, description="Total dipole moment, calculated via restrained electrostatic potential (RESP) (Debye)", ) class RedoxComposite(BaseModel): """ Summary information obtained from RedoxDocs """ property_id: str | None = Field(None, description="Property ID for this RedoxDoc.") level_of_theory: str | None = Field( None, description="Level of theory for this RedoxDoc.", ) electron_affinity: float | None = Field( None, description="Vertical electron affinity in eV" ) ea_task_id: MPID | None = Field( None, description="Molecule ID for electron affinity" ) ionization_energy: float | None = Field( None, description="Vertical ionization energy in eV" ) ie_task_id: MPID | None = Field( None, description="Molecule ID for ionization energy" ) reduction_free_energy: float | None = Field( None, description="Adiabatic free energy of reduction" ) red_molecule_id: MPculeID | None = Field( None, description="Molecule ID for adiabatic reduction" ) oxidation_free_energy: float | None = Field( None, description="Adiabatic free energy of oxidation" ) ox_molecule_id: MPculeID | None = Field( None, description="Molecule ID for adiabatic oxidation" ) reduction_potential: float | None = Field( None, description="Reduction potential referenced to the standard hydrogen electrode (SHE) (units: V)", ) oxidation_potential: float | None = Field( None, description="Oxidation potential referenced to the standard hydrogen electrode (SHE) (units: V)", ) @arrow_incompatible class MetalBindingComposite(BaseModel): """ Summary information obtained from MetalBindingDocs """ property_id: str | None = Field( None, description="Property ID for this MetalBindingDoc." ) level_of_theory: str | None = Field( None, description="Level of theory for this MetalBindingDoc.", ) binding_partial_charges_property_id: str | None = Field( None, description="ID of PartialChargesDoc used to estimate metal charge", ) binding_partial_spins_property_id: str | None = Field( None, description="ID of PartialSpinsDoc used to estimate metal spin", ) binding_partial_charges_lot_solvent: str | None = Field( None, description="Combination of level of theory and solvent used to calculate atomic partial charges", ) binding_partial_spins_lot_solvent: str | None = Field( None, description="Combination of level of theory and solvent used to calculate atomic partial spins", ) binding_charge_spin_method: str | None = Field( None, description="The method used for partial charges and spins (must be the same).", ) binding_bonding_property_id: str | None = Field( None, description="ID of MoleculeBondingDoc used to detect bonding in this molecule", ) binding_bonding_lot_solvent: str | None = Field( None, description="Combination of level of theory and solvent used to determine the coordination environment " "of the metal atom or ion", ) binding_bonding_method: str | None = Field( None, description="The method used for to define bonding." ) binding_thermo_property_id: str | None = Field( None, description="ID of MoleculeThermoDoc used to obtain this molecule's thermochemistry", ) binding_thermo_lot_solvent: str | None = Field( None, description="Combination of level of theory and solvent used for uncorrected thermochemistry", ) binding_thermo_correction_lot_solvent: str | None = Field( None, description="Combination of level of theory and solvent used to correct the electronic energy", ) binding_thermo_combined_lot_solvent: str | None = Field( None, description="Combination of level of theory and solvent used for molecular thermochemistry, combining " "both the frequency calculation and (potentially) the single-point energy correction.", ) binding_data: list[MetalBindingData] | None = Field( None, description="Binding data for each metal atom or ion in the molecule" ) @arrow_incompatible class MoleculeSummaryDoc(PropertyDoc): """ Summary information about molecules and their properties, useful for searching. """ property_name: str = "summary" # molecules molecules: dict[str, MoleculeType] = Field( ..., description="The lowest energy optimized structures for this molecule for each solvent.", ) molecule_levels_of_theory: dict[str, str] | None = Field( None, description="Level of theory used to optimize the best molecular structure for each solvent.", ) species_hash: str | None = Field( None, description="Weisfeiler Lehman (WL) graph hash using the atom species as the graph " "node attribute.", ) coord_hash: str | None = Field( None, description="Weisfeiler Lehman (WL) graph hash using the atom coordinates as the graph " "node attribute.", ) inchi: str | None = Field( None, description="International Chemical Identifier (InChI) for this molecule" ) inchi_key: str | None = Field( None, description="Standardized hash of the InChI for this molecule" ) task_ids: list[MPID] = Field( [], title="Calculation IDs", description="List of Calculation IDs associated with this molecule.", ) similar_molecules: list[MPculeID] = Field( [], description="IDs associated with similar molecules" ) constituent_molecules: list[MPculeID] = Field( [], description="IDs of associated MoleculeDocs used to construct this molecule.", ) unique_calc_types: list[CalcType] | None = Field( None, description="Collection of all unique calculation types used for this molecule", ) unique_task_types: list[TaskType] | None = Field( None, description="Collection of all unique task types used for this molecule", ) unique_levels_of_theory: list[LevelOfTheory] | None = Field( None, description="Collection of all unique levels of theory used for this molecule", ) unique_solvents: list[str] | None = Field( None, description="Collection of all unique solvents (solvent parameters) used for this molecule", ) unique_lot_solvents: list[str] | None = Field( None, description="Collection of all unique combinations of level of theory and solvent used for this molecule", ) # Properties thermo: dict[str, ThermoComposite] | None = Field( None, description="A summary of thermodynamic data available for this molecule, organized by solvent", ) vibration: dict[str, VibrationComposite] | None = Field( None, description="A summary of the vibrational data available for this molecule, organized by solvent", ) orbitals: dict[str, OrbitalComposite] | None = Field( None, description="A summary of the orbital (NBO) data available for this molecule, organized by solvent", ) partial_charges: dict[str, dict[str, PartialChargesComposite]] | None = Field( None, description="A summary of the partial charge data available for this molecule, organized by solvent and by " "method", ) partial_spins: dict[str, dict[str, PartialSpinsComposite]] | None = Field( None, description="A summary of the partial spin data available for this molecule, organized by solvent and by " "method", ) bonding: dict[str, dict[str, BondingComposite]] | None = Field( None, description="A summary of the bonding data available for this molecule, organized by solvent and by method", ) multipole_moments: dict[str, MultipolesComposite] | None = Field( None, description="A summary of the electric multipole data available for this molecule, organized by solvent", ) redox: dict[str, RedoxComposite] | None = Field( None, description="A summary of the redox data available for this molecule, organized by solvent", ) metal_binding: dict[str, dict[str, MetalBindingComposite]] | None = Field( None, description="A summary of the metal binding data available for this molecule, organized by solvent and by " "method", ) # has props has_props: dict[str, bool] | None = Field( None, description="Properties available for this molecule", ) @classmethod def from_docs(cls, molecule_id: MPculeID, docs: dict[str, Any]) -> Self: """Converts a bunch of property docs into a SummaryDoc""" doc = _copy_from_docs(**docs) if len(doc["has_props"]) == 0: raise ValueError("Missing minimal properties!") id_string = f"summary-{molecule_id}" h = blake2b() h.update(id_string.encode("utf-8")) property_id = h.hexdigest() doc["property_id"] = property_id return MoleculeSummaryDoc(molecule_id=molecule_id, **doc) # Key mapping summary_fields: dict[str, list] = { HasProps(k).value: v for k, v in { "molecules": [ "charge", "spin_multiplicity", "natoms", "elements", "nelements", "composition", "composition_reduced", "formula_alphabetical", "chemsys", "symmetry", "molecules", "deprecated", "task_ids", "species_hash", "coord_hash", "inchi", "inchi_key", "unique_calc_types", "unique_task_types", "unique_levels_of_theory", "unique_solvents", "unique_lot_solvents", "similar_molecules", "constituent_molecules", "molecule_levels_of_theory", ], "thermo": [ "electronic_energy", "zero_point_energy", "total_enthalpy", "total_entropy", "free_energy", ], "vibration": [ "frequencies", ], "orbitals": [ "open_shell", ], "partial_charges": ["partial_charges"], "partial_spins": ["partial_spins"], "bonding": ["bond_types", "bonds", "bonds_nometal"], "multipole_moments": [ "total_dipole", "resp_total_dipole", ], "redox": [ "electron_affinity", "ea_task_id", "ionization_energy", "ie_task_id", "reduction_free_energy", "red_molecule_id", "oxidation_free_energy", "ox_molecule_id", "reduction_potential", "oxidation_potential", ], "metal_binding": [ "binding_partial_charges_property_id", "binding_partial_spins_property_id", "binding_partial_charges_lot_solvent", "binding_partial_spins_lot_solvent", "binding_charge_spin_method", "binding_bonding_property_id", "binding_bonding_lot_solvent", "binding_bonding_method", "binding_thermo_property_id", "binding_thermo_lot_solvent", "binding_thermo_correction_lot_solvent", "binding_thermo_combined_lot_solvent", "binding_data", ], }.items() } def _copy_from_docs( molecules: dict[str, Any], partial_charges: dict[str, dict[str, dict[str, Any]]] | None = None, partial_spins: dict[str, dict[str, dict[str, Any]]] | None = None, bonding: dict[str, dict[str, dict[str, Any]]] | None = None, metal_binding: dict[str, dict[str, dict[str, Any]]] | None = None, multipole_moments: dict[str, dict[str, Any]] | None = None, orbitals: dict[str, dict[str, Any]] | None = None, redox: dict[str, dict[str, Any]] | None = None, thermo: dict[str, dict[str, Any]] | None = None, vibration: dict[str, dict[str, Any]] | None = None, ): """Helper function to cut down documents to composite models and then combine to create a MoleculeSummaryDoc""" has_props: dict[str, bool] = {str(val.value): False for val in HasProps} # type: ignore[attr-defined] d: dict[str, Any] = {"has_props": has_props, "origins": []} # Molecules is special because there should only ever be one # MoleculeDoc for a given molecule # There are not multiple MoleculeDocs for different solvents # NB: mypy misfires on enums. Using the following syntax works fine with mypy, # but HasProps.molecules.value can raise errors about attributes being defined d["has_props"][HasProps("molecules").value] = True for copy_key in summary_fields[HasProps("molecules").value]: d[copy_key] = molecules[copy_key] doc_type_mapping = { HasProps(k).value: v for k, v in { "partial_charges": (partial_charges, PartialChargesComposite), "partial_spins": (partial_spins, PartialSpinsComposite), "bonding": (bonding, BondingComposite), "metal_binding": (metal_binding, MetalBindingComposite), "multipole_moments": (multipole_moments, MultipolesComposite), "orbitals": (orbitals, OrbitalComposite), "redox": (redox, RedoxComposite), "thermo": (thermo, ThermoComposite), "vibration": (vibration, VibrationComposite), }.items() } by_method = { HasProps(k).value for k in { "partial_charges", "partial_spins", "bonding", "metal_binding", } } # Function to grab the keys and put them in the root doc for doc_key in summary_fields: sub_docs, target_type = doc_type_mapping.get(doc_key, (None, None)) # No information for this particular set of properties # Shouldn't happen, but can if sub_docs is None or target_type is None: composite_docs = None else: composite_docs = dict() # type: ignore[var-annotated] if isinstance(sub_docs, dict) and len(sub_docs) > 0: d["has_props"][doc_key] = True if doc_key in by_method: for solvent, solv_entries in sub_docs.items(): composite_docs[solvent] = dict() for method, entry in solv_entries.items(): composite_docs[solvent][method] = dict() for copy_key in summary_fields[doc_key]: composite_docs[solvent][method][copy_key] = entry.get( copy_key ) composite_docs[solvent][method]["property_id"] = entry.get( "property_id" ) composite_docs[solvent][method]["level_of_theory"] = ( entry.get("level_of_theory") ) # Convert to appropriate BaseModel composite_docs[solvent][method] = target_type( **composite_docs[solvent][method] ) else: for solvent, entry in sub_docs.items(): composite_docs[solvent] = dict() # type: ignore[assignment] for copy_key in summary_fields[doc_key]: composite_docs[solvent][copy_key] = entry.get(copy_key) # type: ignore[index] composite_docs[solvent]["property_id"] = entry.get( # type: ignore[index] "property_id" ) composite_docs[solvent]["level_of_theory"] = entry.get( # type: ignore[index] "level_of_theory" ) # Convert to appropriate BaseModel composite_docs[solvent] = target_type(**composite_docs[solvent]) # type: ignore[arg-type] d[doc_key] = composite_docs return d ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8228872 emmet_core-0.87.2/emmet/core/molecules/thermo.py0000644000175100017510000002340315240717720021303 0ustar00runnerrunnerfrom hashlib import blake2b from pydantic import Field from emmet.core.molecules import MolPropertyOrigin from emmet.core.molecules.molecule_property import PropertyDoc from emmet.core.mpid import MPculeID from emmet.core.qchem.calc_types import LevelOfTheory from emmet.core.qchem.task import TaskDocument __author__ = "Evan Spotte-Smith " def get_free_energy(energy, enthalpy, entropy, temperature=298.15, convert_energy=True): """ Helper function to calculate Gibbs free energy from electronic energy, enthalpy, and entropy :param energy: Electronic energy in Ha :param enthalpy: Enthalpy in kcal/mol :param entropy: Entropy in csal/mol-K :param temperature: Temperature in K. Default is 298.15, 25C returns: Free energy in eV """ if convert_energy: e = energy * 27.2114 else: e = energy return e + enthalpy * 0.043363 - temperature * entropy * 0.000043363 class MoleculeThermoDoc(PropertyDoc): property_name: str = "thermo" electronic_energy: float | None = Field( None, description="Electronic energy of the molecule (units: eV)" ) correction: bool = Field( False, description="Was a single-point calculation at higher level of " "theory used to correct the electronic energy?", ) base_level_of_theory: LevelOfTheory | None = Field( None, description="Level of theory used for uncorrected thermochemistry." ) base_solvent: str | None = Field( None, description="String representation of the solvent " "environment used for uncorrected thermochemistry.", ) base_lot_solvent: str | None = Field( None, description="String representation of the level of theory and solvent " "environment used for uncorrected thermochemistry.", ) correction_level_of_theory: LevelOfTheory | None = Field( None, description="Level of theory used to correct the electronic energy." ) correction_solvent: str | None = Field( None, description="String representation of the solvent " "environment used to correct the electronic energy.", ) correction_lot_solvent: str | None = Field( None, description="String representation of the level of theory and solvent " "environment used to correct the electronic energy.", ) combined_lot_solvent: str | None = Field( None, description="String representation of the level of theory and solvent " "environment used to generate this ThermoDoc, combining " "both the frequency calculation and (potentially) the " "single-point energy correction.", ) zero_point_energy: float | None = Field( None, description="Zero-point energy of the molecule (units: eV)" ) rt: float | None = Field( None, description="R*T, where R is the gas constant and T is temperature, taken " "to be 298.15K (units: eV)", ) total_enthalpy: float | None = Field( None, description="Total enthalpy of the molecule at 298.15K (units: eV)" ) total_entropy: float | None = Field( None, description="Total entropy of the molecule at 298.15K (units: eV/K)" ) translational_enthalpy: float | None = Field( None, description="Translational enthalpy of the molecule at 298.15K (units: eV)", ) translational_entropy: float | None = Field( None, description="Translational entropy of the molecule at 298.15K (units: eV/K)", ) rotational_enthalpy: float | None = Field( None, description="Rotational enthalpy of the molecule at 298.15K (units: eV)" ) rotational_entropy: float | None = Field( None, description="Rotational entropy of the molecule at 298.15K (units: eV/K)" ) vibrational_enthalpy: float | None = Field( None, description="Vibrational enthalpy of the molecule at 298.15K (units: eV)" ) vibrational_entropy: float | None = Field( None, description="Vibrational entropy of the molecule at 298.15K (units: eV/K)" ) free_energy: float | None = Field( None, description="Gibbs free energy of the molecule at 298.15K (units: eV)" ) @classmethod def from_task( cls, task: TaskDocument, molecule_id: MPculeID, correction_task: TaskDocument | None = None, deprecated: bool = False, **kwargs, ): # type: ignore[override] """ Construct a thermodynamics document from a task :param task: document from which thermodynamic properties can be extracted :param molecule_id: MPculeID :param deprecated: bool. Is this document deprecated? :param kwargs: to pass to PropertyDoc :return: """ if task.output.optimized_molecule is not None: mol = task.output.optimized_molecule else: mol = task.output.initial_molecule if correction_task is None: energy = task.output.final_energy correction = False correction_lot = None correction_solvent = None correction_lot_solvent = None level_of_theory = task.level_of_theory solvent = task.solvent lot_solvent = task.lot_solvent combined_lot_solvent = task.lot_solvent else: energy = correction_task.output.final_energy correction = True correction_lot = correction_task.level_of_theory correction_solvent = correction_task.solvent correction_lot_solvent = correction_task.lot_solvent combined_lot_solvent = f"{task.lot_solvent}//{correction_lot_solvent}" level_of_theory = correction_lot solvent = correction_solvent lot_solvent = combined_lot_solvent total_enthalpy = task.output.enthalpy total_entropy = task.output.entropy origins = [MolPropertyOrigin(name="thermo", task_id=task.task_id)] # type: ignore[call-arg] id_string = f"thermo-{molecule_id}-{task.task_id}-{task.lot_solvent}" if correction and correction_task is not None: origins.append( MolPropertyOrigin( # type: ignore[call-arg] name="thermo_energy_correction", task_id=correction_task.task_id ) ) id_string += f"-{correction_task.task_id}-{correction_task.lot_solvent}" h = blake2b() h.update(id_string.encode("utf-8")) property_id = h.hexdigest() if total_enthalpy is not None and total_entropy is not None: free_energy = get_free_energy(energy, total_enthalpy, total_entropy) for calc in task.calcs_reversed: if all( [ calc.get(x) is not None for x in [ "ZPE", "trans_enthalpy", "rot_enthalpy", "vib_enthalpy", "gas_constant", "trans_entropy", "rot_entropy", "vib_entropy", ] ] ): return super().from_molecule( meta_molecule=mol, property_id=property_id, molecule_id=molecule_id, level_of_theory=level_of_theory, solvent=solvent, lot_solvent=lot_solvent, correction=correction, base_level_of_theory=task.level_of_theory, base_solvent=task.solvent, base_lot_solvent=task.lot_solvent, correction_level_of_theory=correction_lot, correction_solvent=correction_solvent, correction_lot_solvent=correction_lot_solvent, combined_lot_solvent=combined_lot_solvent, electronic_energy=energy * 27.2114 if energy else None, zero_point_energy=calc["ZPE"] * 0.043363, rt=calc["gas_constant"] * 0.043363, total_enthalpy=total_enthalpy * 0.043363, translational_enthalpy=calc["trans_enthalpy"] * 0.043363, rotational_enthalpy=calc["rot_enthalpy"] * 0.043363, vibrational_enthalpy=calc["vib_enthalpy"] * 0.043363, total_entropy=total_entropy * 0.000043363, translational_entropy=calc["trans_entropy"] * 0.000043363, rotational_entropy=calc["rot_entropy"] * 0.000043363, vibrational_entropy=calc["vib_entropy"] * 0.000043363, free_energy=free_energy, deprecated=deprecated, origins=origins, **kwargs, ) # If all thermodynamic data is not available return super().from_molecule( meta_molecule=mol, property_id=property_id, molecule_id=molecule_id, level_of_theory=task.level_of_theory, solvent=task.solvent, lot_solvent=task.lot_solvent, correction=correction, correction_level_of_theory=correction_lot, correction_solvent=correction_solvent, correction_lot_solvent=correction_lot_solvent, combined_lot_solvent=combined_lot_solvent, electronic_energy=energy * 27.2114 if energy else None, deprecated=deprecated, origins=origins, **kwargs, ) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8228872 emmet_core-0.87.2/emmet/core/molecules/trajectory.py0000644000175100017510000004531715240717720022203 0ustar00runnerrunnerfrom hashlib import blake2b import numpy as np from pydantic import Field from emmet.core.io.pymatgen import Element, Molecule, Trajectory from emmet.core.molecules import MolPropertyOrigin from emmet.core.molecules.molecule_property import PropertyDoc from emmet.core.mpid import MPculeID from emmet.core.qchem.task import TaskDocument __author__ = "Evan Spotte-Smith " class ForcesDoc(PropertyDoc): property_name: str = "forces" forces: list[list[float]] = Field(..., description="Atomic forces (units: Ha/Bohr)") precise_forces: list[list[float] | None] = Field( default_factory=list, description="High-precision atomic forces (units: Ha/Bohr)", ) pcm_forces: list[list[float]] | None = Field( None, description="Electrostatic atomic forces from polarizable continuum model (PCM) implicit solvation " "(units: Ha/Bohr).", ) cds_forces: list[list[float]] | None = Field( None, description="Atomic force contributions from cavitation, dispersion, and structural rearrangement in the SMx " "family of implicit solvent models (units: Ha/Bohr)", ) average_force_magnitude: float | None = Field( None, description="Average magnitude of atomic forces (units: Ha/Bohr)" ) max_force_magnitude: float | None = Field( None, description="Maximum magnitude of atomic forces (units: Ha/Bohr)", ) min_force_magnitude: float | None = Field( None, description="Minimum magnitude of atomic forces (units: Ha/Bohr)", ) @classmethod def from_task( cls, task: TaskDocument, molecule_id: MPculeID, deprecated: bool = False, **kwargs, ): # type: ignore[override] """ Construct a force document from a task document :param task: document from which force properties can be extracted :param molecule_id: MPculeID :param deprecated: bool. Is this document deprecated? :param kwargs: to pass to PropertyDoc :return: """ if task.task_type.value != "Force": raise ValueError( "ForcesDoc can only be constructed from force calculations," f"not {task.task_type.value}!" ) mol = task.output.initial_molecule forces = task.output.gradients precise_forces = task.output.precise_gradients pcm_forces = task.output.pcm_gradients cds_forces = task.output.CDS_gradients calc = task.calcs_reversed[0] # Precise forces are either in output or don't exist # For PCM and CDS forces, can check "calcs_reversed" if pcm_forces is None: pcm_forces = calc.get("pcm_gradients") if cds_forces is None: cds_forces = calc.get("CDS_gradients") # Basic stats if precise_forces is not None: magnitudes = [np.linalg.norm(np.asarray(f)) for f in precise_forces] else: magnitudes = [np.linalg.norm(np.asarray(f)) for f in forces] average_force_magnitude = np.mean(magnitudes) max_force_magnitude = max(magnitudes) # type: ignore[type-var] min_force_magnitude = min(magnitudes) # type: ignore[type-var] id_string = f"forces-{molecule_id}-{task.task_id}-{task.lot_solvent}" h = blake2b() h.update(id_string.encode("utf-8")) property_id = h.hexdigest() return super().from_molecule( meta_molecule=mol, property_id=property_id, molecule_id=molecule_id, level_of_theory=task.level_of_theory, solvent=task.solvent, lot_solvent=task.lot_solvent, forces=forces, precise_forces=precise_forces, pcm_forces=pcm_forces, cds_forces=cds_forces, average_force_magnitude=average_force_magnitude, max_force_magnitude=max_force_magnitude, min_force_magnitude=min_force_magnitude, origins=[MolPropertyOrigin(name="forces", task_id=task.task_id)], # type: ignore[call-arg] deprecated=deprecated, **kwargs, ) class TrajectoryDoc(PropertyDoc): property_name: str = "optimization_trajectory" num_trajectories: int = Field( ..., description="Number of separate optimization trajectories extracted from this task", ) species: list[Element | str] = Field( ..., description="Element or element name for each atom in the molecule for each optimization step", ) geometries: list[list[list[list[float]]]] = Field( ..., description="XYZ positions of each atom in the molecule for each optimization step for each optimization " "trajectory (units: Angstrom)", ) energies: list[list[float]] = Field( ..., description="Electronic energies for each optimization step for each optimization trajectory (units: Hartree)", ) forces: list[list[list[list[float]]]] = Field( ..., description="Forces on each atom for each optimization step for each optimization trajectory (units: Ha/Bohr)", ) pcm_forces: list[list[list[list[float]]] | None] = Field( default_factory=list, description="Electrostatic atomic forces from polarizable continuum model (PCM) implicit solvation " "for each optimization step for each optimization trajectory (units: Ha/Bohr).", ) cds_forces: list[list[list[list[float]]] | None] = Field( default_factory=list, description="Atomic force contributions from cavitation, dispersion, and structural rearrangement in the SMx " "family of implicit solvent models, for each optimization step for each optimization trajectory " "(units: Ha/Bohr)", ) mulliken_partial_charges: list[list[list[float]] | None] = Field( default_factory=list, description="Partial charges of each atom for each optimization step for each optimization trajectory, using " "the Mulliken method", ) mulliken_partial_spins: list[list[list[float]] | None] = Field( default_factory=list, description="Partial spins of each atom for each optimization step for each optimization trajectory, using " "the Mulliken method", ) resp_partial_charges: list[list[list[float]] | None] = Field( default_factory=list, description="Partial charges of each atom for each optimization step for each optimization trajectory, using " "the restrained electrostatic potential (RESP) method", ) dipole_moments: list[list[list[float]] | None] = Field( default_factory=list, description="Molecular dipole moment for each optimization step for each optimization trajectory, " "(units: Debye)", ) resp_dipole_moments: list[list[list[float]] | None] = Field( default_factory=list, description="Molecular dipole moment for each optimization step for each optimization trajectory, " "using the restrainted electrostatic potential (RESP) method (units: Debye)", ) @property def molecules(self) -> list[list[Molecule]]: """ Geometries along the optimization trajectory, represented as pymatgen Molecule objects. Args: self Returns: list[Molecule]: the optimization trajectory as a list of Molecules """ return [ [ Molecule( self.species, geom, charge=self.charge, spin_multiplicity=self.spin_multiplicity, ) for geom in trajectory ] for trajectory in self.geometries ] def as_trajectories(self) -> list[Trajectory]: """ Represent this TrajectoryDoc as a list of pymatgen Trajectory objects. Args: self Returns: trajectories (list[Trajectory]): TrajectoryDoc represented as a collection of pymatgen Trajectory objects """ trajectories = list() mol_trajectories = self.molecules for ii, mols in enumerate(mol_trajectories): num_steps = len(mols) # Frame (structure) properties frame_props = {"energies": self.energies[ii]} for prop in ( "dipole_moments", "resp_dipole_moments", ): frame_props[prop] = [] if (vals := getattr(self, prop, None)) is not None: frame_props[prop] = vals[ii] # Site (atomic) properties site_props = {"forces": self.forces[ii]} for prop in ( "pcm_forces", "cds_forces", "mulliken_partial_charges", "mulliken_partial_spins", "resp_partial_charges", ): site_props[prop] = [] if (vals := getattr(self, prop, None)) is not None: site_props[prop] = vals[ii] # Convert into a Trajectory object traj_frame_props = list() traj_mols = list() for jj in range(num_steps): step_dict = dict() for k, v in frame_props.items(): if v is not None: step_dict[k] = v[jj] # type: ignore step_mol = mols[jj] for k, v in site_props.items(): # type: ignore if v is not None: step_mol.add_site_property(property_name=k, values=v[jj]) # type: ignore[arg-type] traj_mols.append(step_mol) traj_frame_props.append(step_dict) traj = Trajectory.from_molecules( traj_mols, frame_properties=traj_frame_props, time_step=None ) # type: ignore[arg-type] trajectories.append(traj) return trajectories @classmethod def from_task( cls, task: TaskDocument, molecule_id: MPculeID, deprecated: bool = False, **kwargs, ): # type: ignore[override] """ Construct a trajectory document from a task document :param task: document from which force properties can be extracted :param molecule_id: MPculeID :param deprecated: bool. Is this document deprecated? :param kwargs: to pass to PropertyDoc :return: """ if task.task_type.value not in [ "Geometry Optimization", "Frequency Flattening Geometry Optimization", "Transition State Geometry Optimization", "Frequency Flattening Transition State Geometry Optimization", ]: raise ValueError( "TrajectoryDoc can only be constructed from geometry optimization calculations," f"not {task.task_type.value}!" ) if task.output.optimized_molecule is not None: mol = task.output.optimized_molecule else: mol = task.output.initial_molecule multiplicity = mol.spin_multiplicity species = None geometries = list() # type: ignore energies = list() total_gradients = list() pcm_gradients = list() cds_gradients = list() mulliken_partial_charges = list() mulliken_partial_spins = list() resp_partial_charges = list() dipole_moments = list() resp_dipole_moments = list() for calculation in task.calcs_reversed: species = calculation.get("species", species) this_geometries = calculation.get("geometries") this_energies = calculation.get("energy_trajectory") this_total_gradients = calculation.get("gradients") this_pcm_gradients = calculation.get("pcm_gradients") this_cds_gradients = calculation.get("CDS_gradients") this_mulliken = calculation.get("Mulliken") this_resp = calculation.get("RESP") this_dipoles = calculation.get("dipoles") valid_trajectory = True if this_geometries is None or this_energies is None: # No valid geometry optimization found valid_trajectory = False elif len(this_energies) != len(this_total_gradients): # Energies and forces cannot be trivially mapped valid_trajectory = False elif len(this_geometries) != len(this_energies): # Initial geometry not included - common because of how parsing is done if len(this_geometries) == len(this_energies) - 1: this_geometries = [ calculation["initial_geometry"] ] + this_geometries # Other issue - no one-to-one mapping of molecule structure and energy else: valid_trajectory = False if not valid_trajectory: continue if isinstance(calculation["initial_molecule"], Molecule): init_mol = calculation["initial_molecule"] # type: ignore else: init_mol = Molecule.from_dict(calculation["initial_molecule"]) # type: ignore if species is None: species = init_mol.species # type: ignore # Number of steps # All data in this (sub)-trajectory must have the same length num_steps = len(geometries) this_dipole_moments = None this_resp_dipole_moments = None # electric dipoles if this_dipoles is not None: if ( this_dipoles.get("dipole") is not None and len(this_dipoles["dipole"]) > 0 ): if ( isinstance(this_dipoles["dipole"][0], list) and len(this_dipoles["dipole"]) == num_steps ): this_dipole_moments = this_dipoles["dipole"] if ( this_dipoles.get("RESP_dipole") is not None and len(this_dipoles["RESP_dipole"]) > 0 ): if ( isinstance(this_dipoles["RESP_dipole"][0], list) and len(this_dipoles["RESP_dipole"]) == num_steps ): this_resp_dipole_moments = this_dipoles["RESP_dipole"] this_mulliken_partial_charges = None this_mulliken_partial_spins = None this_resp_partial_charges = None # Partial charges/spins if this_mulliken is not None: if len(this_mulliken) == num_steps: if int(multiplicity) == 1: this_mulliken_partial_charges = this_mulliken else: # For open-shell molecules, need to split mulliken charges and spins charges = list() spins = list() for step in this_mulliken: step_charges = list() step_spins = list() for atom in step: step_charges.append(atom[0]) step_spins.append(atom[1]) charges.append(step_charges) spins.append(step_spins) this_mulliken_partial_charges = charges this_mulliken_partial_spins = spins elif len(this_mulliken) == num_steps + 1: last = np.asarray(this_mulliken[-1]) seclast = np.asarray(this_mulliken[-2]) if np.allclose(last, seclast): if int(multiplicity) == 1: this_mulliken_partial_charges = this_mulliken[:-1] else: charges = list() spins = list() for step in this_mulliken[::-1]: step_charges = list() step_spins = list() for atom in step: step_charges.append(atom[0]) step_spins.append(atom[1]) charges.append(step_charges) spins.append(step_spins) this_mulliken_partial_charges = charges this_mulliken_partial_spins = spins if this_resp is not None: if len(this_resp) == num_steps: this_resp_partial_charges = this_resp elif len(this_resp) == num_steps + 1: last = np.asarray(this_resp[-1]) seclast = np.asarray(this_resp[-2]) if np.allclose(last, seclast): this_resp_partial_charges = this_resp[:-1] geometries.append(this_geometries) energies.append(this_energies) total_gradients.append(this_total_gradients) pcm_gradients.append(this_pcm_gradients) cds_gradients.append(this_cds_gradients) mulliken_partial_charges.append(this_mulliken_partial_charges) mulliken_partial_spins.append(this_mulliken_partial_spins) resp_partial_charges.append(this_resp_partial_charges) dipole_moments.append(this_dipole_moments) resp_dipole_moments.append(this_resp_dipole_moments) num_trajectories = len(geometries) id_string = f"trajectory-{molecule_id}-{task.task_id}-{task.lot_solvent}" h = blake2b() h.update(id_string.encode("utf-8")) property_id = h.hexdigest() return super().from_molecule( meta_molecule=mol, property_id=property_id, molecule_id=molecule_id, level_of_theory=task.level_of_theory, solvent=task.solvent, lot_solvent=task.lot_solvent, num_trajectories=num_trajectories, species=species, geometries=geometries, energies=energies, forces=total_gradients, pcm_forces=pcm_gradients, cds_forces=cds_gradients, mulliken_partial_charges=mulliken_partial_charges, mulliken_partial_spins=mulliken_partial_spins, resp_partial_charges=resp_partial_charges, dipole_moments=dipole_moments, resp_dipole_moments=resp_dipole_moments, origins=[MolPropertyOrigin(name="trajectory", task_id=task.task_id)], # type: ignore[call-arg] deprecated=deprecated, **kwargs, ) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8228872 emmet_core-0.87.2/emmet/core/molecules/vibration.py0000644000175100017510000001074315240717720022005 0ustar00runnerrunnerfrom hashlib import blake2b from pydantic import Field from emmet.core.molecules import MolPropertyOrigin from emmet.core.molecules.molecule_property import PropertyDoc from emmet.core.mpid import MPculeID from emmet.core.qchem.task import TaskDocument from emmet.core.types.pymatgen_types.structure_adapter import MoleculeType __author__ = "Evan Spotte-Smith " class VibrationDoc(PropertyDoc): property_name: str = "vibrations" molecule: MoleculeType = Field(..., description="Molecular structure") frequencies: list[float] = Field( ..., description="List of molecular vibrational frequencies" ) frequency_modes: list[list[list[float]]] = Field( ..., description="Vibrational frequency modes of the molecule" ) ir_intensities: list[float] = Field( ..., title="IR intensities", description="Intensities for IR vibrational spectrum peaks", ) ir_activities: list[bool] = Field( ..., title="IR activities", description="List indicating if frequency-modes are IR-active", ) raman_intensities: list[float] | None = Field( None, title="Raman intensities", description="Intensities for Raman spectrum peaks", ) raman_activities: list[float] | None = Field( None, title="Raman activities", description="List indicating if frequency-modes are Raman-active", ) @classmethod def from_task( cls, task: TaskDocument, molecule_id: MPculeID, deprecated: bool = False, **kwargs, ): # type: ignore[override] """ Construct a vibration document from a task document :param task: document from which vibrational properties can be extracted :param molecule_id: MPculeID :param deprecated: bool. Is this document deprecated? :param kwargs: to pass to PropertyDoc :return: """ if task.output.frequencies is None: raise Exception("No frequencies in task!") if task.output.optimized_molecule is not None: mol = task.output.optimized_molecule else: mol = task.output.initial_molecule frequencies = task.output.frequencies frequency_modes = None ir_intensities = None ir_active = None raman_intensities = None raman_active = None for calc in task.calcs_reversed: frequency_modes = calc.get("frequency_mode_vectors") ir_intensities = calc.get("IR_intens") ir_active = calc.get("IR_active") raman_intensities = calc.get("raman_intens") raman_active = calc.get("raman_active") # IR intensities and activities required # Raman intensitives/activities are optional if all( [x is not None for x in [frequency_modes, ir_intensities, ir_active]] ): break if frequency_modes is None: raise Exception("No frequency modes in task!") elif ir_intensities is None: raise Exception("No IR intensities in task!") elif ir_active is None: raise Exception("No IR activities in task!") ir_active = [True if ira.upper() == "YES" else False for ira in ir_active] if raman_active is not None: raman_active = [ True if ra.upper() == "YES" else False for ra in raman_active ] warnings = list() if frequencies[0] < 0.0: warnings.append("Imaginary frequencies") id_string = f"vibrations-{molecule_id}-{task.task_id}-{task.lot_solvent}" h = blake2b() h.update(id_string.encode("utf-8")) property_id = h.hexdigest() return super().from_molecule( meta_molecule=mol, property_id=property_id, molecule_id=molecule_id, level_of_theory=task.level_of_theory, solvent=task.solvent, lot_solvent=task.lot_solvent, molecule=mol, frequencies=frequencies, frequency_modes=frequency_modes, ir_intensities=ir_intensities, ir_activities=ir_active, raman_intensities=raman_intensities, raman_activities=raman_active, warnings=warnings, origins=[MolPropertyOrigin(name="vibrations", task_id=task.task_id)], # type: ignore[call-arg] deprecated=deprecated, **kwargs, ) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8228872 emmet_core-0.87.2/emmet/core/molecules_jcesr.py0000644000175100017510000000314715240717720021176 0ustar00runnerrunnerfrom pydantic import BaseModel, Field from emmet.core.io.pymatgen import Element from emmet.core.types.pymatgen_types.structure_adapter import MoleculeType class MoleculesDoc(BaseModel): """ Molecules relevant to battery electrolytes. """ elements: list[Element] | None = Field( None, description="List of elements in the molecule.", ) nelements: int | None = Field( None, description="Number of elements in the molecule.", ) EA: float | None = Field( None, description="Electron affinity of the molecule in eV.", ) IE: float | None = Field( None, description="Ionization energy of the molecule in eV.", ) charge: int | None = Field( None, description="Charge of the molecule in +e.", ) pointgroup: str | None = Field( None, description="Point group of the molecule in Schoenflies notation.", ) smiles: str | None = Field( None, description="The simplified molecular input line-entry system (SMILES) \ representation of the molecule.", ) task_id: str | None = Field( None, description="Materials Project molecule ID. This takes the form mol-*****.", ) molecule: MoleculeType | None = Field( None, description="Pymatgen molecule object.", ) formula_pretty: str | None = Field( None, description="Chemical formula of the molecule.", ) svg: str | None = Field( None, description="String representation of the SVG image of the molecule.", ) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8232715 emmet_core-0.87.2/emmet/core/mpcomplete.py0000644000175100017510000000152115240717720020157 0ustar00runnerrunnerfrom pydantic import Field from pydantic.main import BaseModel from emmet.core.types.enums import ValueEnum from emmet.core.types.pymatgen_types.structure_adapter import StructureType class MPCompleteDoc(BaseModel): """ Defines data for MPComplete structure submissions """ structure: StructureType | None = Field( None, description="Structure submitted by the user.", ) public_name: str | None = Field( None, description="Public name of submitter.", ) public_email: str | None = Field( None, description="Public email of submitter.", ) class MPCompleteDataStatus(ValueEnum): """ Submission status for MPComplete data """ submitted = "SUBMITTED" pending = "PENDING" running = "RUNNING" error = "ERROR" complete = "COMPLETE" ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8232715 emmet_core-0.87.2/emmet/core/mpid.py0000644000175100017510000006447715240717720016766 0ustar00runnerrunner"""Define identifier schemas used in MP.""" from __future__ import annotations import re from math import floor, log from pathlib import Path from string import ascii_lowercase, digits from typing import TYPE_CHECKING from pydantic_core import CoreSchema, core_schema # For dev_scripts compatibility, safe import this list if (Path(__file__).parent / "_forbidden_alpha_id.py").exists(): from emmet.core._forbidden_alpha_id import FORBIDDEN_ALPHA_ID_VALUES else: FORBIDDEN_ALPHA_ID_VALUES = set() if TYPE_CHECKING: from collections.abc import Callable from typing import Any from pydantic import GetJsonSchemaHandler from pydantic.json_schema import JsonSchemaValue from typing_extensions import Self # matches "mp-1234" or "1234" followed by and optional "-(Alphanumeric)" MPID_REGEX_PATTERN = r"^([A-Za-z]+-)?(\d+)(-[A-Za-z0-9]+)*$" mpid_regex = re.compile(MPID_REGEX_PATTERN) MPCULE_REGEX_PATTERN = ( r"^([A-Za-z]+-)?([A-Fa-f0-9]+)-([A-Za-z0-9]+)-(m?[0-9]+)-([0-9]+)$" ) mpculeid_regex = re.compile(MPCULE_REGEX_PATTERN) # matches capital letters and numbers of length 26 (ULID) # followed by and optional "-(Alphanumeric)" check_ulid = re.compile(r"^[A-Z0-9]{26}(-[A-Za-z0-9]+)*$") VALID_ALPHA_SEPARATORS: set[str] = { "-", ":", "_", } class MPID(str): """ A Materials Project type ID with a prefix and an integer This class enables seemlessly mixing MPIDs and regular integer IDs Prefixed IDs are considered less than non-prefixed IDs to enable proper mixing with the Materials Project Args: val: Either 1) a prefixed string e.g. "mp-1234" 2) an integer e.g. 1234 3) a number stored as a string e.g. '1234' 4) an MPID Numbers stored as strings are coerced to ints """ def __init__(self, val: MPID | int | str) -> None: if isinstance(val, MPID): self.parts = val.parts # type: ignore self.string = val.string # type: ignore elif isinstance(val, int) or (isinstance(val, str) and val.isnumeric()): if int(val) < 0: raise ValueError("MPID cannot represent a negative integer.") self.parts = ("", int(val)) self.string = str(val) elif isinstance(val, str): if mpid_regex.fullmatch(val): parts = val.split("-") parts[1] = int(parts[1]) # type: ignore self.parts = tuple(parts) elif check_ulid.fullmatch(val): ulid = val.split("-")[0] self.parts = (ulid, 0) else: raise ValueError( "MPID string representation must follow the format prefix-number or start with a valid ULID." ) self.string = val else: raise ValueError( "Must provide an MPID, int, or string of the format prefix-number or start with a valid ULID." ) def __eq__(self, other: Any) -> bool: if other is None: return False if isinstance(other, MPID): return self.string == other.string elif isinstance(other, AlphaID): return other == self elif isinstance(other, (int, str)): try: return self.string == MPID(other).string except ValueError: return False return NotImplemented def __str__(self) -> str: return self.string def __repr__(self) -> str: return f"MPID({self})" def __lt__(self, other: MPID | int | str) -> bool: other_parts = MPID(other).parts if self.parts[0] and other_parts[0]: # both have prefixes; normal comparison return self.parts < other_parts elif self.parts[0]: # other is a pure int, self is prefixed # Always sort MPIDs before pure integer IDs return True elif other_parts[0]: # self is pure int, other is prefixed return False # both are pure ints; normal comparison return self.parts[1] < other_parts[1] def __gt__(self, other: MPID | int | str) -> bool: """Define greater than for MPID. Note that `__gt__` is not the same as `not __lt__`. If two values are equal, `__lt__` will return False. Defining ```__gt__ := not __lt__``` will then incorrectly return True for equal values. """ other_parts = MPID(other).parts if self.parts[0] and other_parts[0]: # both have prefixes; normal comparison return self.parts > other_parts elif not self.parts[0] and not other_parts[0]: # both are pure ints; normal comparison return self.parts[1] > other_parts[1] return not self.__lt__(other) def __hash__(self) -> int: return hash(self.string) @classmethod def __get_pydantic_core_schema__( cls, source: type[Any], handler: Callable[[Any], core_schema.CoreSchema] ) -> core_schema.CoreSchema: return core_schema.with_info_plain_validator_function(cls.validate) @classmethod def __get_pydantic_json_schema__( cls, core_schema: CoreSchema, handler: GetJsonSchemaHandler ) -> JsonSchemaValue: return dict( pattern=MPID_REGEX_PATTERN, examples=["mp-3534", "3453", "mp-834-Ag"], type="string", ) @classmethod def validate(cls, __input_value: Any, _: core_schema.ValidationInfo): if isinstance(__input_value, MPID): return __input_value elif isinstance(__input_value, str) and mpid_regex.fullmatch(__input_value): return MPID(__input_value) elif isinstance(__input_value, str) and check_ulid.fullmatch(__input_value): return MPID(__input_value) elif isinstance(__input_value, int): return MPID(__input_value) raise ValueError("Invalid MPID Format") class MPculeID(str): """ A Materials Project Molecule ID with a prefix, hash, and two integer values representing the charge and spin of the molecule Unlike the MPID, you cannot compare raw hashes or raw integers to MPculeIDs Args: val: Either 1) an MPculeID 2) a prefixed string of format "prefix-hash-formula-charge-spin" 3) a non-prefixed string of format "hash-formula-charge-spin" Numbers stored as strings are coerced to ints """ def __init__(self, val: MPculeID | str): if isinstance(val, MPculeID): self.parts = val.parts # type: ignore self.string = val.string # type: ignore elif isinstance(val, str): parts = val.split("-") if len(parts) == 4: parts[1] = int(parts[2].replace("m", "-")) # type: ignore parts[2] = int(parts[3]) # type: ignore elif len(parts) == 5: parts[3] = int(parts[3].replace("m", "-")) # type: ignore parts[4] = int(parts[4]) # type: ignore else: raise ValueError( "MPculeID string representation must follow the " "format prefix-hash-formula-charge-spin or hash-formula-charge-spin." ) self.parts = tuple(parts) self.string = val else: raise ValueError( "Must provide an MPculeID, or string of the format prefix-hash-formula-charge-spin " "or hash-formula-charge-spin" ) def __eq__(self, other: object) -> bool: if isinstance(other, MPculeID): return self.string == other.string elif isinstance(other, str): return self.string == MPculeID(other).string return NotImplemented def __str__(self) -> str: return self.string def __repr__(self) -> str: return f"MPculeID({self})" def __lt__(self, other: MPculeID | str) -> bool: other_parts = MPculeID(other).parts return "-".join([str(x) for x in self.parts[-4:]]) < "-".join( [str(x) for x in other_parts[-4:]] ) def __gt__(self, other: MPculeID | str) -> bool: return not self.__lt__(other) def __hash__(self) -> int: return hash(self.string) @classmethod def __get_pydantic_core_schema__( cls, source: type[Any], handler: Callable[[Any], core_schema.CoreSchema] ) -> core_schema.CoreSchema: return core_schema.with_info_plain_validator_function(cls.validate) @classmethod def __get_pydantic_json_schema__( cls, core_schema: CoreSchema, handler: GetJsonSchemaHandler ) -> JsonSchemaValue: return dict( pattern=r"^^([A-Za-z]+-)?([A-Fa-f0-9]+)-([A-Za-z0-9]+)-(m?[0-9]+)-([0-9]+)$", examples=[ "1a525231bdac3f13e2fac0962fe8d053-Mg1-0-1", "22b40b99719ac570fc7e6225e855ec6e-F5Li1P1-m1-2", "mpcule-b9ba54febc77d2a9177accf4605767db-C1H41-2", ], type="string", ) @classmethod def validate(cls, __input_value: Any, _: core_schema.ValidationInfo): if isinstance(__input_value, MPculeID): return __input_value elif isinstance(__input_value, str) and mpculeid_regex.fullmatch(__input_value): return MPculeID(__input_value) raise ValueError("Invalid MPculeID Format") class AlphaID(str): """Identifier based on representing an integer as an alphabetical string. Args: _alphabet (str) : The alphabet to use, defaults to lowercase Roman. _identifier (str) : The alphabetical string identifier. _padlen (int) : The minimum length of an identifier to pad left with zeroes. _prefix (str) : The ID prefix, ex. "mp" _separator (str) : The separator between `_prefix` and `_identifier`, ex: "-" _cut_point (int or None) : For legacy purposes, all MPIDs minted before the transition to AlphaID will use the legacy format as `mp-` when calling `AlphaID(...).string`. Thus `_cut_point`, if not `None`, defines the maximum MPID at which the integer in `AlphaID(...).string` is preferred. Notes: There is possibility for "obscene" strings to be used in AlphaID. For a comprehensive list of integer values of these IDs, you can import them: ``` from pathlib import Path from importlib_resources import files as import_resource_file if (Path(import_resource_file("emmet.core")) / "_forbidden_alpha_id.py").exists(): from emmet.core._forbidden_alpha_id import FORBIDDEN_ALPHA_ID_VALUES else: FORBIDDEN_ALPHA_ID_VALUES = set() ``` Or run `generate_identifier_exclude_list.py` in `emmet-core/dev_scripts` """ _identifier: str _padlen: int _prefix: str _separator: str _alphabet: str = ascii_lowercase _value: int | None = None _cut_point: int | None = 3347529 def __new__( cls, identifier: str | int | MPID, padlen: int = 0, prefix: str | None = None, separator: str = "-", ) -> Self: """Define a new instance of AlphaID. Args: identifier (str, int, or MPID) : the identifier, either a string with characters belonging to AlphaID._alphabet, an integer to represent as a string, or an MPID. padlen (int, default = 0) : the amount of characters to pad to if a character string is too short. For example, the integer 149 has alpha representation "ft", and thus `AlphaID(149, padlen = 6)` would present as "aaaaft". Both "ft" and "aaaaft" have the same integer value. prefix (str or None, default = None) : if a str, the prefix to use. For example, `AlphaID(149, padlen = 6, prefix="mp")` would present as "mp-aaaaft". separator (str, default = "-") : the separator to use between the prefix and the string representation of the integer, if the prefix is non-empty. For example, `AlphaID(149, padlen = 6, prefix="mp", separator = ":")` would present as "mp:aaaaft". """ if isinstance(identifier, str): if any(char.isupper() for char in identifier): raise ValueError( "To avoid amibiguities, AlphaID does not permit uppercase letters." ) non_alpha_char = set(identifier).difference(cls._alphabet + digits) valid_seps = non_alpha_char.intersection(VALID_ALPHA_SEPARATORS) > set() if len(non_alpha_char) == 1 and valid_seps: separator = list(non_alpha_char)[0] prefix, identifier = identifier.split(separator) if not prefix or not identifier: raise ValueError( "Missing prefix and/or identifer." "To specify an AlphaID without a prefix, provide only the identifier." ) elif len(non_alpha_char) > 1 or ( len(non_alpha_char) == 1 and not valid_seps ): raise ValueError( f"Too many non-alpha-numeric characters: {', '.join(non_alpha_char)}" ) elif isinstance(identifier, MPID): prefix, identifier = identifier.parts separator = "-" if isinstance(identifier, str) and set(identifier).intersection(digits): identifier = int(identifier) if isinstance(identifier, int): if identifier < 0: raise ValueError("AlphaID cannot represent a negative integer.") identifier = cls._integer_to_alpha_rep(identifier) prefix = prefix or "" if not prefix: separator = "" padded = max(0, padlen - len(identifier)) * cls._alphabet[0] if separator and separator not in VALID_ALPHA_SEPARATORS: raise ValueError( f"Invalid separator: {separator}. Use one of: {', '.join(VALID_ALPHA_SEPARATORS)}" ) new_cls = str.__new__(cls, prefix + separator + padded + identifier) new_cls._identifier = identifier new_cls._padlen = padlen new_cls._prefix = prefix new_cls._separator = separator return new_cls @property def padded(self) -> str: """Return the padded string identifier (without prefixing).""" padded = max(0, self._padlen - len(self._identifier)) * self._alphabet[0] return f"{padded}{self._identifier}" @classmethod def _string_to_base_10_value(cls, string_value: str) -> int: """Obtain the integer value of an alphabetical string.""" value = 0 rev_codex = {letter: idx for idx, letter in enumerate(cls._alphabet)} base = len(cls._alphabet) for ipow, char in enumerate(string_value[::-1]): value += rev_codex[char] * base**ipow return value @classmethod def _integer_to_alpha_rep(cls, integer_val: int) -> str: """Obtain the string representation of an integer.""" if integer_val == 0: return cls._alphabet[0] base = len(cls._alphabet) max_pow = floor(log(integer_val) / log(base)) string: str = "" rem = integer_val for pow in range(max_pow, -1, -1): if rem == 0: string += cls._alphabet[0] continue mult = base**pow for coeff in range(base - 1, -1, -1): if coeff * mult <= rem: string += cls._alphabet[coeff] rem -= coeff * mult break else: # Should never reach this point but still good to have this here raise ValueError( f"Could not parse integer into string representation: {integer_val}" ) return string def __hash__(self) -> int: """Ensure hashability.""" return hash(str(self)) def __int__(self) -> int: """Get and cache the current AlphaID's integer value.""" if self._value is None: self._value = self._string_to_base_10_value(self._identifier) return self._value def __repr__(self) -> str: """Set AlphaID display name to distinguish from base string class.""" return "AlphaID(" + self + ")" def __eq__(self, other: Any) -> bool: """Define equality of AlphaID. If other is an int, returns True if the integer value of the current instance equals the other. If other is a base str, returns True if the string representations are equal. If other is an AlphaID, returns True only if the prefix, separator, and value of the two are equal. """ if other is None: return False if isinstance(other, MPID) or ( isinstance(other, str) and other.startswith("mp-") ): test = AlphaID(other) else: test = other if isinstance(test, int): return int(self) == test elif isinstance(test, AlphaID): return ( test._prefix == self._prefix and test._separator == self._separator and int(self) == int(test) ) elif isinstance(test, str): return str(self) == test raise NotImplementedError(f"Cannot compare AlphaID with {type(test)}") def __ne__(self, other: Any) -> bool: """Define inverse equality for AlphaID.""" return not self.__eq__(other) @staticmethod def _coerce_value( alpha_id: AlphaID, other: Any, exception_on_mismatch: bool = True ) -> int: """Check if another value is comparable to a reference AlphaID. Used in defining `__add__`, `__sub__`, `__gt__`, and `__lt_``. Args: alpha_id : AlphaID other : Any other value exception_on_mismatch : bool (True = default) If True, raises an exception when the prefix and separators of two AlphaIDs do not match. To allow sorting of AlphaIDs, this is False for `__gt__` and `__lt__` but is True for `__add__` and `__sub__`. Returns: integer reprsenting the other value if possible. Raises exceptions if `other` cannot be compared to `alpha_id`. """ test = AlphaID(other) if isinstance(other, MPID) else other if isinstance(test, AlphaID): if exception_on_mismatch: exc_str = "" if test._prefix != alpha_id._prefix: exc_str += f"Prefixes do not match\n left {alpha_id._prefix or None}\n right {test._prefix or None}" if ( test._prefix and alpha_id._prefix and test._separator != alpha_id._separator ): if exc_str: exc_str += "\n" exc_str += f"Separators do not match:\n left {alpha_id._separator}\n right {test._separator}" if exc_str: raise TypeError(exc_str) diff = int(test) elif isinstance(test, str): diff = alpha_id._string_to_base_10_value(test) elif isinstance(test, int): diff = test else: raise NotImplementedError(f"Cannot compare AlphaID with type {type(test)}") return diff def __add__(self, other: Any) -> "AlphaID": """Define addition of AlphaID. Returns an AlphaID with the same `padlen` as the current instance, not `other`. Thus the order of addition can change the presentation of AlphaID, but not its value. If other is also an AlphaID, but its `prefix` and `separator` do not match, will not add the two. Only checks the separator if prefixes are both non-null. Args: other : the value to add to the current identifier. If a string, its integer value is first computed. The integer values are then added, and a new instance of AlphaID is returned. Returns: AlphaID representing the sum of the current and other values. """ return AlphaID( int(self) + self._coerce_value(self, other), padlen=self._padlen, prefix=self._prefix, separator=self._separator, ) def __sub__(self, other: Any) -> "AlphaID": """Define subtraction of AlphaID. See the docstring for `__add__`. The resultant `padlen` is taken from the current instance, not `other`. Will not subtract two AlphaIDs if `prefix` and `separator` do not match. """ return self.__add__(-self._coerce_value(self, other)) def __lt__(self, other: Any) -> bool: """Define AlphaID less than. Returns False between two AlphaIDs if `prefix` and `separator` do not match. """ return int(self) < self._coerce_value(self, other, exception_on_mismatch=False) def __gt__(self, other: Any) -> bool: """Define AlphaID greater than. Returns False between two AlphaIDs if `prefix` and `separator` do not match. """ return int(self) > self._coerce_value(self, other, exception_on_mismatch=False) @staticmethod def _format_legacy_ids( alpha_id: AlphaID, cutoff: int | None, prefix: str | None, separator: str | None, as_object: bool = False, ) -> str | MPID | AlphaID: """Format an AlphaID as either an MPID for legacy data, or AlphaID for newer data. Parameters ----------- alpha_id : AlphaID An instance of AlphaID cutoff : int | None The largest legacy ID value to format as an MPID. If None, this returns the original AlphaID. prefix : str or None Identifier prefix separator : str or None Identifier separator. If returning an MPID and the prefix is not None, will override as a hyphen ("-") as_object : bool = False Whether to return objects (True) or a str (False) Returns ----------- MPID if as_object and int(alpha_id) <= cutoff AlphaID if as_object and int(alpha_id) > cutoff str otherwise """ if cutoff and ((v := int(alpha_id)) <= cutoff): idx = f"{prefix or ''}{'-' if prefix else ''}{v}" if as_object: return MPID(idx) return idx if as_object: return alpha_id return str(alpha_id) @property def string(self) -> str: """Legacy access to .string attr as in MPID.""" return self._format_legacy_ids( self, self._cut_point, self._prefix, self._separator, as_object=False ) @property def formatted(self) -> MPID | AlphaID: """Return an MPID for legacy indices, and AlphaID otherwise.""" return self._format_legacy_ids( # type: ignore[return-value] self, self._cut_point, self._prefix, self._separator, as_object=True ) def copy(self) -> AlphaID: """Return a deep copy of the current instance.""" return AlphaID(str(self)) @classmethod def __get_pydantic_core_schema__( cls, source: Any, handler: core_schema.CoreSchema ) -> core_schema.CoreSchema: """Generate pydantic schema for AlphaID.""" return core_schema.with_info_plain_validator_function(cls.validate) @classmethod def __get_pydantic_json_schema__( cls, _core_schema: CoreSchema, handler: GetJsonSchemaHandler ) -> JsonSchemaValue: json_schema = handler(core_schema.str_schema()) json_schema = handler.resolve_ref_schema(json_schema) json_schema["examples"] = ["mp-ft", "task:pqrs"] return json_schema @classmethod def validate(cls, __input_value: Any, _: core_schema.ValidationInfo) -> AlphaID: """Define pydantic validator for AlphaID.""" if isinstance(__input_value, AlphaID): return __input_value elif isinstance(__input_value, str | int | MPID): return AlphaID(__input_value) raise ValueError(f"Invalid AlphaID Format {__input_value}") @property def parts(self) -> tuple[str, int]: """Mimic the parts attribute of MPID (prefix, integer value).""" return ( self._prefix or "", int(self), ) @property def next_safe(self) -> AlphaID: """Return the next multi-lingually "safe" AlphaID.""" return _next_safe_alpha_id(self) def _next_safe_alpha_id( start: int | str | AlphaID = 0, ) -> AlphaID: """Get the next "safe" (non-obscene) AlphaID. Returns the least AlphaID > AlphaID(start) such that its string value is "non-obscene". Uses a multi-lingual list of profanity from the shutter stock team (see emmet-core/dev_scripts/generate_identifier_exclude_list.py) to exclude generally-accepted crude language in AlphaIDs. Parameters ----------- start : int | str | AlphaID = 0 The AlphaID to start from. Returns ----------- AlphaID : the next sequential AlphaID that is (mostly-)profanity free. (We're not perfect, there are so many languages.) """ start_id = AlphaID(start) + 1 while int(start_id) in FORBIDDEN_ALPHA_ID_VALUES: start_id += 1 return start_id def validate_identifier( idx: str | MPID | AlphaID, serialize: bool = False ) -> str | MPID | AlphaID: """Format an input string or identifier as a valid Materials Project format identifier. Parameters ----------- idx : str, MPID, AlphaID The input identifier, can either be an already instantiated identifier object, or a plain string serialize : bool = False If True, returns the string representation of the identifier object. If False, returns the object. """ try: parsed_idx = AlphaID(idx) except Exception as exc: raise ValueError(f"Invalid identifier {idx}, must be MPID or AlphaID.") from exc if serialize: return ( str(AlphaID(parsed_idx)) if isinstance(parsed_idx, MPID | AlphaID) else str(parsed_idx) ) return parsed_idx ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8232715 emmet_core-0.87.2/emmet/core/mpid_ext.py0000644000175100017510000000040515240717720017623 0ustar00runnerrunner"""Stub imports to prevent breakages.""" import warnings warnings.warn( "`emmet.core.mpid_ext` has been deprecated " "and will be removed after emmet-core==0.87.0." ) from emmet.core.mpid import validate_identifier __all__ = ["validate_identifier"] ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8232715 emmet_core-0.87.2/emmet/core/neb.py0000644000175100017510000005435615240717720016574 0ustar00runnerrunner"""Schemas and utils for NEB calculations.""" from __future__ import annotations from pathlib import Path from typing import TYPE_CHECKING, Any import numpy as np import orjson from monty.os.path import zpath from pydantic import ( BaseModel, Field, field_serializer, field_validator, model_validator, ) from scipy.interpolate import CubicSpline from typing_extensions import Self from emmet.core.io.pymatgen import Molecule, Structure from emmet.core.tasks import ( _VOLUMETRIC_FILES, CustodianDoc, _find_vasp_files, _parse_custodian, _parse_orig_inputs, ) from emmet.core.types.enums import ValueEnum, VaspObject from emmet.core.types.pymatgen_types.structure_adapter import StructureType from emmet.core.types.typing import DateTimeType, NullableDateTimeType from emmet.core.utils import arrow_incompatible, type_override from emmet.core.vasp.calculation import Calculation, CalculationInput from emmet.core.vasp.task_valid import TaskState if TYPE_CHECKING: from collections.abc import Callable, Sequence from numpy.typing import NDArray def _join_endpoint_and_image_data( endpoint_data: list[Any], image_data: list[Any] | None, map_fun: Callable | None = None, ) -> list[Any]: data = [endpoint_data[0], *(image_data or []), endpoint_data[1]] if map_fun: data = list(map(map_fun, data)) return data class NebMethod(ValueEnum): """Common methods for NEB calculations. TODO: convert to StrEnum """ STANDARD = "standard" CLIMBING_IMAGE = "climbing_image" APPROX = "approx_neb" class HopFailureReason(ValueEnum): """Define failure modes for ApproxNEB calculations.""" ENDPOINT = "Endpoint structure relaxation failed." MIN_DIST = "Linear distance traversed by working ion is below threshold." MIN_IMAGE = "Too few image calculations succeeded." IMAGE_FAILURE = "No image calculation output." class BarrierAnalysis(BaseModel): """Define analysis schema for barrier calculations.""" energies: list[float] = Field( description="The energies of each frame along the reaction coordinate." ) frame_index: list[float] | None = Field( None, description="The fractional index along the reaction coordinate, between 0 and 1.", ) cubic_spline_pars: list[list[float]] | None = Field( None, description="Parameters of the cubic spline used to fit the energies." ) ts_frame_index: float | None = Field( None, description="The fractional index of the reaction coordinate." ) ts_energy: float | None = Field( None, description="The energy at the transition state." ) ts_in_frames: bool | None = Field( None, description="Whether the transition state is one of the computed snapshots.", ) forward_barrier: float | None = Field(None, description="The forward barrier.") reverse_barrier: float | None = Field(None, description="The reverse barrier.") @classmethod def from_energies( cls, energies: Sequence[float], frame_index: Sequence[float] | NDArray | None = None, spline_kwargs: dict[str, Any] | None = None, frame_match_tol: float = 1.0e-6, ) -> Self: """ Define basic NEB analysis tools. Parameters ---------- energies : Sequence of float The energies sorted by increasing frame index. Must include endpoints. frame_index : Sequence of float or None (default) If None, defaults to a linear interpolation between 0 and 1 for each energy in energies. If not None, specifies the indices of succcessful images. spline_kwargs : dict or None The kwargs to pass to the spline fit. Defaults to clamping the derivative to zero at the endpoints, consistent with the assumption that they represent minima along the potential energy surface. frame_match_tol : float = 1.e-6 The tolerance for matching the transition state frame index to the input frame indices. """ frame_index = frame_index or list(range(len(energies))) frame_index = np.array(frame_index) / max(frame_index) analysis: dict[str, Any] = { "energies": list(energies), "frame_index": list(frame_index), } energies = np.array(energies) # type: ignore[assignment] spline_kwargs = spline_kwargs or {"bc_type": "clamped"} spline_fit = CubicSpline(frame_index, energies, **spline_kwargs) analysis["cubic_spline_pars"] = spline_fit.c.tolist() crit_points = spline_fit.derivative().roots(extrapolate=False) analysis["ts_frame_index"] = -1 analysis["ts_energy"] = -np.inf for crit_point in crit_points: if (energy := spline_fit(crit_point)) > analysis[ "ts_energy" ] and spline_fit(crit_point, 2) <= 0.0: analysis["ts_frame_index"] = crit_point analysis["ts_energy"] = float(energy) analysis["ts_in_frames"] = any( abs(analysis["ts_frame_index"] - idx) < frame_match_tol * max(idx, frame_match_tol) for idx in frame_index ) analysis["forward_barrier"] = analysis["ts_energy"] - energies[0] analysis["reverse_barrier"] = analysis["ts_energy"] - energies[-1] return cls(**analysis) @arrow_incompatible class NebResult(BaseModel): """Container class to store high-level NEB calculation info. This is intended to be code-agnostic, whereas NebTaskDoc is VASP-specific. """ images: list[Structure | Molecule] | None = Field( None, description=( "Structures/molecules along the reaction pathway, " "including endpoints, after NEB." ), ) initial_images: list[Structure | Molecule] | None = Field( None, description="Structures/molecules along the reaction pathway, prior to NEB.", ) image_indices: list[int] | None = Field( None, description="The indexes corresponding to initial_images of all successful image calculations.", ) energies: list[float] | None = Field( None, description="Energies corresponding the structures in `images`." ) state: TaskState | None = Field( None, description="Whether the NEB calculation succeeded." ) neb_method: NebMethod | None = Field( None, description="The NEB method used for this calculation." ) forward_barrier: float | None = Field( None, description=( "Forward barrier for this reaction, " "i.e., the transition state energy minus " "the reactant / initial configuration energy." ), ) reverse_barrier: float | None = Field( None, description=( "Reverse barrier for this reaction, " "i.e., the transition state energy minus " "the product / final configuration energy." ), ) barrier_analysis: BarrierAnalysis | None = Field( None, description="Analysis of the reaction barrier." ) failure_reasons: list[HopFailureReason] | None = Field( None, description="Reasons why the barrier calculation failed." ) tags: list[str] | None = Field( None, description="List of string metadata about the calculation." ) identifier: str | None = Field(None, description="Identifier for the calculation.") dir_name: str | None = Field( None, description="Directory where calculation was run." ) @model_validator(mode="after") def set_barriers(self) -> Self: """Perform analysis on barrier if needed.""" if ( (not self.forward_barrier or not self.reverse_barrier) and isinstance(self.energies, list) and len(self.energies) > 0 ): self.barrier_analysis = BarrierAnalysis.from_energies( self.energies, frame_index=self.image_indices ) for k in ("forward", "reverse"): setattr( self, f"{k}_barrier", getattr(self.barrier_analysis, f"{k}_barrier", None), ) return self @property def barrier_energy_range(self) -> float | None: """The maximum computed energy minus the minimum computed energy along the path.""" if self.energies: return max(self.energies) - min(self.energies) return None @property def num_images(self) -> int | None: if self.images: return len(self.images) return None @type_override({"objects": str}) class NebIntermediateImagesDoc(BaseModel): """Schema for high-level intermediate image NEB data.""" energies: list[float] | None = Field( None, description="The final energies of the intermediate images." ) images: list[StructureType] | None = Field( None, description="Final structures for each intermediate image." ) initial_images: list[StructureType] | None = Field( None, description="The initial intermediate image structures." ) calculations: list[Calculation] | None = Field( None, description="Full calculation output for the intermediate images." ) dir_name: str | None = Field( None, description="Top-level NEB calculation directory." ) directories: list[str] | None = Field( None, description="List of the directories where the NEB images are located." ) state: TaskState | None = Field( None, description="Whether the NEB calculation succeeded." ) neb_method: NebMethod | None = Field( None, description="The NEB method used for this calculation." ) orig_inputs: CalculationInput | None = Field( None, description="The exact set of input parameters used to generate the current task document.", ) objects: list[dict[VaspObject, Any]] | None = Field( None, description="VASP objects for each image calculation." ) inputs: CalculationInput | None = Field( None, description="Inputs used in this calculation." ) custodian: list[CustodianDoc] | None = Field( None, description="Detailed custodian data for each VASP calculation contributing to the task document.", ) completed_at: NullableDateTimeType = Field( description="Timestamp for when this task was completed" ) task_label: str | None = Field( None, description="Label for the NEB calculation(s)." ) @field_serializer("objects", mode="wrap") def objects_serializer(self, d, default_serializer, info): default_serialized_object = default_serializer(d, info) format = info.context.get("format") if info.context else None if format == "arrow": return orjson.dumps(default_serialized_object) return default_serialized_object @field_validator("objects", mode="before") def objects_deserializer(cls, d): return orjson.loads(d) if isinstance(d, str) else d @classmethod def from_directory( cls, dir_name: str | Path, volumetric_files: Sequence[str] = _VOLUMETRIC_FILES, store_calculations: bool = True, **kwargs, ) -> Self: """ Return an NebTaskDoc from a single NEB calculation directory. This method populates only the image energies and calculations fields, and the endpoint structures. """ if isinstance(dir_name, str): dir_name = Path(dir_name) neb_directories = sorted(dir_name.glob("[0-9][0-9]")) image_directories = neb_directories[1:-1] image_calculations = [] initial_structures = [] image_structures = [] image_objects = [] for iimage, image_dir in enumerate(image_directories): vasp_files = _find_vasp_files(image_dir, volumetric_files=volumetric_files) calc, objects = Calculation.from_vasp_files( dir_name=image_dir, task_name=f"NEB image {iimage + 1}", vasprun_file=vasp_files["standard"]["vasprun_file"], outcar_file=vasp_files["standard"]["outcar_file"], contcar_file=vasp_files["standard"]["contcar_file"], volumetric_files=vasp_files["standard"].get("volumetric_files", []), oszicar_file=vasp_files["standard"].get("oszicar_file", None), vasprun_kwargs={ "parse_potcar_file": False, }, ) image_calculations.append(calc) image_structures.append(calc.output.structure) initial_structures.append(calc.input.structure) image_objects.append(objects) task_state = ( TaskState.SUCCESS if all( calc.has_vasp_completed == TaskState.SUCCESS for calc in image_calculations ) else TaskState.FAILED ) orig_inputs = _parse_orig_inputs(dir_name, suffix=".orig") inputs = _parse_orig_inputs(dir_name, suffix=None) neb_method = ( NebMethod.CLIMBING_IMAGE if inputs.incar.get("LCLIMB", False) else NebMethod.STANDARD ) return cls( calculations=image_calculations if store_calculations else None, images=image_structures, initial_images=initial_structures, dir_name=str(dir_name), directories=[str(img_dir) for img_dir in image_directories], orig_inputs=orig_inputs, inputs=inputs, objects=image_objects, neb_method=neb_method, # type: ignore[arg-type] state=task_state, energies=[calc.output.energy for calc in image_calculations], custodian=_parse_custodian(dir_name), completed_at=max(calc.completed_at for calc in image_calculations), **kwargs, ) class NebTaskDoc(NebResult): """Define schema for VASP NEB tasks.""" images: list[StructureType] | None = Field( # type: ignore[assignment] None, description=( "Structures (including endpoints) along the reaction pathway after NEB." ), ) initial_images: list[StructureType] | None = Field( # type: ignore[assignment] None, description="Structures (including endpoints) along the reaction pathway prior to NEB.", ) directories: list[str] | None = Field( None, description="Calculation directories for each image." ) calculations: list[Calculation] | None = Field( None, description="The VASP calculations associated with each image." ) last_updated: DateTimeType = Field( description="Timestamp for the most recent calculation for this task document", ) objects: list[dict[VaspObject, Any] | None] | None = Field( None, description="VASP objects associated with each image calculation." ) @classmethod def from_directories( cls, endpoint_directories: list[str | Path], neb_directory: str | Path, volumetric_files: Sequence[str] = _VOLUMETRIC_FILES, store_calculations: bool = True, **kwargs, ) -> Self: """ Return an NebTaskDoc from endpoint and NEB calculation directories. This method populates the endpoint and image fields completely, permitting an analysis of the barrier. """ ep_dirs = [Path(ep_dir).resolve() for ep_dir in endpoint_directories] neb_dir = Path(neb_directory).resolve() endpoint_calculations: list[Calculation | None] = [None, None] endpoint_objects = [None, None] for idx, endpoint_dir in enumerate(ep_dirs): vasp_files = _find_vasp_files( endpoint_dir, volumetric_files=volumetric_files ) if vasp_files.get("standard"): ep_key = "standard" else: max_rel_idx = max( int(k.split("relax")[-1]) for k in vasp_files if k.startswith("relax") ) ep_key = f"relax{max_rel_idx}" ( endpoint_calculations[idx], endpoint_objects[idx], ) = Calculation.from_vasp_files( dir_name=endpoint_dir, task_name=f"NEB endpoint {idx + 1}", vasprun_file=vasp_files[ep_key]["vasprun_file"], outcar_file=vasp_files[ep_key]["outcar_file"], contcar_file=vasp_files[ep_key]["contcar_file"], volumetric_files=vasp_files[ep_key].get("volumetric_files", []), oszicar_file=vasp_files[ep_key].get("oszicar_file", None), vasprun_kwargs={ "parse_potcar_file": False, }, ) img_dirs = sorted(neb_dir.glob("[0-9][0-9]")) ep_img_dirs = [img_dirs[0], img_dirs[-1]] endpoint_energies: list[float | None] = [None, None] endpoint_structures: list[Structure | None] = [None, None] for idx, ep_calc in enumerate(endpoint_calculations): if ep_calc is None: ep_dirs[idx] = ep_img_dirs[idx] endpoint_structures[idx] = Structure.from_file( zpath(f"{ep_dirs[idx]}/POSCAR") ) else: endpoint_structures[idx] = ep_calc.output.structure endpoint_energies[idx] = ep_calc.output.energy intermediate_images = NebIntermediateImagesDoc.from_directory( neb_dir, volumetric_files=volumetric_files, store_calculations=store_calculations, **kwargs, ) states = [ getattr(ep_calc, "state", TaskState.FAILED) for ep_calc in endpoint_calculations ] + [intermediate_images.state] state = TaskState.SUCCESS if any(calc_state == TaskState.FAILED for calc_state in states): state = TaskState.FAILED elif any(calc_state == TaskState.ERROR for calc_state in states): state = TaskState.ERROR images = _join_endpoint_and_image_data( endpoint_structures, intermediate_images.images ) calculations = None if store_calculations: calculations = _join_endpoint_and_image_data( endpoint_calculations, intermediate_images.calculations ) return cls( images=images, initial_images=_join_endpoint_and_image_data( endpoint_structures, intermediate_images.initial_images ), image_indices=list(range(len(images))), energies=_join_endpoint_and_image_data( endpoint_energies, intermediate_images.energies ), state=state, neb_method=intermediate_images.neb_method, dir_name=str(intermediate_images.dir_name), directories=_join_endpoint_and_image_data( ep_dirs, intermediate_images.directories, map_fun=str ), calculations=calculations, objects=_join_endpoint_and_image_data( endpoint_objects, intermediate_images.objects ), **kwargs, ) @arrow_incompatible class NebPathwayResult(BaseModel): # type: ignore[call-arg] """Class for containing multiple NEB calculations, as along a reaction pathway.""" hops: dict[str, NebResult] = Field( description="Dict of NEB calculations included in this calculation" ) forward_barriers: dict[str, float | None] | None = Field( None, description="Dict of the forward barriers computed here." ) reverse_barriers: dict[str, float | None] | None = Field( None, description="Dict of the reverse barriers computed here." ) identifier: str | None = Field(None, description="Identifier for the calculation.") tags: list[str] | None = Field( None, description="List of string metadata about the calculation." ) host_structure: StructureType | None = Field( None, description="The structure without active/mobile site(s)." ) host_formula: str | None = Field( None, description="The chemical formula of the structure without active site(s).", ) host_formula_reduced: str | None = Field( None, description="The reduced chemical formula of the structure without active site(s).", ) host_chemsys: str | None = Field( None, description="The chemical system for the structure without active site(s).", ) active_species: str | None = Field( None, description="The formula of the active/mobile species." ) @model_validator(mode="after") def set_top_levels(self) -> Self: """Set barriers and host structure metadata, if needed.""" for direction in ("forward", "reverse"): if getattr(self, f"{direction}_barriers", None) is None: setattr( self, f"{direction}_barriers", { idx: getattr(neb_calc, f"{direction}_barrier", None) for idx, neb_calc in self.hops.items() }, ) if self.host_structure is not None: self.host_formula = self.host_structure.formula self.host_formula_reduced = self.host_structure.reduced_formula self.host_chemsys = self.host_structure.chemical_system return self @property def max_barriers(self) -> dict[str, float | None] | None: """Retrieve the maximum barrier along each hop.""" fwd_barr = self.forward_barriers or {} rev_barr = self.reverse_barriers or {} return { hop_key: max( fwd_barr.get(hop_key) or float("-inf"), rev_barr.get(hop_key) or float("-inf"), ) for hop_key in self.hops if fwd_barr.get(hop_key) or rev_barr.get(hop_key) } or None @property def barrier_ranges(self) -> dict[str, float | None]: """Retrieve the max minus min computed energy along each hop.""" return { idx: neb_calc.barrier_energy_range for idx, neb_calc in self.hops.items() } ././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1787011028.973509 emmet_core-0.87.2/emmet/core/openff/0000755000175100017510000000000015240717725016723 5ustar00runnerrunner././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8232715 emmet_core-0.87.2/emmet/core/openff/__init__.py0000644000175100017510000000015315240717720021026 0ustar00runnerrunnerfrom emmet.core.openff.tasks import ( MoleculeSpec, ClassicalMDTaskDocument, MDTaskDocument, ) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8241093 emmet_core-0.87.2/emmet/core/openff/tasks.py0000644000175100017510000000673315240717720020426 0ustar00runnerrunner"""Schemas for classical MD package.""" from __future__ import annotations import zlib from datetime import datetime from pydantic import ( BaseModel, ConfigDict, Field, PlainSerializer, PlainValidator, WithJsonSchema, ) from emmet.core.io.pymatgen import Structure from typing_extensions import Annotated from emmet.core.utils import arrow_incompatible, utcnow from emmet.core.vasp.task_valid import TaskState def compressed_str_validator(s: str) -> str: try: compressed_bytes = bytes.fromhex(s) decompressed_bytes = zlib.decompress(compressed_bytes) return decompressed_bytes.decode("utf-8") except: # noqa return s def compressed_str_serializer(s: str) -> str: decompressed_bytes = s.encode("utf-8") return zlib.compress(decompressed_bytes).hex() # this type will take a string and automatically compress and # decompress it when it is serialized and deserialized CompressedStr = Annotated[ str, PlainValidator(compressed_str_validator), PlainSerializer(compressed_str_serializer), WithJsonSchema({"type": "string"}), ] class MoleculeSpec(BaseModel): """A molecule schema to be output by OpenMMGenerators.""" name: str count: int charge_scaling: float charge_method: str openff_mol: str # a tk.Molecule object serialized with to_json # def as_dict(self) -> dict: # return self.model_dump() # @classmethod # def from_dict(cls,dct): # return cls(**dct) @arrow_incompatible class MDTaskDocument(BaseModel): # type: ignore[call-arg] """Definition of the OpenMM task document.""" tags: list[str] | None = Field( [], title="tag", description="Metadata tagged to a given task." ) dir_name: str | None = Field(None, description="The directory for this MD task") state: TaskState | None = Field(None, description="State of this calculation") job_uuids: list[str] | None = Field( None, description="The job_uuids for all contributing jobs, this will only" "have a value if the taskdoc is generated by a Flow.", ) calcs_reversed: list | None = Field( None, title="Calcs reversed data", description="Detailed data for each MD calculation contributing to " "the task document.", ) interchange: CompressedStr | None = Field( None, description="An interchange object serialized to json." ) mol_specs: list[MoleculeSpec] | None = Field( None, description="Molecules within the system. Only makes sense " "for molecular systems.", ) structure: Structure | None = Field( None, title="Structure", description="The final structure for the simulation. Saved only " "if specified by job.", ) force_field: str | None = Field(None, description="The classical MD forcefield.") task_type: str | None = Field(None, description="The type of calculation.") # task_label: str | None= Field(None, description="A description of the task") # TODO: where does task_label get added last_updated: datetime = Field( default_factory=utcnow, description="Timestamp for the most recent calculation for this task document", ) model_config = ConfigDict(extra="allow") class ClassicalMDTaskDocument(MDTaskDocument): """Definition of the OpenMM task document.""" mol_specs: list[MoleculeSpec] | None = Field( None, description="Molecules within the system." ) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011028.9740493 emmet_core-0.87.2/emmet/core/openmm/0000755000175100017510000000000015240717725016741 5ustar00runnerrunner././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8241093 emmet_core-0.87.2/emmet/core/openmm/__init__.py0000644000175100017510000000017615240717720021051 0ustar00runnerrunnerfrom emmet.core.openmm.tasks import ( Calculation, CalculationInput, CalculationOutput, OpenMMTaskDocument, ) ././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1787011023.824219 emmet_core-0.87.2/emmet/core/openmm/calculations.py0000644000175100017510000000474415240717720022000 0ustar00runnerrunnerfrom datetime import datetime from pydantic import BaseModel, Field from emmet.core.openmm.tasks import Calculation class CalculationsDoc(BaseModel): """ A document for storing metadata from a list of OpenMM calculations. In each field, calculations are listed sequentially, in the order they were run. """ task_names: list[str] | None = Field(None, description="Names of tasks.") calc_types: list[str] | None = Field(None, description="Types of calculations.") elapsed_times: list[float | None] | None = Field( None, description="Elapsed time for calculations." ) steps: list[float | None] | None = Field( None, description="n_steps for calculations." ) step_sizes: list[float | None] | None = Field( None, description="Step sizes for each calculations." ) temperatures: list[float | None] | None = Field( None, description="Temperature for each calculations." ) pressures: list[float | None] | None = Field( None, description="Pressure for each calculations." ) friction_coefficients: list[float | None] | None = Field( None, description="Friction coefficients for each calculations.", ) completed_at: datetime | None = Field( None, description="Timestamp for when the final calculation completed.", ) job_uuid: str | None = Field( None, description="The UUID of the flow that generated this data." ) flow_uuid: str | None = Field( None, description="The UUID of the top level host from that job." ) @classmethod def from_calcs_reversed( cls, calcs_reversed: list[Calculation], job_uuid: str | None = None, flow_uuid: str | None = None, ) -> "CalculationsDoc": calcs = calcs_reversed[::-1] return CalculationsDoc( task_names=[calc.task_name for calc in calcs], calc_types=[calc.calc_type for calc in calcs], elapsed_times=[calc.output.elapsed_time for calc in calcs], steps=[calc.input.n_steps for calc in calcs], step_sizes=[calc.input.step_size for calc in calcs], temperatures=[calc.input.temperature for calc in calcs], pressures=[calc.input.pressure for calc in calcs], friction_coefficients=[calc.input.friction_coefficient for calc in calcs], completed_at=calcs[-1].completed_at, job_uuid=job_uuid, flow_uuid=flow_uuid, ) ././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1787011023.824219 emmet_core-0.87.2/emmet/core/openmm/tasks.py0000644000175100017510000001673315240717720020445 0ustar00runnerrunner"""Schemas for OpenMM tasks.""" from __future__ import annotations from pathlib import Path from typing import TYPE_CHECKING import pandas as pd # type: ignore[import-untyped] from pydantic import BaseModel, ConfigDict, Field from emmet.core.openff import MDTaskDocument # type: ignore[import-untyped] from emmet.core.openff.tasks import CompressedStr # type: ignore[import-untyped] from emmet.core.vasp.task_valid import TaskState if TYPE_CHECKING: from typing import Any from emmet.core.utils import arrow_incompatible, type_override @arrow_incompatible class CalculationInput(BaseModel): # type: ignore[call-arg] """OpenMM input settings for a job, these are the attributes of the OpenMMMaker.""" n_steps: int | None = Field( None, description="The number of simulation steps to run." ) step_size: float | None = Field( None, description="The size of each simulation step (picoseconds)." ) temperature: float | None = Field( None, description="The simulation temperature (kelvin)." ) pressure: float | None = Field( None, description="The simulation pressure (atmospheres)." ) friction_coefficient: float | None = Field( None, description=( "The friction coefficient for the integrator (inverse picoseconds)." ), ) platform_name: str | None = Field( None, description=( "The name of the OpenMM platform to use, passed to " "Interchange.to_openmm_simulation." ), ) platform_properties: dict | None = Field( None, description=( "Properties for the OpenMM platform, passed to " "Interchange.to_openmm_simulation." ), ) state_interval: int | None = Field( None, description=( "State is saved every `state_interval` timesteps. For no state, set to 0." ), ) state_file_name: str | None = Field( None, description="The name of the state file to save." ) traj_interval: int | None = Field( None, description=( "The trajectory is saved every `traj_interval` timesteps. For no trajectory, set to 0." ), ) wrap_traj: bool | None = Field( None, description="Whether to wrap trajectory coordinates." ) report_velocities: bool | None = Field( None, description="Whether to report velocities in the trajectory file." ) traj_file_name: str | None = Field( None, description="The name of the trajectory file to save." ) traj_file_type: str | None = Field( None, description="The type of trajectory file to save.", ) embed_traj: bool | None = Field( None, description="Whether to embed the trajectory blob in CalculationOutput.", ) model_config = ConfigDict(extra="allow") @type_override({"traj_blob": str}) class CalculationOutput(BaseModel): """OpenMM calculation output files and extracted data.""" dir_name: str | None = Field(None, description="The directory for this OpenMM task") traj_file: str | None = Field( None, description="Path to the trajectory file relative to `dir_name`" ) traj_blob: CompressedStr | None = Field( None, description="Trajectory file bytes blob hex encoded to a string" ) state_file: str | None = Field( None, description="Path to the state file relative to `dir_name`" ) steps_reported: list[int] | None = Field( None, description="Steps where outputs are reported" ) time: list[float] | None = Field(None, description="List of times") potential_energy: list[float] | None = Field( None, description="List of potential energies" ) kinetic_energy: list[float] | None = Field( None, description="List of kinetic energies" ) total_energy: list[float] | None = Field(None, description="List of total energies") temperature: list[float] | None = Field(None, description="List of temperatures") volume: list[float] | None = Field(None, description="List of volumes") density: list[float] | None = Field(None, description="List of densities") elapsed_time: float | None = Field( None, description="Elapsed time for the calculation (seconds)." ) @classmethod def from_directory( cls, dir_name: Path | str, state_file_name: str, traj_file_name: str, elapsed_time: float | None = None, embed_traj: bool = False, ) -> CalculationOutput: """Extract data from the output files in the directory.""" state_file = Path(dir_name) / state_file_name column_name_map: dict[str, str] = { '#"Step"': "steps_reported", "Potential Energy (kJ/mole)": "potential_energy", "Kinetic Energy (kJ/mole)": "kinetic_energy", "Total Energy (kJ/mole)": "total_energy", "Temperature (K)": "temperature", "Box Volume (nm^3)": "volume", "Density (g/mL)": "density", } state_is_not_empty = state_file.exists() and state_file.stat().st_size > 0 if state_is_not_empty: data = pd.read_csv(state_file, header=0) data = data.rename(columns=column_name_map) data = data.filter(items=list(column_name_map.values())) attributes: dict[str, Any] = data.to_dict(orient="list") # type: ignore[assignment] else: attributes = {name: None for name in column_name_map.values()} state_file_name = None # type: ignore[assignment] traj_file = Path(dir_name) / traj_file_name traj_is_not_empty = traj_file.exists() and traj_file.stat().st_size > 0 traj_file_name = traj_file_name if traj_is_not_empty else None # type: ignore traj_blob: str | None = None if traj_is_not_empty and embed_traj: with open(traj_file, "rb") as f: traj_blob = f.read().hex() return CalculationOutput( dir_name=str(dir_name), elapsed_time=elapsed_time, traj_file=traj_file_name, state_file=state_file_name, traj_blob=traj_blob, **attributes, ) @arrow_incompatible class Calculation(BaseModel): """All input and output data for an OpenMM calculation.""" dir_name: str | None = Field( None, description="The directory for this OpenMM calculation" ) has_openmm_completed: TaskState | bool | None = Field( None, description="Whether OpenMM completed the calculation successfully" ) input: CalculationInput | None = Field( None, description="OpenMM input settings for the calculation" ) output: CalculationOutput | None = Field( None, description="The OpenMM calculation output" ) completed_at: str | None = Field( None, description="Timestamp for when the calculation was completed" ) task_name: str | None = Field( None, description="Name of task given by custodian (e.g., relax1, relax2)" ) calc_type: str | None = Field( None, description="Return calculation type (run type + task_type). or just new thing", ) class OpenMMTaskDocument(MDTaskDocument): """Definition of the OpenMM task document.""" calcs_reversed: list[Calculation] | None = Field( None, title="Calcs reversed data", description="Detailed data for each OpenMM calculation contributing to the " "task document.", ) ././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1787011023.824219 emmet_core-0.87.2/emmet/core/optimade.py0000644000175100017510000001177215240717720017625 0ustar00runnerrunnerfrom __future__ import annotations import string from datetime import datetime from monty.fractions import gcd from optimade.models import Species, StructureResourceAttributes from pydantic import Field from emmet.core.io.pymatgen import Composition, formula_double_format, Structure from typing_extensions import TypedDict from emmet.core.base import EmmetBaseModel from emmet.core.types.typing import IdentifierType from emmet.core.utils import arrow_incompatible letters = "ABCDEFGHIJKLMNOPQRSTUVXYZ" def optimade_form(comp: Composition): symbols = sorted([str(e) for e in comp.keys()]) numbers = set([comp[s] for s in symbols if comp[s]]) reduced_form = [] for s in symbols: reduced_form.append(s) if comp[s] != 1 and len(numbers) > 1: reduced_form.append(str(int(comp[s]))) return "".join(reduced_form) def optimade_anonymous_form(comp: Composition): reduced = comp.element_composition if all(x == int(x) for x in comp.values()): reduced /= gcd(*(int(i) for i in comp.values())) anon = [] for e, amt in zip(string.ascii_uppercase, sorted(reduced.values(), reverse=True)): if amt == 1: amt_str = "" elif abs(amt % 1) < 1e-8: amt_str = str(int(amt)) else: amt_str = str(amt) anon.append(str(e)) anon.append(amt_str) return "".join(anon) def hill_formula(comp: Composition) -> str: """ :return: Hill formula. The Hill system (or Hill notation) is a system of writing empirical chemical formulas, molecular chemical formulas and components of a condensed formula such that the number of carbon atoms in a molecule is indicated first, the number of hydrogen atoms next, and then the number of all other chemical elements subsequently, in alphabetical order of the chemical symbols. When the formula contains no carbon, all the elements, including hydrogen, are listed alphabetically. """ c = comp.element_composition elements = sorted([el.symbol for el in c.keys()]) form_elements = [] if "C" in elements: form_elements.append("C") if "H" in elements: form_elements.append("H") form_elements.extend([el for el in elements if el != "C" and el != "H"]) else: form_elements = elements formula = [ f"{el}{formula_double_format(c[el]) if c[el] != 1 else ''}" for el in form_elements ] return "".join(formula) class TypedStabilityDict(TypedDict): thermo_id: str energy_above_hull: float formation_energy_per_atom: float last_updated_thermo: datetime @arrow_incompatible class OptimadeMaterialsDoc(StructureResourceAttributes, EmmetBaseModel): """ Optimade Structure resource with a few extra MP specific fields for materials Thermo calculations are stored as a nested dict, with keys corresponding to the functional used to perform stability calc, i.e., R2SCAN, GGA_GGA+U_R2SCAN, or GGA_GGA+U """ material_id: IdentifierType | None = Field( None, description="The ID of the material" ) chemical_system: str stability: dict[str, TypedStabilityDict] @classmethod def from_structure( cls, structure: Structure, last_updated_structure: datetime, thermo_calcs: dict, material_id: IdentifierType | None = None, **kwargs, ) -> StructureResourceAttributes: structure.remove_oxidation_states() return OptimadeMaterialsDoc( material_id=material_id, chemical_system=structure.composition.chemical_system, stability=thermo_calcs, elements=sorted(set([e.symbol for e in structure.composition.elements])), nelements=len(structure.composition.elements), elements_ratios=list(structure.composition.fractional_composition.values()), chemical_formula_descriptive=optimade_form(structure.composition), chemical_formula_reduced=optimade_form( structure.composition.get_reduced_composition_and_factor()[0] ), chemical_formula_anonymous=optimade_anonymous_form(structure.composition), chemical_formula_hill=hill_formula(structure.composition), dimension_types=[1, 1, 1], nperiodic_dimensions=3, lattice_vectors=structure.lattice.matrix.tolist(), cartesian_site_positions=[site.coords.tolist() for site in structure], nsites=len(structure), species=list( { site.species_string: Species( chemical_symbols=[site.species_string], concentration=[1.0], name=site.species_string, ) for site in structure }.values() ), species_at_sites=[site.species_string for site in structure], last_modified=last_updated_structure, structure_features=[], **kwargs, ) ././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1787011023.824219 emmet_core-0.87.2/emmet/core/oxidation_states.py0000644000175100017510000001463715240717720021407 0ustar00runnerrunnerfrom __future__ import annotations import logging from collections import defaultdict from copy import deepcopy from typing import TYPE_CHECKING, Any import numpy as np from pydantic import Field from emmet.core.io.pymatgen import BVAnalyzer, Specie from emmet.core.material_property import PropertyDoc from emmet.core.types.enums import ValueEnum from emmet.core.types.pymatgen_types.structure_adapter import StructureType if TYPE_CHECKING: from emmet.core.types.typing import MaterialIdentifierType class OxiStateAssigner(ValueEnum): MANUAL = "Manual" BVA = "Bond Valence Analysis" GUESS = "Oxidation State Guess" class OxidationStateDoc(PropertyDoc): """Oxidation states computed from the structure""" property_name: str = "oxidation" structure: StructureType = Field( ..., description="The structure used in the generation of the oxidation state data.", ) possible_species: list[str] = Field( description="Possible charged species in this material." ) possible_valences: list[float] = Field( description="List of valences for each site in this material." ) average_oxidation_states: dict[str, float] = Field( description="Average oxidation states for each unique species." ) method: OxiStateAssigner | None = Field( None, description="Method used to compute oxidation states." ) @classmethod def from_structure( cls, structure: StructureType, material_id: MaterialIdentifierType | None = None, **kwargs, ): # Check if structure already has oxidation states, # if so pass this along unchanged with "method" == "manualx" d: dict[str, Any] struct_valences: list[float | None] = [] species = [] method = None if _method := kwargs.pop("method", None): method = _method.lower() site_oxidation_list = defaultdict(list) for site in structure: if (oxi_state := getattr(site.species, "oxi_state", None)) and hasattr( site.species, "element" ): site_oxidation_list[site.species.element].append(oxi_state) species.append(site.species) struct_valences.append(oxi_state) average_oxidation_states: dict[str, float] = { str(el): np.mean(oxi_states) for el, oxi_states in site_oxidation_list.items() } if any(struct_valences) and (not method): d = { "possible_species": species, "possible_valences": struct_valences, "average_oxidation_states": average_oxidation_states, "method": OxiStateAssigner.MANUAL, "state": "successful", } return super().from_structure( meta_structure=structure, material_id=material_id, **d, **kwargs ) # otherwise, continue with assignment structure.remove_oxidation_states() # Null document d = { "possible_species": [], "possible_valences": [], "average_oxidation_states": {}, "method": method or OxiStateAssigner.BVA, "material_id": material_id, } if d["method"] == OxiStateAssigner.BVA: # BVAnalyzer (through SpaceGroupAnalyzer) is sensitive to magnetic configuration # -> magmoms can be removed for improved reliablity during oxi state analysis, # but original structure should be passed as meta_structure to preserve data if "magmom" in structure.site_properties: meta_structure = deepcopy(structure) structure.remove_site_property("magmom") else: meta_structure = structure try: bva = BVAnalyzer() valences = bva.get_valences(structure) possible_species = { str(Specie(str(structure[idx].specie), oxidation_state=valence)) for idx, valence in enumerate(valences) } structure.add_oxidation_state_by_site(valences) # construct a dict of average oxi_states for use # by MP2020 corrections. The format should mirror # the output of the first element from Composition.oxi_state_guesses() # e.g. {'Li': 1.0, 'O': -2.0} site_oxidation_list = defaultdict(list) for site in structure: site_oxidation_list[site.specie.element].append( site.specie.oxi_state ) oxi_state_dict = { str(el): np.mean(oxi_states) for el, oxi_states in site_oxidation_list.items() # type: ignore } d.update( possible_species=list(possible_species), possible_valences=valences, average_oxidation_states=oxi_state_dict, ) except Exception: logging.debug( f"BVAnalyzer failed for {structure.composition.reduced_composition}. Trying oxi_state_guesses." ) d["method"] = OxiStateAssigner.GUESS if d["method"] == OxiStateAssigner.GUESS: try: first_oxi_state_guess = structure.composition.oxi_state_guesses( max_sites=-50 )[0] valences = [ first_oxi_state_guess[site.species_string] for site in structure ] possible_species = { str(Specie(el, oxidation_state=valence)) for el, valence in first_oxi_state_guess.items() } structure.add_oxidation_state_by_site(valences) d.update( possible_species=list(possible_species), possible_valences=valences, average_oxidation_states=first_oxi_state_guess, ) except Exception as e: logging.error("Oxidation state guess failed with: {}".format(e)) d["warnings"] = ["Oxidation state guessing failed."] d["state"] = "unsuccessful" d["method"] = None return super().from_structure( meta_structure=meta_structure, **d, **kwargs, ) ././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1787011023.824219 emmet_core-0.87.2/emmet/core/phonon.py0000644000175100017510000014513515240717720017325 0ustar00runnerrunner"""Define schemas for DFPT, phonopy, and pheasy-derived phonon data.""" from __future__ import annotations from functools import cached_property from pathlib import Path from typing import TYPE_CHECKING, Annotated import numpy as np import yaml # type: ignore[import-untyped] from monty.io import zopen from monty.os.path import zpath from pydantic import BaseModel, BeforeValidator, Field, PlainSerializer, PrivateAttr from typing_extensions import Literal, TypedDict from emmet.core.band_theory import BandStructure, BandTheoryBase from emmet.core.base import CalcMeta from emmet.core.io.pymatgen import ( BandStructureSymmLine, CompletePhononDos, Kpoint, Lattice, PhononBandStructureSymmLine, ) from emmet.core.io.pymatgen import PhononDos as PhononDosObject from emmet.core.io.pymatgen import Spin, Structure from emmet.core.math import Matrix3D, Tensor4R, Vector3D from emmet.core.polar import BornEffectiveCharges, DielectricDoc, IRDielectric from emmet.core.structure import StructureMetadata from emmet.core.types.enums import DocEnum, ValueEnum from emmet.core.types.pymatgen_types.structure_adapter import StructureType from emmet.core.types.typing import DateTimeType, FSPathType, IdentifierType from emmet.core.utils import get_num_formula_units, type_override if TYPE_CHECKING: from collections.abc import Sequence from typing import Any from typing_extensions import Self DEFAULT_PHONON_FILES = { "structure": "POSCAR", "phonon_bandstructure": "band.yaml", # chosen as Phonopy default "phonon_dos": "total_dos.dat", # chosen as Phonopy default "projected_dos": "projected_dos.dat", # chosen as Phonopy default "force_constants": "FORCE_CONSTANTS", # chosen as Phonopy default "born": "born.npz", "epsilon_static": "epsilon_static.npz", "epsilon_static_and_born": "BORN", "phonopy_output": "phonopy.yaml", } class PhononMethod(ValueEnum): """Define common methods for computed phonon properties.""" DFPT = "dfpt" PHONOPY = "phonopy" PHEASY = "pheasy" class PhononDOS(BandTheoryBase): """Define schema of pymatgen phonon density of states.""" frequencies: list[float] = Field(description="The phonon frequencies in THz.") densities: list[float] = Field(description="The phonon density of states.") projected_densities: list[list[float]] | None = Field( None, description="The projected phonon density of states." ) @classmethod def from_pmg(cls, config: PhononDosObject | CompletePhononDos | dict) -> Any: """Correctly parse pymatgen-like objects.""" remap = { "structure": "structure", "frequencies": "frequencies", "densities": "densities", "projected_densities": "pdos", } if isinstance(config, PhononDosObject | CompletePhononDos): cls_config = {k: getattr(config, v, None) for k, v in remap.items()} if isinstance(pdos_as_dict := cls_config.get("projected_densities"), dict): if not cls_config["structure"]: raise ValueError( "Cannot parse atom-projected phonon density of states without a structure." ) cls_config["projected_densities"] = [ list(pdos_as_dict.get(site, [])) for site in cls_config["structure"] ] return cls(**cls_config) return cls(**{k: config.get(v) for k, v in remap.items()}) @cached_property def to_pmg(self) -> PhononDosObject | CompletePhononDos: """Get / cache corresponding pymatgen object.""" dos = PhononDosObject(frequencies=self.frequencies, densities=self.densities) if self.projected_densities and self.structure: return CompletePhononDos( self.structure, dos, dict(zip(self.structure, self.projected_densities, strict=True)), ) return dos @classmethod def from_phonopy( cls, phonon_dos_file: FSPathType, structure_file: FSPathType | None = None, projected_dos_file: FSPathType | None = None, **kwargs, ) -> Self: """Create a PhononDOS from phonopy .dat output. Ref. for data formats used by phonopy: - total_dos.dat : https://phonopy.github.io/phonopy/output-files.html#total-dos-dat-and-projected-dos-dat - projected_dos.dat : https://phonopy.github.io/phonopy/output-files.html#file-format-of-projected-dos-dat Parameters ----------- phonon_dos_file (FSPathType) : path to total_dos.dat structure_file (FSPathType or None) : path to a POSCAR-like object, either the base computational cell (POSCAR) or the supercell used in the phonon calculation (SPOSCAR). projected_dos_file (FSPathType or None) : path to projected_dos.dat **kwargs : any other kwargs to pass into the PhononDOS constructor """ phonopy_dos: dict[str, Any] = { k: [] for k in ( "frequencies", "densities", ) } # can handle gzipped files total_dos = np.loadtxt(phonon_dos_file) phonopy_dos["frequencies"] = total_dos[:, 0].tolist() phonopy_dos["densities"] = total_dos[:, 1].tolist() if structure_file: phonopy_dos["structure"] = Structure.from_file(structure_file) if projected_dos_file: # First column are frequencies, can skip these in parsing since # they should be the same as in the total DOS. proj_dos = np.loadtxt(projected_dos_file) phonopy_dos["projected_densities"] = [ proj_dos[:, i].tolist() for i in range(1, proj_dos.shape[1]) ] return cls(**phonopy_dos, **kwargs) class ShreddedEigendisplacements(TypedDict): real: list[list[list[tuple[float, float, float]]]] imag: list[list[list[tuple[float, float, float]]]] _EIGENMODE_CTYPE = list[list[list[tuple[complex, complex, complex]]]] _EIGENMODE_RTYPE = list[list[list[tuple[float, float, float]]]] def _ser_eigenmode(eigenmode: _EIGENMODE_CTYPE) -> dict[str, _EIGENMODE_RTYPE] | None: if not eigenmode: return None eigv = np.array(eigenmode) return {k: getattr(eigv, k).tolist() for k in ("real", "imag")} def _deser_eigenmode( dct: _EIGENMODE_CTYPE | dict[str, _EIGENMODE_RTYPE], ) -> _EIGENMODE_CTYPE | None: if isinstance(dct, dict): try: real = np.array(dct["real"]) imag = np.array(dct["imag"]) return real + 1.0j * imag # type: ignore[return-value] except Exception: return None return dct def _recover_labels_from_segments( phonopy_bandstructure: dict, structure_file: FSPathType | None, path_convention: str, tol: float = 1e-3, ) -> dict[str, list[float]]: """Reconstruct ``labels_dict`` for a phonopy band.yaml that lacks per-entry ``label`` keys by matching segment-boundary q-positions to the canonical high-symmetry kpoint labels of the requested path convention. Matching tries three transformations to find an equivalence between each boundary q-position and a canonical kpoint: identity, integer reciprocal-lattice translations, and the structure's point group operations applied to the boundary q-position. This is required because band.yaml q-positions are often related to canonical labels by point-group symmetry, not only by lattice translations. When two boundaries match the same canonical label at different q-positions (i.e. visit the same physical high-symmetry point along different symmetry-equivalent path segments), later occurrences are suffixed with primes (e.g. ``"X"`` then ``"X'"`` then ``"X''"``) so every boundary in the band.yaml receives a tick on the plot. Args: phonopy_bandstructure: parsed band.yaml dict; must contain ``phonon``, ``segment_nqpoint``, and either an embedded structure (``lattice`` + ``points``) or a separate ``structure_file``. structure_file: optional path to a structure file used in preference to the band.yaml's embedded structure data. path_convention: kpath convention name passed to ``pymatgen.symmetry.bandstructure.HighSymmKpath`` (e.g. ``"latimer_munro"``, ``"setyawan_curtarolo"``, ``"hinuma"``). tol: tolerance for matching a boundary q-position to a canonical kpoint (in fractional reciprocal-lattice coordinates). Returns: dict[str, list[float]]: ``{label: q-position}`` for every boundary that found a match within ``tol``. May be empty if no boundaries match. Errors from ``HighSymmKpath`` (e.g. missing ``seekpath`` dependency for the ``"hinuma"`` convention) propagate. """ from pymatgen.symmetry.analyzer import SpacegroupAnalyzer from pymatgen.symmetry.bandstructure import HighSymmKpath phonon_entries = phonopy_bandstructure["phonon"] if not phonon_entries: return {} segment_nqpoint = phonopy_bandstructure.get("segment_nqpoint") if not segment_nqpoint: return {} if structure_file: struct = Structure.from_file(structure_file) else: latt_raw = phonopy_bandstructure.get("lattice") pts = phonopy_bandstructure.get("points") if latt_raw is None or not pts: return {} struct = Structure( lattice=Lattice(np.asarray(latt_raw)), species=[p["symbol"] for p in pts], coords=[p["coordinates"] for p in pts], coords_are_cartesian=False, ) # Walk segment boundaries (first q-point + last q-point of each segment). boundary_idxs = [0] running = 0 for s in segment_nqpoint: running += s boundary_idxs.append(running - 1) hsk = HighSymmKpath(struct, path_type=path_convention) canonical = hsk.kpath.get("kpoints", {}) # {label: frac_coords} if not canonical: return {} # Consider unit-cell translations when matching: phonopy may emit a # q-position related to a canonical kpoint by an integer lattice shift. shifts = [ np.array(s) for s in ( (0, 0, 0), (1, 0, 0), (-1, 0, 0), (0, 1, 0), (0, -1, 0), (0, 0, 1), (0, 0, -1), ) ] # Point-group operations on fractional reciprocal coordinates. Boundary # q-positions in band.yaml are frequently equivalent to canonical labels # only after applying one of these (e.g. rhombohedral cells where the # path traverses symmetry-related copies of the same high-symmetry # point). Falls back to identity-only matching if the SpacegroupAnalyzer # fails for any reason (unusual cells, malformed structure, etc.). try: sym_ops = SpacegroupAnalyzer(struct).get_point_group_operations(cartesian=False) except Exception: sym_ops = [] labels_dict: dict[str, list[float]] = {} for idx in boundary_idxs: q = np.asarray(phonon_entries[idx]["q-position"]) best_label = None best_dist = float("inf") for label, kc in canonical.items(): kc_arr = np.asarray(kc) # Try identity first, then each symmetry operation. for op in (None, *sym_ops): q_eff = q if op is None else op.operate(q) for shift in shifts: d = float(np.linalg.norm(q_eff - (kc_arr + shift))) if d < best_dist: best_dist = d best_label = label if best_label is None or best_dist > tol: continue # Disambiguate duplicate labels that come from symmetry-equivalent # but distinct path points. The first occurrence keeps the canonical # label; later occurrences are suffixed with primes. candidate = best_label suffix = 0 while candidate in labels_dict and not np.allclose( labels_dict[candidate], q, atol=tol ): suffix += 1 candidate = best_label + "'" * suffix labels_dict.setdefault(candidate, q.tolist()) return labels_dict @type_override({"eigendisplacements": ShreddedEigendisplacements}) class PhononBS(BandStructure): """Define schema of pymatgen phonon band structure.""" has_nac: bool = Field( False, description="Whether the calculation includes non-analytical corrections at Gamma.", ) frequencies: list[list[float]] = Field( description="The eigen-frequencies, with the first index representing the band, and the second the k-point.", validation_alias="bands", ) eigendisplacements: Annotated[ _EIGENMODE_CTYPE | None, BeforeValidator(_deser_eigenmode), PlainSerializer(_ser_eigenmode), ] = Field(None, description="Phonon eigendisplacements in Cartesian coordinates.") _primitive_structure: Structure | None = PrivateAttr(None) @classmethod def from_pmg(cls, config: PhononBandStructureSymmLine | dict) -> Any: """Ensure fields are correctly populated.""" config = ( config.as_dict() if isinstance(config, PhononBandStructureSymmLine) else config ) # legacy data contains abipy structure objects if (struct := config.get("structure")) and not isinstance(struct, Structure): config["structure"] = Structure.from_dict(struct) # remap legacy fields for k, v in { "lattice_rec": "reciprocal_lattice", "bands": "frequencies", }.items(): if config.get(k): config[v] = config.pop(k) if isinstance(config["reciprocal_lattice"], dict): config["reciprocal_lattice"] = config["reciprocal_lattice"].get("matrix") return cls(**config) @property def primitive_structure(self) -> Structure | None: """Cache primitive structure for use in computing entropy, heat capacity, etc.""" if self.structure and not self._primitive_structure: self._primitive_structure = self.structure.get_primitive_structure() return self._primitive_structure @cached_property def to_pmg(self) -> PhononBandStructureSymmLine: """Get / cache corresponding pymatgen object.""" rlatt = Lattice(self.reciprocal_lattice) eigendisplacements = ( None if self.eigendisplacements is None else np.array(self.eigendisplacements) ) return PhononBandStructureSymmLine( [Kpoint(q, lattice=rlatt).frac_coords for q in self.qpoints], # type: ignore[misc] np.array(self.frequencies), rlatt, has_nac=self.has_nac, eigendisplacements=eigendisplacements, structure=self.structure, labels_dict={ k: Kpoint(v, lattice=rlatt).frac_coords for k, v in (self.labels_dict or {}).items() }, coords_are_cartesian=False, ) @property def _to_pmg_es_bs(self) -> BandStructureSymmLine: """Convenience method for crystal toolkit to create electronic-structure style band structure object.""" rlatt = Lattice(self.reciprocal_lattice) return BandStructureSymmLine( [Kpoint(q, lattice=rlatt).frac_coords for q in self.qpoints], # type: ignore[arg-type] {Spin.up: np.array(self.frequencies)}, # type: ignore[dict-item] rlatt, 1e-6, # There is no Fermi level in a Phonon DOS (these are bosons definitionally) but we want to plot the bands from zero. { k: Kpoint(v, lattice=rlatt).frac_coords # type: ignore[misc] for k, v in (self.labels_dict or {}).items() }, coords_are_cartesian=False, structure=self.structure, ) @classmethod def from_phonopy( cls, phonon_bandstructure_file: FSPathType, structure_file: FSPathType | None = None, path_convention: str | None = None, label_match_tol: float = 1e-3, **kwargs, ) -> Self: """Create a PhononBS from phonopy .yaml output. Args: phonon_bandstructure_file (FSPathType) : path to a band.yaml-like file. structure_file (FSPathType or None) : path to a POSCAR-like structure file. If provided, takes precedence over the structure embedded in the band.yaml. path_convention (str or None) : name of the high-symmetry path convention used to generate the band.yaml (e.g. ``"latimer_munro"``, ``"setyawan_curtarolo"``, ``"hinuma"``). When provided AND the band.yaml does not already carry per-entry ``label`` keys, `from_phonopy` reconstructs ``labels_dict`` by matching segment-boundary q-positions against `pymatgen.symmetry.bandstructure.HighSymmKpath` labels for the requested convention. If the lookup fails for any reason (e.g. missing optional `seekpath` dep for ``"hinuma"``, or no match within `label_match_tol`), label recovery is skipped silently. label_match_tol (float) : tolerance (in fractional reciprocal-lattice coordinates) for matching boundary q-positions to canonical high-symmetry kpoint labels. Defaults to 1e-3. **kwargs : other kwargs to pass to the class constructor """ with zopen(phonon_bandstructure_file, "rt") as f: phonopy_bandstructure = yaml.safe_load(f.read()) phonon_entries = phonopy_bandstructure["phonon"] qpoints = [entry["q-position"] for entry in phonon_entries] # Phonopy's band.yaml stores frequencies per q-point (one entry per # q-point, each containing a list of bands). The `PhononBS` schema # expects the transpose: first index = band, second = q-point. n_bands = len(phonon_entries[0]["band"]) if phonon_entries else 0 frequencies = [ [ phonon_entries[q_idx]["band"][b_idx]["frequency"] for q_idx in range(len(phonon_entries)) ] for b_idx in range(n_bands) ] # Prefer per-entry labels when present (phonopy with BAND_LABELS set). labels_dict = { entry["label"]: entry["q-position"] for entry in phonon_entries if entry.get("label") is not None } # Fall back to reconstructing labels via HighSymmKpath when the # caller tells us which path convention was used to generate the # band.yaml. Pheasy-produced band.yamls lack per-entry labels but # do include segment boundary metadata, which is enough to look up # canonical high-symmetry kpoint labels. if not labels_dict and path_convention: labels_dict = ( _recover_labels_from_segments( phonopy_bandstructure, structure_file=structure_file, path_convention=path_convention, tol=label_match_tol, ) or {} ) phonopy_bandstructure.update( qpoints=qpoints, frequencies=frequencies, ) if labels_dict: phonopy_bandstructure.setdefault("labels_dict", labels_dict) if structure_file: phonopy_bandstructure["structure"] = Structure.from_file(structure_file) return cls(**phonopy_bandstructure, **kwargs) class SumRuleChecks(BaseModel): """Container class for defining sum rule checks.""" asr: float | None = Field( None, description="The violation of the acoustic sum rule." ) cnsr: float | None = Field( None, description="The violation of the charge neutral sum rule." ) class PhononComputationalSettings(BaseModel): """Collection to store computational settings for the phonon computation.""" # could be optional and implemented at a later stage? npoints_band: int | None = Field( None, description="number of points for band structure computation" ) kpath_scheme: str | None = Field(None, description="indicates the kpath scheme") kpoint_density_dos: int | None = Field( None, description="number of points for computation of free energies and densities of states", ) class ThermalDisplacementData(BaseModel): """Collection to store information on the thermal displacement matrices.""" freq_min_thermal_displacements: float | None = Field( None, description="cutoff frequency in THz to avoid numerical issues in the " "computation of the thermal displacement parameters", ) thermal_displacement_matrix_cif: list[list[Matrix3D]] | None = Field( None, description="field including thermal displacement matrices in CIF format" ) thermal_displacement_matrix: list[list[Matrix3D]] | None = Field( None, description="field including thermal displacement matrices in Cartesian " "coordinate system", ) temperatures_thermal_displacements: list[float] | None = Field( None, description="temperatures at which the thermal displacement matrices" "have been computed", ) class PhononBSDOSTask(StructureMetadata): """Phonon band structures and density of states data.""" identifier: IdentifierType | None = Field( None, description="The identifier of this phonon analysis task." ) phonon_method: PhononMethod | None = Field( None, description="The method used to calculate phonon properties." ) epsilon_static: Matrix3D | None = Field( None, description="The high-frequency dielectric constant." ) epsilon_electronic: Matrix3D | None = Field( None, description="The electronic contribution to the high-frequency dielectric constant.", ) born: list[Matrix3D] | None = Field( None, description="Born charges, only for symmetrically inequivalent atoms", ) last_updated: DateTimeType = Field( description="Timestamp for the most recent calculation for this Material document.", ) sum_rules_breaking: SumRuleChecks | None = Field( None, description="Deviations from sum rules.", ) structure: StructureType | None = Field( None, description="Structure used in the calculation." ) total_dft_energy: float | None = Field( None, description="total DFT energy in eV/atom." ) volume_per_formula_unit: float | None = Field( None, description="volume per formula unit in Angstrom**3." ) formula_units: int | None = Field(None, description="Formula units per cell.") supercell_matrix: Matrix3D | None = Field( None, description="matrix describing the supercell." ) primitive_matrix: Matrix3D | None = Field( None, description="matrix describing relationship to primitive cell." ) code: str | None = Field( None, description="String describing the code for the computation." ) post_process_settings: PhononComputationalSettings | None = Field( None, description="Field including settings for the post processing code, e.g., phonopy.", ) thermal_displacement_data: ThermalDisplacementData | None = Field( None, description="Includes all data of the computation of the thermal displacements", ) calc_meta: list[CalcMeta] | None = Field( None, description="Metadata for individual calculations used to build this document.", ) @cached_property def charge_neutral_sum_rule(self) -> Matrix3D | None: r"""Sum of Born effective charges over sites should be zero. See Eq. 46 of https://doi.org/10.1103/PhysRevB.55.10355: $$ \sum_{i=1}^N Z_{i\alpha\beta} $$ where $Z_{i\alpha\beta}$ is the N x 3 x 3 Born effective charge tensor, $i = 1,2,...,N$ ranges up to the number of atoms $N$, and $\alpha, \beta = x, y, z$ are Cartesian directions. This returns a 3 x 3 matrix. The maximum deviation from the sum rule is checked by `check_sum_rule_deviations`. """ if self.born: bec = np.array(self.born) return tuple(tuple(row) for row in np.sum(bec, axis=0).tolist()) return None @property def check_sum_rule_deviations(self) -> SumRuleChecks: """Report deviations from sum rules.""" if not self.sum_rules_breaking: self.sum_rules_breaking = SumRuleChecks( **{ k: ( np.abs(getattr(self, attr)).max() if getattr(self, attr) is not None else None ) for k, attr in { "asr": "acoustic_sum_rule", "cnsr": "charge_neutral_sum_rule", }.items() } ) return self.sum_rules_breaking class PhononBSDOSDoc(PhononBSDOSTask): """Built data version of PhononBSDOSTask.""" phonon_bandstructure: PhononBS | None = Field( None, description="Phonon band structure object.", ) phonon_dos: PhononDOS | None = Field( None, description="Phonon density of states object.", ) # needed, e.g. to compute Grueneisen parameter etc force_constants: list[list[Matrix3D]] | None = Field( None, description="Force constants between every pair of atoms in the structure" ) @classmethod def migrate_fields(cls, **config) -> Any: """Migrate legacy input fields.""" # This block is for older DFPT data for k, v in { "ph_dos": "phonon_dos", "ph_bs": "phonon_bandstructure", "e_total": "epsilon_static", "e_electronic": "epsilon_electronic", "becs": "born", }.items(): if config.get(k): config[v] = config.pop(k) # migrate pymatgen objects for k, emmet_cls in { "phonon_dos": PhononDOS, "phonon_bandstructure": PhononBS, }.items(): if (old_obj := config.get(k)) and not isinstance(old_obj, emmet_cls): try: new_obj = emmet_cls.from_pmg(old_obj) # type: ignore[attr-defined] except Exception: # purposely no except here - want this to hard fail if we # can't parse the input type new_obj = emmet_cls(**old_obj) config[k] = new_obj if (ph_bs := config.get("phonon_bandstructure")) and not config.get( "structure" ): config["structure"] = ph_bs.structure # This block is for the migration from atomate2 --> emmet-core schemas if config.get("structure"): if isinstance(config["structure"], dict): config["structure"] = Structure.from_dict(config["structure"]) old_formula_units = config.pop("formula_units", 1) if toten := config.pop("total_dft_energy", None): config["total_dft_energy"] = ( toten * old_formula_units / config["structure"].num_sites ) config["formula_units"] = get_num_formula_units( config["structure"].composition ) if (fc := config.get("force_constants")) and isinstance(fc, dict): config["force_constants"] = fc["force_constants"] if comp_sett := config.get("phonopy_settings"): config["post_process_settings"] = comp_sett calc_meta_migrate = { "uuids": "_uuid", "jobdirs": "_job_dir", } calc_meta_remap = {"uuids": "identifier", "jobdirs": "dir_name"} if any(config.get(k) for k in calc_meta_migrate): calc_meta: dict[str, dict[str, str]] = {} for field_name, splitter in calc_meta_migrate.items(): if vals := config.get(field_name): if not isinstance(vals, dict): vals = vals.model_dump() for k, v in vals.items(): job_name = k.split(splitter)[0] if isinstance(v, list): for idx, sub_v in enumerate(v): if (sub_name := f"{job_name}-{idx}") not in calc_meta: calc_meta[sub_name] = {} calc_meta[sub_name][calc_meta_remap[field_name]] = sub_v else: if job_name not in calc_meta: calc_meta[job_name] = {} calc_meta[job_name][calc_meta_remap[field_name]] = v config["calc_meta"] = [ CalcMeta( name=job_name, **meta, ) for job_name, meta in calc_meta.items() ] return cls.from_structure( meta_structure=config.pop("meta_structure", config.get("structure")), **config, ) @classmethod def from_phonopy_pheasy_files( cls, structure_file: FSPathType, phonon_bandstructure_file: FSPathType | None = None, phonon_dos_file: FSPathType | None = None, projected_dos_file: FSPathType | None = None, force_constants_file: FSPathType | None = None, born_file: FSPathType | None = None, epsilon_static_file: FSPathType | None = None, epsilon_static_and_born_file: FSPathType | None = None, phonopy_output_file: FSPathType | None = None, **kwargs, ) -> Self: """ Create a PhononBSDOSTask from a list of explicit Phonopy/Pheasy file paths. """ try: import phonopy except ImportError: phonopy = None # type: ignore[assignment] cls_config: dict[str, Any] = { "structure": Structure.from_file(structure_file), } if "poscar" in str(structure_file).lower(): cls_config["code"] = "vasp" if phonon_bandstructure_file: cls_config["phonon_bandstructure"] = PhononBS.from_phonopy( phonon_bandstructure_file, structure=cls_config["structure"], ) if phonon_dos_file: cls_config["phonon_dos"] = PhononDOS.from_phonopy( phonon_dos_file, projected_dos_file=projected_dos_file, structure=cls_config["structure"], ) if force_constants_file: # read FORCE_CONSTANTS manually force_constant_matrix: np.ndarray idxs: tuple[int | None, int | None] = ( None, None, ) irow = 0 with zopen(force_constants_file, "rt") as f: for idx, line in enumerate(f.read().splitlines()): vals = line.strip().split() if idx == 0: force_constant_matrix = np.zeros( (int(vals[0]), int(vals[1]), 3, 3) ) elif len(vals) == 2: # idxs written like this for mypy idxs = ( int(vals[0]) - 1, int(vals[1]) - 1, ) irow = 0 elif len(vals) == 3: force_constant_matrix[idxs[0], idxs[1], irow] = [ float(v) for v in vals ] irow += 1 cls_config["force_constants"] = force_constant_matrix.tolist() if phonopy_output_file: with zopen(phonopy_output_file, "rt") as f: phonopy_output = yaml.safe_load(f.read()) for k in ("primitive_matrix", "supercell_matrix"): cls_config[k] = phonopy_output.get(k) if all( fs is not None and ".npz" in Path(fs).name for fs in (born_file, epsilon_static_file) ): cls_config["born"] = np.load(born_file) # type: ignore[arg-type] cls_config["epsilon_static"] = np.load(epsilon_static_file) # type: ignore[arg-type] elif ( phonopy_output_file and epsilon_static_and_born_file and "BORN" in Path(epsilon_static_and_born_file).name ): if phonopy is None: raise ImportError("You must `pip install phonopy` to parse BORN.") phonopy_calc = phonopy.load(phonopy_output_file) with zopen(epsilon_static_and_born_file, "rt") as f: born_data = phonopy.file_IO.parse_BORN_from_strings( f.read(), phonopy_calc.unitcell ) cls_config.update( { "epsilon_static": born_data["dielectric"], "born": born_data["born"], } ) return cls.from_structure(cls_config["structure"], **cls_config, **kwargs) @classmethod def from_phonopy_pheasy_directory( cls, phonon_dir: Path | str, **kwargs, ) -> Self: """Create a PhononBSDOSDoc from a Phonopy/Pheasy directory. Parameters ----------- phonon_dir : str or Path **kwargs to pass to `PhononBSDOSDoc.from_phonopy_pheasy_files`. The user can override the default file paths for select files using this. """ phonon_path = Path(phonon_dir).resolve() file_paths = {} for k, file_name in DEFAULT_PHONON_FILES.items(): file_path = phonon_path / kwargs.pop(f"{k}_file", file_name) if (file_path := Path(zpath(file_path))).exists(): file_paths[f"{k}_file"] = file_path return cls.from_phonopy_pheasy_files(**file_paths, **kwargs) @cached_property def has_imaginary_modes(self) -> bool | None: tol: float = 1e-5 if self.phonon_bandstructure: return self.phonon_bandstructure.to_pmg.has_imaginary_freq(tol=tol) elif self.phonon_dos: return bool( np.any( np.array(self.phonon_dos.densities)[ np.array(self.phonon_dos.frequencies) < tol ] > tol ) ) return None def _get_thermo_from_dos( self, quantity: Literal["entropy", "cv", "internal_energy", "helmholtz_free_energy"], temperature: float, normalization: Literal["atoms", "formula_units"] | None = "formula_units", ) -> float | None: """Get a thermodynamic property from the phonon DOS. quantity : "entropy", "cv", "internal_energy", or "helmholtz_free_energy" If "entropy" or "cv", the units before normalization are J/(K * mol). If "internal_energy" or "helmholtz_free_energy", the units before normalization are J/mol. temperature : float normalization : "atoms", "formula_units", or None Whether to normalize by the number of atoms in the cell ("atoms"), the number of formula units ("formula_units", default), or not (None). """ if not self.phonon_dos: return None if normalization and not self.structure: raise ValueError( "Cannot normalize thermodynamic quantities without a structure." ) norm_fac = 1.0 if normalization == "atoms": norm_fac = 1.0 / self.structure.num_sites # type: ignore[union-attr] elif normalization == "formula_units": norm_fac = 1.0 / self.formula_units # type: ignore[operator] elif normalization: raise ValueError(f"Unknown {normalization=} convention.") return ( getattr(self.phonon_dos.to_pmg, quantity)( temp=temperature, ) * norm_fac ) def entropy( self, temperature: float, normalization: Literal["atoms", "formula_units"] | None = "formula_units", ) -> float | None: """Compute the entropy in J/(K * mol * formula units). temperature : float normalization : "atoms", "formula_units", or None Whether to normalize by the number of atoms in the cell ("atoms"), the number of formula units ("formula_units"), or not (None). """ return self._get_thermo_from_dos( "entropy", temperature, normalization=normalization ) def heat_capacity( self, temperature: float, normalization: Literal["atoms", "formula_units"] | None = "formula_units", ) -> float | None: """Compute the heat capacity in J/(K * mol * formula units). temperature : float normalization : "atoms", "formula_units", or None Whether to normalize by the number of atoms in the cell ("atoms"), the number of formula units ("formula_units"), or not (None). """ return self._get_thermo_from_dos("cv", temperature, normalization=normalization) def internal_energy( self, temperature: float, normalization: Literal["atoms", "formula_units"] | None = "formula_units", ) -> float | None: """Compute the internal energy in J/(mol * formula units). temperature : float normalization : "atoms", "formula_units", or None Whether to normalize by the number of atoms in the cell ("atoms"), the number of formula units ("formula_units"), or not (None). """ return self._get_thermo_from_dos( "internal_energy", temperature, normalization=normalization ) def free_energy( self, temperature: float, normalization: Literal["atoms", "formula_units"] | None = "formula_units", ) -> float | None: """Compute the Helmholtz free energy in J/(mol * formula units). temperature : float normalization : "atoms", "formula_units", or None Whether to normalize by the number of atoms in the cell ("atoms"), the number of formula units ("formula_units"), or not (None). """ return self._get_thermo_from_dos( "helmholtz_free_energy", temperature, normalization=normalization ) def compute_thermo_quantities( self, temperatures: Sequence[float], normalization: Literal["atoms", "formula_units"] | None = "formula_units", ) -> dict[str, Sequence[float | None]]: """Compute all thermodynamic quantities as a convenience method.""" quantities = { "entropy": "entropy", "heat_capacity": "cv", "internal_energy": "internal_energy", "free_energy": "helmholtz_free_energy", } thermo_props: dict[str, Sequence[float | None]] = { k: [ self._get_thermo_from_dos(v, temp, normalization=normalization) # type: ignore[arg-type] for temp in temperatures ] for k, v in quantities.items() } thermo_props["temperature"] = temperatures return thermo_props @cached_property def acoustic_sum_rule(self) -> np.ndarray | None: r"""Determine violations of the acoustic sum rule. The acoustic sum rule for the interatomic force constant (IFC) tensor is (Eq. 44 of https://doi.org/10.1103/PhysRevB.55.10355): $$ \sum_{j=1}^N C_{ij\alpha\beta} = 0. $$ The IFCs are an N x N x 3 x 3 tensor, where N = number of atoms in the structure (`structure.num_sites`). $i, j = 1,2,3,...,N$, and $\alpha,\beta = x,y,z$ are the Cartesian directions. The maximum deviation from the sum rule is checked by `check_sum_rule_deviations`. For an ASR-corrected force-constant set, this is numerically zero. """ if self.force_constants: return np.asarray(self.force_constants).sum(axis=1) return None class PhononWarnings(DocEnum): ASR = "ASR break", "acoustic sum rule max breaking is larger than 30 cm^-1." CNSR = "CNSR break", "charge neutrality sum rule max breaking is larger than 0.2." NEG_FREQ = ( "has negative frequencies", "phonon band structure has negative " "frequencies anywhere in the Brillouin zone.", ) SMALL_Q_NEG_FREQ = ( "has small q negative frequencies", "the phonon band structure has negative frequencies," " but these are small and very close to the Gamma point " "(usually related to numerical errors).", ) vec2D = list[float, float] # type: ignore[type-arg] vec3D = list[float, float, float] # type: ignore[type-arg] class PhononWebsiteStruct(TypedDict): atom_numbers: list[int] atom_pos_car: list[vec3D] atom_pos_red: list[vec3D] atom_types: list[str] distances: list[float] eigenvalues: list[list[float]] formula: str # fails arrow conversion # highsym_qpts: list[list[int, str]] lattice: list[vec3D] line_breaks: list[tuple[int, int]] name: str natoms: int qpoints: list[vec3D] repetitions: list[int, int, int] # type: ignore[type-arg] vectors: list[list[list[list[vec2D, vec2D, vec2D]]]] # type: ignore[type-arg] class PhononWebsiteBS(BaseModel): """ Document with a serialized version of the phonon band structure suitable for the phononwebsite (http://henriquemiranda.github.io/phononwebsite/). """ material_id: str = Field( ..., description="The ID of this material, used as a universal reference across property documents." "This comes in the form: mp-******", ) doc_type: Literal["phononwebsite"] = Field( "phononwebsite", description="The type of the document: a phonon band structure for the phononwebsite.", ) phononwebsite: PhononWebsiteStruct | None = Field( None, description="Phononwebsite dictionary to plot the animated " "phonon modes.", ) last_updated: DateTimeType = Field( description="Timestamp for the most recent calculation update for this property", ) created_at: DateTimeType = Field( description="Timestamp for when this material document was first created", ) class Ddb(BaseModel): """ Document with a the string version of the DDB file produced by abinit. """ material_id: str = Field( ..., description="The ID of this material, used as a universal reference across property documents." "This comes in the form: mp-******", ) doc_type: Literal["ddb"] = Field( "ddb", description="The type of the document: a DDB file." ) ddb: str | None = Field(None, description="The string of the DDB file.") last_updated: DateTimeType = Field( description="Timestamp for the most recent calculation update for this property", ) created_at: DateTimeType = Field( description="Timestamp for when this material document was first created", ) class ThermodynamicProperties(BaseModel): """ Definition of the thermodynamic properties extracted from the phonon frequencies. """ temperatures: list[float] = Field( ..., description="The list of temperatures at which the thermodynamic properties " "are calculated", ) cv: list[float] = Field( ..., description="The values of the constant-volume specific heat.", alias="heat_capacity", ) entropy: list[float] = Field( ..., description="The values of the vibrational entropy." ) class VibrationalEnergy(BaseModel): """ Definition of the vibrational contribution to the energy as function of the temperature. """ temperatures: list[float] = Field( ..., description="The list of temperatures at which the thermodynamic properties " "are calculated", ) internal_energy: list[float] = Field( ..., description="The values of the phonon contribution to the internal energy." ) helmholtz_free_energy: list[float] = Field( ..., description="The values of the Helmholtz free energy." ) zero_point_energy: float = Field( ..., description="The value of the zero point energy." ) class Phonon(StructureMetadata): """ Definition for a document with data produced by a phonon calculation. """ material_id: str = Field( ..., description="The ID of this material, used as a universal reference across property documents." "This comes in the form: mp-******", ) structure: StructureType = Field( ..., description="The relaxed structure for the phonon calculation." ) asr_break: float | None = Field( None, description="The maximum breaking of the acoustic sum rule (ASR)." ) warnings: list[PhononWarnings] | None = Field( None, description="List of warnings associated to the phonon calculation." ) dielectric: DielectricDoc | None = Field( None, description="Dielectric properties obtained during a phonon calculations." ) becs: BornEffectiveCharges | None = Field( None, description="Born effective charges obtained for a phonon calculation." ) ir_spectra: IRDielectric | None = Field(None, description="The IRDielectricTensor.") thermodynamic: ThermodynamicProperties | None = Field( None, description="The thermodynamic properties extracted from the phonon " "frequencies.", ) vibrational_energy: VibrationalEnergy | None = Field( None, description="The vibrational contributions to the total energy." ) last_updated: DateTimeType = Field( description="Timestamp for when this document was last updated", ) created_at: DateTimeType = Field( description="Timestamp for when this material document was first created", ) class TypedBaseInputDict(TypedDict): charge: float chksymbreak: int ecut: float fband: float kptopt: int nband: int nbdbuf: int ngkpt: list[int] nshiftk: int nspden: int nspinor: int nsppol: int nstep: int pawecutdg: float shiftk: list[list[float]] tolvrs: float class TypedDdBaseDict(TypedBaseInputDict): chkprim: int nqpt: int qpt: list[int] rfdir: list[int] rfelfd: int class TypedDdeInputDict(TypedDdBaseDict): prtwf: int class TypedDdkInputDict(TypedDdBaseDict): iscf: float class TypedPhononInputDict(TypedDdeInputDict): rfatpol: list[int] class TypedPseudopotentialsDict(TypedDict): md5: list[str] name: list[str] class TypedAbinitInputVars(TypedDict): dde_input: TypedDdeInputDict ddk_input: TypedDdkInputDict ecut: float gs_input: TypedBaseInputDict ngkpt: list[int] ngqpt: list[int] occopt: int phonon_input: TypedPhononInputDict pseudopotentials: TypedPseudopotentialsDict shiftk: list[list[float]] tsmear: int wfq_input: TypedBaseInputDict # have to guess here without an example entry class AbinitPhonon(Phonon): """ Definition for a document with data produced from a phonon calculation with Abinit. """ abinit_input_vars: TypedAbinitInputVars | None = Field( None, description="Dict representation of the inputs used to obtain the phonon" "properties and the main general options (e.g. number of " "k-points, number of q-points).", ) class SoundVelocity(BaseModel): """ Definition for a document with the sound velocities of the acoustic modes close to Gamma, as obtained from a phonon calculation. """ material_id: str = Field( ..., description="The ID of this material, in the form: mp-******", ) structure: StructureType = Field( ..., description="The relaxed structure for the phonon calculation." ) directions: list[Vector3D] = Field( ..., description="Q-points identifying the directions for the calculation" "of the speed of sound. In fractional coordinates.", ) labels: list[str | None] = Field(..., description="labels of the directions.") sound_velocities: list[Vector3D] = Field( ..., description="Values of the sound velocities in SI units.", ) mode_types: list[tuple[str | None, str | None, str | None]] = Field( ..., description="The types of the modes ('transversal', 'longitudinal'). " "None if not correctly identified.", ) last_updated: DateTimeType = Field( description="Timestamp for when this document was last updated", ) created_at: DateTimeType = Field( description="Timestamp for when this material document was first created", ) class ThermalDisplacement(BaseModel): """ Definition of a Document for the generalized density of states and mean square displacements related to phonon oscillations. """ material_id: str = Field( ..., description="The ID of this material, used as a universal reference across property documents." "This comes in the form: mp-******", ) last_updated: DateTimeType = Field( description="Timestamp for the most recent calculation update for this property", ) created_at: DateTimeType = Field( description="Timestamp for when this material document was first created", ) nsites: int = Field( ..., description="The number of sites in the structure.", ) nomega: int = Field( ..., description="The number of frequencies.", ) ntemp: int = Field( ..., description="The number of temperatures for which the displacements are calculated", ) temperatures: list[float] = Field( ..., description="The list of temperatures at which the thermodynamic properties " "are calculated", ) frequencies: list[float] = Field( ..., description="The list of frequencies for the generalized DOS" ) gdos_aijw: Tensor4R = Field( ..., description=" Generalized DOS in Cartesian coords, with shape (nsites, 3, 3, nomega)", ) amu: dict[str, float] = Field( ..., description="Dictionary of the atomic masses in atomic units." ) structure: StructureType = Field( ..., description="The relaxed structure for the phonon calculation." ) ucif_t: Tensor4R = Field( ..., description="Mean squared displacement U tensors as a function of T for T in tmesh in CIF format." "With shape (natom, 3, 3, ntemp) ", ) ucif_string_t300k: str = Field( ..., description="Mean squared displacement U tensors at T=300K in CIF string format.", ) ././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1787011023.824219 emmet_core-0.87.2/emmet/core/polar.py0000644000175100017510000001314015240717720017127 0ustar00runnerrunner"""Core definition for Polar property Document""" from __future__ import annotations from typing import TYPE_CHECKING import numpy as np from pydantic import BaseModel, Field from emmet.core.io.pymatgen import PiezoTensor as BasePiezoTensor, Tensor from emmet.core.material_property import PropertyDoc from emmet.core.math import Matrix3D from emmet.core.settings import EmmetSettings from emmet.core.types.pymatgen_types.ir_dielectric_tensor_adapter import ( IRDielectricTensorType, ) if TYPE_CHECKING: from emmet.core.io.pymatgen import Structure from emmet.core.types.typing import IdentifierType SETTINGS = EmmetSettings() Vector = list[float] PiezoTensor = list[Vector] class DielectricDoc(PropertyDoc): """ A dielectric property block """ property_name: str = "dielectric" total: Matrix3D = Field(description="Total dielectric tensor.") ionic: Matrix3D = Field(description="Ionic contribution to dielectric tensor.") electronic: Matrix3D = Field( description="Electronic contribution to dielectric tensor." ) e_total: float = Field(description="Total electric permittivity.") e_ionic: float = Field( description="Electric permittivity from atomic rearrangement." ) e_electronic: float = Field( description="Electric permittivity due to electrons rearrangement." ) n: float = Field(description="Refractive index.") @classmethod def from_ionic_and_electronic( cls, epsilon_ionic: Matrix3D, epsilon_static: Matrix3D, structure: Structure, material_id: IdentifierType | None = None, **kwargs, ): ionic_tensor = Tensor(epsilon_ionic).convert_to_ieee(structure) electronic_tensor = Tensor(epsilon_static).convert_to_ieee(structure) total = ionic_tensor + electronic_tensor return super().from_structure( meta_structure=structure, material_id=material_id, **{ "total": total.tolist(), "ionic": ionic_tensor.tolist(), "electronic": electronic_tensor.tolist(), "e_total": np.average(np.diagonal(total)), "e_ionic": np.average(np.diagonal(ionic_tensor)), "e_electronic": np.average(np.diagonal(electronic_tensor)), "n": np.sqrt(np.average(np.diagonal(electronic_tensor))), }, **kwargs, ) class PiezoelectricDoc(PropertyDoc): """ A dielectric package block """ property_name: str = "piezoelectric" total: PiezoTensor = Field(description="Total piezoelectric tensor in C/m²") ionic: PiezoTensor = Field( description="Ionic contribution to piezoelectric tensor in C/m²" ) electronic: PiezoTensor = Field( description="Electronic contribution to piezoelectric tensor in C/m²" ) e_ij_max: float = Field(description="Piezoelectric modulus") max_direction: list[int] = Field( description="Miller direction for maximum piezo response" ) strain_for_max: list[float] = Field( description="Normalized strain direction for maximum piezo repsonse" ) @classmethod def from_ionic_and_electronic( cls, piezo_ionic: PiezoTensor, piezo_static: PiezoTensor, structure: Structure, material_id: IdentifierType | None = None, **kwargs, ): ionic_tensor = BasePiezoTensor.from_vasp_voigt(piezo_ionic) electronic_tensor = BasePiezoTensor.from_vasp_voigt(piezo_static) total: BasePiezoTensor = ionic_tensor + electronic_tensor # type: ignore[assignment] # Symmeterize Convert to IEEE orientation total = total.convert_to_ieee(structure) ionic_tensor = ionic_tensor.convert_to_ieee(structure) electronic_tensor = electronic_tensor.convert_to_ieee(structure) directions, charges, strains = np.linalg.svd(total.voigt, full_matrices=False) max_index = np.argmax(np.abs(charges)) max_direction = directions[max_index] # Allow a max miller index of 10 min_val = np.abs(max_direction) min_val = min_val[min_val > (np.max(min_val) / SETTINGS.MAX_PIEZO_MILLER)] min_val = np.min(min_val) return super().from_structure( meta_structure=structure, material_id=material_id, **{ "total": total.zeroed().voigt.tolist(), "ionic": ionic_tensor.zeroed().voigt.tolist(), "electronic": electronic_tensor.zeroed().voigt.tolist(), "e_ij_max": charges[max_index], "max_direction": tuple(np.round(max_direction / min_val)), "strain_for_max": tuple(strains[max_index]), }, **kwargs, ) class BornEffectiveCharges(BaseModel): """ A block for the Born effective charges """ value: list[Matrix3D] | None = Field( None, description="Value of the Born effective charges." ) symmetrized_value: list[Matrix3D] | None = Field( None, description="Value of the Born effective charges after symmetrization to obey the" "charge neutrality sum rule.", ) cnsr_break: float | None = Field( None, description="The maximum breaking of the charge neutrality sum " "rule (CNSR) in the Born effective charges.", ) class IRDielectric(BaseModel): """ A block for the pymatgen IRDielectricTensor object """ ir_dielectric_tensor: IRDielectricTensorType | None = Field( None, description="Serialized version of a pymatgen IRDielectricTensor object." ) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8242714 emmet_core-0.87.2/emmet/core/provenance.py0000644000175100017510000004015515240717720020160 0ustar00runnerrunner"""Core definition of a Provenance Document""" from __future__ import annotations import json import warnings from typing import TYPE_CHECKING, Annotated, Any, Literal from pybtex.database import BibliographyData, parse_string from pybtex.errors import set_strict_mode from pydantic import BaseModel, BeforeValidator, Field from emmet.core.io.pymatgen import Lattice, PeriodicSite, Structure from emmet.core.material_property import PropertyDoc from emmet.core.math import Matrix3D from emmet.core.structure import StructureMetadata from emmet.core.symmetry import SymmetryData from emmet.core.types.enums import ValueEnum from emmet.core.types.pymatgen_types.lattice_adapter import LatticeType from emmet.core.types.pymatgen_types.structure_adapter import StructureType from emmet.core.types.typing import DateTimeType, IdentifierType if TYPE_CHECKING: from typing_extensions import Self class Database(ValueEnum): """ Database identifiers for provenance IDs """ ICSD = "icsd" Pauling_Files = "pauling" COD = "cod" class Author(BaseModel): """ Author information """ name: str | None = Field(None) email: str | None = Field(None) def _remove_dupe_authors(authors: list[dict[str, Any] | Author]): """Remove duplicate authors from a list of Author objects or their dict rep.""" _authors = [a.model_dump() if hasattr(a, "model_dump") else a for a in authors] authors_dict = {a["name"].lower(): a for a in _authors} return list(authors_dict.values()) def _migrate_legacy_history_data( config: list[dict[str, Any]] | list[History] | None, ) -> list[History]: """Migrate legacy provenance and SNL `history` data as a classmethod. Parameters ----------- config : list of history data as a dict or History object Returns ----------- list of History objects. """ if not config: return [] top_level_history = [ h.model_dump() if isinstance(h, History) else h for h in config ] history: list[History] = [] for inp_hist in top_level_history: descs: list = [] _flatten_nested_descriptions(descs, inp_hist.get("description"), 0) history.extend( [ History( **{k: inp_hist.get(k) for k in ("name", "url")}, # type: ignore[arg-type] description=desc, ) for desc in descs ] ) return history def _flatten_nested_descriptions( descs: list[ProvenanceDescription | None], entry: dict[str, Any] | str | None, depth: int, remark: str | None = None, ) -> None: """Flatten legacy provenance description data. Parameters ----------- descs : list[ProvenanceDescription | None] Running list of provenance description data. entry : dict[str,Any] or None The current description depth : int Used to indicate how far recursively into the history this field is nested. remark : str or None (default) Optional remark to supersede that in entry. Returns ----------- None : all data goes in to `descs` """ if not entry: descs.append(None) return # Handle plain string descriptions (e.g. from user-submitted SNLs) if isinstance(entry, str): descs.append(ProvenanceDescription(string=entry, remark=remark)) return elif nested_hist := entry.pop("history", []): for sub_entry in nested_hist: _flatten_nested_descriptions( descs, sub_entry, depth + 1, remark=f"Nested history depth {depth}" ) elif init_args := entry.get("init_args"): _flatten_nested_descriptions( descs, init_args, depth + 1, remark=f"init_args-depth-{depth}" ) # always append current entry even if there were nested fields orig_remark = entry.pop("remark", None) if not remark: remark = orig_remark if entry.get("lattice"): entry["lattice"] = Lattice.from_dict(entry["lattice"]) for k in ("sites", "input_structure"): if c := entry.get(k): try: if all(c.get(x) for x in ("sites", "lattice")): entry[k] = Structure.from_sites( [ PeriodicSite.from_dict( site, lattice=Lattice.from_dict(c["lattice"]) ) for site in c["sites"] ] ) else: entry[k] = Structure.from_sites( [ PeriodicSite.from_dict(site, lattice=entry.get("lattice")) for site in c ] ) except Exception: entry[k] = None if isinstance(entry.get("species_map"), list): # Some of these appear to be .items() entry["species_map"] = {v[0]: v[1] for v in entry["species_map"]} if isinstance(cif_data := entry.get("cif_data"), dict): # CIF fields from pymatgen are too heterogeneous to schematize. entry["cif_data"] = json.dumps(cif_data) descs.append(ProvenanceDescription(**entry, remark=remark)) class TransformationMeta(BaseModel): """Schema for transformation fields in history.description.""" transformation_name: str | None = None formula: str | None = None structureid: int | None = None class ProvenanceDescription(BaseModel): """Schema for heterogeneous provenance description data.""" monty_class: str | None = Field(None, validation_alias="@class") monty_module: str | None = Field(None, validation_alias="@module") IZA_code: str | None = None algo: int | None = None cif_data: str | None = None crystal_id: int | None = None datetime: str | None = None description: str | None = None disordered_crystal_id: int | None = None experimental: bool = False fraction_to_remove: float | None = None fw_id: int | None = None icsd_id: int | None = None id: str | None = None input_structure: StructureType | None = None lattice: LatticeType | None = None materialsid: IdentifierType | None = None name: str | None = None original_file: str | None = None oxidation_states: dict[str, int] | None = None query: str | None = None remark: str | None = None scaling_matrix: Matrix3D | None = None sites: StructureType | None = None source: str | None = None spacegroup: SymmetryData | None = None specie_to_remove: str | None = None species_map: dict[str, str] | None = None species_to_remove: list[str] | None = None string: str | None = None structureid: int | None = None task_id: str | None = None task_type: str | None = None transformation_name: str | None = None transformations: list[TransformationMeta] | None = None version: str | None = None @classmethod def parse_str_or_dict(cls, x: str | dict[str, Any] | Self) -> Self: """Method used in validating string or dict-like input. Parameters ----------- x : a string, dict, or ProvenanceDescription Returns ----------- ProvenanceDescription """ if isinstance(x, str): return cls(string=x) # type: ignore[arg-type,call-arg] elif isinstance(x, dict): return cls(**x) return x class History(BaseModel): """ History of the material provenance """ name: str url: str description: Annotated[ ProvenanceDescription | None, BeforeValidator(ProvenanceDescription.parse_str_or_dict), ] = Field(None, description="Dictionary of extra data for this history node.") HistoryType = Annotated[ list[History | None], BeforeValidator(_migrate_legacy_history_data), ] class SNLAbout(BaseModel): """A data dictionary defining extra fields in a SNL""" references: str = Field( "", description="Bibtex reference strings for this material." ) authors: list[Author] | None = Field( None, description="list of authors for this material." ) remarks: list[str] | None = Field( None, description="list of remarks for the provenance of this material." ) tags: list[str] | None = Field(None) database_IDs: dict[Database, list[str]] | None = Field( None, description="Database IDs corresponding to this material." ) history: HistoryType = Field( [], description="list of history nodes specifying the transformations or orignation" " of this material for the entry closest matching the material input.", ) created_at: DateTimeType = Field(description="The creation date for this SNL.") @classmethod def migrate_legacy_data(cls, config: dict[str, Any]) -> Self: """Migrate legacy SNL data with free-form JSON values to schematized.""" config["history"] = _migrate_legacy_history_data(config.get("history", [])) if projs := config.pop("projects", None): if "tags" not in config: config["tags"] = [] config["tags"].extend(projs) return cls(**config) class SNLDict(BaseModel): """Pydantic validated dictionary for SNL""" about: SNLAbout snl_id: str = Field(..., description="The SNL ID for this entry") class ProvenanceDoc(PropertyDoc): """ A provenance property block """ property_name: str = "provenance" created_at: DateTimeType = Field( description="creation date for the first structure corresponding to this material", ) references: list[str] = Field( default_factory=list, description="Bibtex reference strings for this material" ) authors: Annotated[ list[Author], BeforeValidator(_remove_dupe_authors), ] = Field(default_factory=list, description="list of authors for this material") remarks: list[str] | None = Field( None, description="list of remarks for the provenance of this material" ) tags: list[str] | None = Field(None) theoretical: bool = Field( True, description="If this material has any experimental provenance or not" ) database_IDs: dict[Database, list[str]] | None = Field( None, description="Database IDs corresponding to this material" ) history: HistoryType = Field( default_factory=list, description="list of history nodes specifying the transformations or orignation" " of this material for the entry closest matching the material input", ) @classmethod def from_SNLs( cls, structure: Structure, snls: list[SNLDict], material_id: IdentifierType | None = None, **kwargs, ) -> "ProvenanceDoc": """ Converts legacy Pymatgen SNLs into a single provenance document """ assert ( len(snls) > 0 ), "Error must provide a non-zero list of SNLs to convert from SNLs" # Choose earliest created_at created_at = min([snl.about.created_at for snl in snls]) # last_updated = max([snl.about.created_at for snl in snls]) # Choose earliest history history = sorted(snls, key=lambda snl: snl.about.created_at)[0].about.history # Aggregate all references into one dict to remove duplicates refs = {} for snl in snls: try: set_strict_mode(False) entries = parse_string(snl.about.references, bib_format="bibtex") refs.update(entries.entries) except Exception as e: warnings.warn( f"Failed parsing bibtex: {snl.about.references} due to {e}" ) bib_data = BibliographyData(entries=refs) references = [ref.to_string("bibtex") for ref in bib_data.entries.values()] # TODO: Maybe we should combine this robocrystallographer? # TODO: Refine these tags / remarks remarks = list(set([remark for snl in snls for remark in snl.about.remarks])) # type: ignore[union-attr] tags = [r for r in remarks if len(r) < 140] authors = [entry for snl in snls for entry in snl.about.authors] # type: ignore[union-attr] # Check if this entry is experimental exp_vals = [] for snl in snls: for entry in snl.about.history: # type: ignore[union-attr] if entry is not None and entry.description is not None: exp_vals.append(entry.description.experimental) experimental = any(exp_vals) # Aggregate all the database IDs snl_ids = {snl.snl_id for snl in snls} db_ids = { Database(db_id): [snl_id for snl_id in snl_ids if db_id in snl_id] for db_id in map(str, Database) # type: ignore } # remove Nones and empty lists db_ids = {k: list(filter(None, v)) for k, v in db_ids.items()} db_ids = {k: v for k, v in db_ids.items() if len(v) > 0} fields = { "created_at": created_at, "references": references, "authors": authors, "remarks": remarks, "tags": tags, "database_IDs": db_ids, "theoretical": not experimental, "history": history, } return super().from_structure( material_id=material_id, meta_structure=structure, **fields, **kwargs ) @classmethod def migrate_legacy_data(cls, config: dict[str, Any]) -> Self: """Migrate legacy provenance data with free-form JSON values to schematized.""" config["history"] = _migrate_legacy_history_data(config.get("history", [])) return cls(**config) class DatabaseSNL(StructureMetadata): """Define schemas for database entries. This particular SNL schema is used for experimental databases like ICSD and Pauling File. """ snl_id: str | None = Field(None, description="The SNL ID for this entry") structure: StructureType | None = Field( None, description="The structure for this entry" ) about: SNLAbout = Field( default_factory=SNLAbout, # type: ignore[arg-type] description="Extended metadata for this entry.", ) theoretical: bool = Field( True, description="Whether this entry is a theoretical database entry." ) is_ordered: bool | None = Field( None, description="Whether this represents a (configurationally) ordered structure.", ) last_updated: DateTimeType = Field( description="The last time this entry was updated." ) tags: list[str] | None = Field( None, description="List of high-level metadata for this entry." ) source: Literal["icsd", "pauling", "mp-complete", "user"] | None = Field( None, description="The source of this SNL." ) submission_id: int | None = Field( None, description="If applicable, the identifier of the submitted structure." ) submitter_email: str | None = Field( None, description="If applicable, the email of the user who submitted the structure.", ) @classmethod def migrate_legacy_config(cls, config: dict) -> Self: """Migrate legacy, JSONL-format SNLs to the current schema. Legacy database SNLs appear to extend the properties of the pymatgen Structure object. """ if all( config.get(k) for k in ( "sites", "lattice", ) ): config["structure"] = Structure.from_dict( { k: config.pop(k, None) for k in ( "sites", "lattice", ) } ) if "structure" in config and "is_ordered" not in config: config["is_ordered"] = config["structure"].is_ordered if (expt := config.pop("experimental", None)) is not None: config["theoretical"] = not expt if "structure" in config: return cls.from_structure( meta_structure=config["structure"], **config, ) return cls(**config) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8242714 emmet_core-0.87.2/emmet/core/py.typed0000644000175100017510000000000015240717720017126 0ustar00runnerrunner././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8242714 emmet_core-0.87.2/emmet/core/qc_tasks.py0000644000175100017510000005247115240717720017634 0ustar00runnerrunner"""Core definition of a Q-Chem Task Document""" # mypy: ignore-errors from __future__ import annotations import logging import re from collections import OrderedDict from pathlib import Path from typing import Any, TYPE_CHECKING from custodian.qchem.jobs import QCJob from monty.serialization import loadfn from pydantic import BaseModel, Field from emmet.core.io.pymatgen import Molecule, QCInput from emmet.core.qchem.calc_types import CalcType, LevelOfTheory, TaskType from emmet.core.qchem.calculation import Calculation, CalculationInput from emmet.core.qchem.task import QChemStatus from emmet.core.structure import MoleculeMetadata from emmet.core.utils import arrow_incompatible if TYPE_CHECKING: from typing_extensions import Self __author__ = ( "Evan Spotte-Smith , Rishabh D. Guha " ) logger = logging.getLogger(__name__) # _DERIVATIVE_FILES = ("GRAD", "HESS") @arrow_incompatible class OutputDoc(BaseModel): initial_molecule: Molecule = Field(None, description="Input Molecule object") optimized_molecule: Molecule | None = Field( None, description="Optimized Molecule object" ) # TODO: Discuss with Evan if these go here # species_hash: str = Field( # None, # description="Weisfeiler Lehman (WL) graph hash using the atom species as the graph node attribute.", # ) # coord_hash: str = Field( # None, # description="Weisfeiler Lehman (WL) graph hash using the atom coordinates as the graph node attribute.", # ) # last_updated: datetime = Field( # None, # description = "Timestamp for the most recent calculation for this QChem task document", # ) final_energy: float = Field( description="Final electronic energy for the calculation (units: Hartree)" ) enthalpy: float | None = Field( None, description="Total enthalpy of the molecule (units: kcal/mol)" ) entropy: float | None = Field( None, description="Total entropy of the molecule (units: cal/mol-K" ) dipoles: dict[str, Any] | None = Field( None, description="Dipolar information from the output" ) mulliken: list[Any] | None = Field( None, description="Mulliken atomic partial charges and partial spins" ) resp: list[float | list[Any]] | None = Field( None, description="Restrained Electrostatic Potential (RESP) atomic partial charges", ) nbo: dict[str, Any] | None = Field( None, description="Natural Bonding Orbital (NBO) output" ) frequencies: dict[str, Any] | list | None = Field( None, description="The list of calculated frequencies if job type is freq (units: cm^-1)", ) frequency_modes: list | str | None = Field( None, description="The list of calculated frequency mode vectors if job type is freq", ) @classmethod def from_qchem_calc_doc(cls, calc_doc: Calculation) -> "OutputDoc": """ Create a summary of QChem calculation outputs from a QChem calculation document. Parameters ---------- calc_doc A QChem calculation document. kwargs Any other additional keyword arguments Returns -------- OutputDoc The calculation output summary """ return cls( initial_molecule=calc_doc.input.initial_molecule, optimized_molecule=calc_doc.output.optimized_molecule, # species_hash = self.species_hash, #The three entries post this needs to be checked again # coord_hash = self.coord_hash, # last_updated = self.last_updated, final_energy=calc_doc.output.final_energy, dipoles=calc_doc.output.dipoles, enthalpy=calc_doc.output.enthalpy, entropy=calc_doc.output.entropy, mulliken=calc_doc.output.mulliken, resp=calc_doc.output.resp, nbo=calc_doc.output.nbo_data, frequencies=calc_doc.output.frequencies, frequency_modes=calc_doc.output.frequency_modes, ) @arrow_incompatible class InputDoc(BaseModel): initial_molecule: Molecule = Field( title="Input Structure", description="Input molecule and calc details for the QChem calculation", ) prev_rem_params: dict[str, Any] | None = Field( None, description="Parameters from a previous qchem calculation in the series", ) rem: dict[str, Any] = Field( None, description="Parameters from the rem section of the current QChem calculation", ) level_of_theory: str | LevelOfTheory | None = Field( None, description="Level of theory used in the qchem calculation" ) task_type: str | TaskType | None = Field( None, description="The type of the QChem calculation : optimization, single point ... etc.", ) tags: list[str] | None = Field( None, title="tag", description="Metadata tagged to a given task." ) solvation_lot_info: dict[str, Any] | str | None = Field( None, description="Str or Dict representation of the solvent method used for the calculation", ) special_run_type: str | None = Field( None, description="Special workflow name (if applicable)" ) smiles: str | None = Field( None, description="Simplified molecular-input line-entry system (SMILES) string for the molecule involved " "in this calculation.", ) calc_type: str | CalcType | None = Field( None, description="A combined dictionary representation of the task type along with the level of theory used", ) @classmethod def from_qchem_calc_doc(cls, calc_doc: Calculation) -> "InputDoc": """ Create qchem calculation input summary from a qchem calculation document. Parameters ---------- calc_doc A QChem calculation document. Returns -------- InputDoc A summary of the input molecule and corresponding calculation parameters """ try: lot_val = calc_doc.level_of_theory.value except AttributeError: lot_val = calc_doc.level_of_theory try: ct_val = calc_doc.calc_type.value except AttributeError: ct_val = calc_doc.calc_type # TODO : modify this to get the different variables from the task doc. return cls( initial_molecule=calc_doc.input.initial_molecule, rem=calc_doc.input.rem, level_of_theory=lot_val, task_type=calc_doc.task_type.value, tags=calc_doc.input.tags, solvation_lot_info=calc_doc.solvation_lot_info, # special_run_type = calc_doc.input.special_run_type, # smiles = calc_doc.input.smiles, calc_type=ct_val, ) @arrow_incompatible class CustodianDoc(BaseModel): corrections: list[Any] | None = Field( None, title="Custodian Corrections", description="List of custodian correction data for calculation.", ) job: dict[str, Any] | QCJob | None = Field( None, title="Custodian Job Data", description="Job data logged by custodian.", ) # AnalysisDoc? Is there a scope for AnalysisDoc in QChem? @arrow_incompatible class TaskDoc(MoleculeMetadata): """ Calculation-level details about QChem calculations that would eventually take over the TaskDocument implementation """ dir_name: str | Path | None = Field( None, description="The directory for this QChem task" ) state: QChemStatus | None = Field( None, description="State of this QChem calculation" ) calcs_reversed: list[Calculation] | None = Field( None, title="Calcs reversed data", description="Detailed data for each QChem calculation contributing to the task document.", ) task_type: CalcType | TaskType | None = Field( None, description="the type of QChem calculation" ) orig_inputs: CalculationInput | dict[str, Any] | None = Field( {}, description="Summary of the original Q-Chem inputs" ) input: InputDoc | None = Field( None, description="The input molecule and calc parameters used to generate the current task document.", ) output: OutputDoc | None = Field( None, description="The exact set of output parameters used to generate the current task document.", ) # TODO: Implement entry dict custodian: list[CustodianDoc] | None = Field( None, title="Calcs reversed data", description="Detailed custodian data for each QChem calculation contributing to the task document.", ) critic2: dict[str, Any] | None = Field( None, description="Outputs from the critic2 calculation if performed" ) custom_smd: str | dict[str, Any] | None = Field( None, description="The seven solvent parameters necessary to define a custom_smd model", ) additional_fields: dict[str, Any] | None = Field( None, description="Any miscellaneous fields passed to the pydantic model" ) # TODO some sort of @validator s if necessary @classmethod def from_directory( cls, dir_name: Path | str, validate_lot: bool = True, store_additional_json: bool = True, additional_fields: dict[str, Any] = None, **qchem_calculation_kwargs, ) -> Self: """ Create a task document from a directory containing QChem files. Parameters ---------- dir_name The path to the folder containing the calculation outputs. validate_lot Flag for matching the basis and functional with the list of functionals consistent with MPCules. Defaults to True. Change to False if you want to create a TaskDoc with other basis sets and functionals. store_additional_json Whether to store additional json files in the calculation directory. additional_fields Dictionary of additional fields to add to output document. **qchem_calculation_kwargs Additional parsing options that will be passed to the :obj:`.Calculation.from_qchem_files` function. Returns ------- QChemTaskDoc A task document for the calculation """ logger.info(f"Getting task doc in: {dir_name}") additional_fields = additional_fields or {} dir_name = Path(dir_name) task_files = _find_qchem_files(dir_name) if len(task_files) == 0: raise FileNotFoundError("No QChem files found!") critic2 = {} custom_smd = {} calcs_reversed = [] for task_name, files in task_files.items(): if task_name == "orig": continue else: calc_doc = Calculation.from_qchem_files( dir_name, task_name, **files, **qchem_calculation_kwargs, validate_lot=validate_lot, ) calcs_reversed.append(calc_doc) # all_qchem_objects.append(qchem_objects) # Lists need to be reversed so that newest calc is the first calc, all_qchem_objects are also reversed to match calcs_reversed.reverse() # all_qchem_objects.reverse() custodian = _parse_custodian(dir_name) additional_json = None if store_additional_json: additional_json = _parse_additional_json(dir_name) for key, _ in additional_json.items(): if key == "processed_critic2": critic2["processed"] = additional_json["processed_critic2"] elif key == "cpreport": critic2["cp"] = additional_json["cpreport"] elif key == "YT": critic2["yt"] = additional_json["yt"] elif key == "bonding": critic2["bonding"] = additional_json["bonding"] elif key == "solvent_data": custom_smd = additional_json["solvent_data"] _orig_inputs = _parse_orig_inputs(dir_name) orig_inputs = ( CalculationInput.from_qcinput(_orig_inputs) if _orig_inputs else {} ) dir_name = get_uri(dir_name) # convert to full path # only store objects from last calculation # TODO: If vasp implementation makes this an option, change here as well qchem_objects = None included_objects = None if qchem_objects: included_objects = list(qchem_objects.keys()) # run_stats = _get_run_stats(calcs_reversed), Discuss whether this is something which is necessary in terms of QChem calcs doc = cls.from_molecule( meta_molecule=calcs_reversed[-1].input.initial_molecule, dir_name=dir_name, calcs_reversed=calcs_reversed, custodian=custodian, additional_json=additional_json, additional_fields=additional_fields, completed_at=calcs_reversed[0].completed_at, orig_inputs=orig_inputs, input=InputDoc.from_qchem_calc_doc(calcs_reversed[0]), output=OutputDoc.from_qchem_calc_doc(calcs_reversed[0]), state=_get_state(calcs_reversed), qchem_objects=qchem_objects, included_objects=included_objects, critic2=critic2, custom_smd=custom_smd, task_type=calcs_reversed[0].task_type, ) # doc = doc.copy(update=additional_fields) doc = doc.model_copy(update=additional_fields) return doc @staticmethod def get_entry( calcs_reversed: list[Calculation], task_id: str | None = None ) -> dict: """ Get a computed entry from a list of QChem calculation documents. Parameters ---------- calcs_reversed A list of QChem calculation documents in reverse order. task_id The job identifier Returns -------- Dict A dict of computed entries """ entry_dict = { "entry_id": task_id, "task_id": task_id, "charge": calcs_reversed[0].output.molecule.charge, "spin_multiplicity": calcs_reversed[0].output.molecule.spin_multiplicity, "level_of_theory": calcs_reversed[-1].input.level_of_theory, "solvent": calcs_reversed[-1].input.solv_spec, "lot_solvent": calcs_reversed[-1].input.lot_solv_combo, "custom_smd": calcs_reversed[-1].input.custom_smd, "task_type": calcs_reversed[-1].input.task_spec, "calc_type": calcs_reversed[-1].input.calc_spec, "tags": calcs_reversed[-1].input.tags, "molecule": calcs_reversed[0].output.molecule, "composition": calcs_reversed[0].output.molecule.composition, "formula": calcs_reversed[ 0 ].output.formula.composition.aplhabetical_formula, "energy": calcs_reversed[0].output.final_energy, "output": calcs_reversed[0].output.as_dict(), "critic2": calcs_reversed[ 0 ].output.critic, # TODO: Unclear about orig_inputs "last_updated": calcs_reversed[0].output.last_updated, } return entry_dict def get_uri(dir_name: str | Path) -> str: """ Return the URI path for a directory. This allows files hosted on different file servers to have distinct locations. Parameters ---------- dir_name : str or Path A directory name. Returns ------- str Full URI path, e.g., "fileserver.host.com:/full/payj/of/fir_name". """ import socket fullpath = Path(dir_name).absolute() hostname = socket.gethostname() try: hostname = socket.gethostbyaddr(hostname)[0] except (socket.gaierror, socket.herror): pass return f"{hostname}:{fullpath}" def _parse_custodian(dir_name: Path) -> dict | None: """ Parse custodian.json file. Calculations done using custodian have a custodian.json file which tracks the makers performed and any errors detected and fixed. Parameters ---------- dir_name Path to calculation directory. Returns -------- dict | None The information parsed from custodian.json file. """ filenames = tuple(dir_name.glob("custodian.json*")) if len(filenames) >= 1: return loadfn(filenames[0], cls=None) return None def _parse_orig_inputs( dir_name: Path, ) -> dict[str, Any] | QCInput: """ Parse original input files. Calculations using custodian generate a *.orig file for the inputs. This is useful to know how the calculation originally started. Parameters ---------- dir_name Path to calculation directory. Returns ------- dict[str, Any] The original molecule, rem, solvent and other data. """ orig_inputs: dict[str, Any] | QCInput = {} if orig_file_path := next(dir_name.glob("*.orig*"), None): orig_inputs = QCInput.from_file(orig_file_path) return orig_inputs def _parse_additional_json(dir_name: Path) -> dict[str, Any]: """Parse additional json files in the directory.""" additional_json = {} for filename in dir_name.glob("*.json*"): key = filename.name.split(".")[0] if key not in ("custodian", "transformations"): if key not in additional_json: additional_json[key] = loadfn(filename, cls=None) return additional_json def _get_state(calcs_reversed: list[Calculation]) -> QChemStatus: """Get state from calculation documents of QChem tasks.""" all_calcs_completed = all( [c.has_qchem_completed == QChemStatus.SUCCESS for c in calcs_reversed] ) if all_calcs_completed: return QChemStatus.SUCCESS return QChemStatus.FAILED # def _get_run_stats(calcs_reversed: list[Calculation]) -> dict[str, RunStatistics]: # """Get summary of runtime statistics for each calculation in this task.""" # run_stats = {} # total = dict( # average_memory=0.0, # max_memory=0.0, # elapsed_time=0.0, # system_time=0.0, # user_time=0.0, # total_time=0.0, # cores=0, # ) def _find_qchem_files( path: str | Path, ) -> dict[str, Any]: """ Find QChem files in a directory. Only the mol.qout file (or alternatively files with the task name as an extension, e.g., mol.qout.opt_0.gz, mol.qout.freq_1.gz, or something like this...) will be returned. Parameters ---------- path Path to a directory to search. Returns ------- dict[str, Any] The filenames of the calculation outputs for each QChem task, given as a ordered dictionary of:: { task_name:{ "qchem_out_file": qcrun_filename, }, ... } If there is only 1 qout file task_name will be "standard" otherwise it will be the extension name like "opt_0" """ path = Path(path) task_files = OrderedDict() in_file_pattern = re.compile(r"^(?Pmol\.(qin|in)(?:\..+)?)(\.gz)?$") for file in path.iterdir(): if file.is_file(): in_match = in_file_pattern.match(file.name) # This block is for generalizing outputs coming from both atomate and manual qchem calculations if in_match: in_task_name = re.sub( r"(\.gz|gz)$", "", in_match.group("in_task_name").replace("mol.qin.", ""), ) in_task_name = in_task_name or "mol.qin" if in_task_name == "orig": task_files[in_task_name] = {"orig_input_file": file.name} elif in_task_name == "last": continue elif in_task_name == "mol.qin" or in_task_name == "mol.in": if in_task_name == "mol.qin": out_file = ( path / "mol.qout.gz" if (path / "mol.qout.gz").exists() else path / "mol.qout" ) else: out_file = ( path / "mol.out.gz" if (path / "mol.out.gz").exists() else path / "mol.out" ) task_files["standard"] = { "qcinput_file": file.name, "qcoutput_file": out_file.name, } # This block will exist only if calcs were run through atomate else: try: task_files[in_task_name] = { "qcinput_file": file.name, "qcoutput_file": Path( "mol.qout." + in_task_name + ".gz" ).name, } except FileNotFoundError: task_files[in_task_name] = { "qcinput_file": file.name, "qcoutput_file": "No qout files exist for this in file", } return task_files ././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1787011028.974707 emmet_core-0.87.2/emmet/core/qchem/0000755000175100017510000000000015240717725016543 5ustar00runnerrunner././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8242714 emmet_core-0.87.2/emmet/core/qchem/__init__.py0000644000175100017510000000000015240717720020635 0ustar00runnerrunner././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011028.9757776 emmet_core-0.87.2/emmet/core/qchem/calc_types/0000755000175100017510000000000015240717725020671 5ustar00runnerrunner././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8250031 emmet_core-0.87.2/emmet/core/qchem/calc_types/__init__.py0000644000175100017510000000041315240717720022773 0ustar00runnerrunner"""Module defining Q-Chem calculation types.""" from emmet.core.qchem.calc_types.enums import CalcType, LevelOfTheory, TaskType from emmet.core.qchem.calc_types.utils import ( calc_type, level_of_theory, task_type, solvent, lot_solvent_string, ) ././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1787011023.825055 emmet_core-0.87.2/emmet/core/qchem/calc_types/calc_types.py0000644000175100017510000000133215240717720023363 0ustar00runnerrunner"""Task types and level of theory components for Q-Chem calculations""" from importlib.resources import files as import_resource_files from monty.serialization import loadfn __author__ = "Evan Spotte-Smith " _calc_type_config = loadfn( str(import_resource_files("emmet.core.qchem.calc_types") / "calc_types.yaml") ) # NB: this would be easier with setattr but the following is less opaque FUNCTIONAL_CLASSES = _calc_type_config.get("FUNCTIONAL_CLASSES") TASK_TYPES = _calc_type_config.get("TASK_TYPES") BASIS_SETS = _calc_type_config.get("BASIS_SETS") SOLVENT_MODELS = _calc_type_config.get("SOLVENT_MODELS") FUNCTIONALS = [rt for functionals in FUNCTIONAL_CLASSES.values() for rt in functionals] ././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1787011023.825055 emmet_core-0.87.2/emmet/core/qchem/calc_types/calc_types.yaml0000644000175100017510000000254715240717720023706 0ustar00runnerrunner# - Task Functional and calc types needed for QChem enums TASK_TYPES: - Single Point - Force - Geometry Optimization - Frequency Analysis - Frequency Flattening Geometry Optimization - Transition State Geometry Optimization - Frequency Flattening Transition State Geometry Optimization - Unknown FUNCTIONAL_CLASSES: gga: - PBE # - PBE-D3(BJ) # - BLYP # - BLYP-D3(BJ) - B97-D - B97-D3 # - mPW91 # - mPW91-D3(BJ) # - VV10 # - rVV10 meta-gga: # - M06-L # - M06-L-D3(0) # - SCAN # - SCAN-D3(BJ) # - TPSS # - TPSS-D3(BJ) - MN12-L # - MN12-L-D3(BJ) - B97M-V - B97M-rV hybrid-gga: # - PBE0 # - PBE0-D3(BJ) - B3LYP # - B3LYP-D3(BJ) # - CAM-B3LYP # - CAM-B3LYP-D3(0) # - mPW1PW91 # - mPW1PW91-D3(BJ) # - wB97X - wB97X-D - wB97X-D3 - wB97X-V hybrid-meta-gga: # - M06-2X # - M06-2X-D3(0) # - M06-HF # - M08-SO # - M11 # - MN15 # - BMK # - BMK-D3(BJ) # - TPSSh # - TPSSh-D3(BJ) # - SCAN0 # - mPWB1K # - mPWB1K-D3(BJ) - wB97M-V BASIS_SETS: - 6-31g* - def2-SVPD - def2-TZVP - def2-TZVPD - def2-TZVPP - def2-TZVPPD - def2-QZVPD - def2-QZVPPD # TODO: add ISOSVP and CMIRS once these are implemented in pymatgen and atomate/atomate2 SOLVENT_MODELS: - VACUUM - PCM - SMD ././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1787011023.825055 emmet_core-0.87.2/emmet/core/qchem/calc_types/em_utils.py0000644000175100017510000001632715240717720023070 0ustar00runnerrunner"""Utilities to determine level of theory, task type, and calculation type for Q-Chem calculations in the pydantic Docs paradigm""" from emmet.core.qchem.calc_types import CalcType, LevelOfTheory, TaskType from emmet.core.qchem.calc_types.calc_types import BASIS_SETS, FUNCTIONALS from emmet.core.qchem.calculation import CalculationInput __author__ = ( "Evan Spotte-Smith , Rishabh Debraj Guha " ) # TODO: better way of translating names functional_synonyms = { "b97mv": "b97m-v", "b97mrv": "b97m-rv", "wb97xd": "wb97x-d", "wb97xd3": "wb97x-d3", "wb97xv": "wb97x-v", "wb97mv": "wb97m-v", } smd_synonyms = { "DIELECTRIC=7,230;N=1,410;ALPHA=0,000;BETA=0,859;GAMMA=36,830;PHI=0,000;PSI=0,000": "diglyme", "DIELECTRIC=18,500;N=1,415;ALPHA=0,000;BETA=0,735;GAMMA=20,200;PHI=0,000;PSI=0,000": "3:7 EC:EMC", } def level_of_theory(parameters: CalculationInput) -> LevelOfTheory: """ Returns the level of theory for a calculation, based on the input parameters given to Q-Chem Args: parameters: dict of Q-Chem input parameters """ funct_raw = parameters.rem.get("method") basis_raw = parameters.rem.get("basis") if funct_raw is None or basis_raw is None: raise ValueError( 'Method and basis must be included in "rem" section ' "of parameters!" ) disp_corr = parameters.rem.get("dft_d") if disp_corr is None: funct_lower = funct_raw.lower() funct_lower = functional_synonyms.get(funct_lower, funct_lower) else: # Replace Q-Chem terms for D3 tails with more common expressions disp_corr = disp_corr.replace("_bj", "(bj)").replace("_zero", "(0)") funct_lower = f"{funct_raw}-{disp_corr}" basis_lower = basis_raw.lower() # --> TODO: replace with enums functional = [f for f in FUNCTIONALS if f.lower() == funct_lower] if not functional: raise ValueError(f"Unexpected functional {funct_lower}!") functional = functional[0] basis = [b for b in BASIS_SETS if b.lower() == basis_lower] if not basis: raise ValueError(f"Unexpected basis set {basis_lower}!") # <-- basis = basis[0] solvent_method = parameters.rem.get("solvent_method", "").lower() if solvent_method == "": solvation = "VACUUM" elif solvent_method in ["pcm", "cosmo"]: solvation = "PCM" # TODO: Add this once added into pymatgen and atomate # elif solvent_method == "isosvp": # if parameters.get("svp", {}).get("idefesr", 0): # solvation = "CMIRS" # else: # solvation = "ISOSVP" elif solvent_method == "smd": solvation = "SMD" else: raise ValueError(f"Unexpected implicit solvent method {solvent_method}!") lot = f"{functional}/{basis}/{solvation}" return LevelOfTheory(lot) def solvent(parameters: CalculationInput, custom_smd: str | None = None) -> str: """ Returns the solvent used for this calculation. Args: parameters: dict of Q-Chem input parameters custom_smd: (Optional) string representing SMD parameters for a non-standard solvent """ lot = level_of_theory(parameters) solvation = lot.value.split("/")[-1] if solvation == "PCM": dielectric = float(parameters.get("solvent", {}).get("dielectric", 78.39)) dielectric_string = f"{dielectric:.2f}".replace(".", ",") return f"DIELECTRIC={dielectric_string}" # TODO: Add this once added into pymatgen and atomate # elif solvation == "ISOSVP": # dielectric = float(parameters.get("svp", {}).get("dielst", 78.39)) # rho = float(parameters.get("svp", {}).get("rhoiso", 0.001)) # return f"DIELECTRIC={round(dielectric, 2)},RHO={round(rho, 4)}" # elif solvation == "CMIRS": # dielectric = float(parameters.get("svp", {}).get("dielst", 78.39)) # rho = float(parameters.get("svp", {}).get("rhoiso", 0.001)) # a = parameters.get("pcm_nonels", {}).get("a") # b = parameters.get("pcm_nonels", {}).get("b") # c = parameters.get("pcm_nonels", {}).get("c") # d = parameters.get("pcm_nonels", {}).get("d") # solvrho = parameters.get("pcm_nonels", {}).get("solvrho") # gamma = parameters.get("pcm_nonels", {}).get("gamma") # # string = f"DIELECTRIC={round(dielectric, 2)},RHO={round(rho, 4)}" # for name, (piece, digits) in {"A": (a, 6), "B": (b, 6), "C": (c, 1), "D": (d, 3), # "SOLVRHO": (solvrho, 2), "GAMMA": (gamma, 1)}.items(): # if piece is None: # piecestring = "NONE" # else: # piecestring = f"{name}={round(float(piece), digits)}" # string += "," + piecestring # return string elif solvation == "SMD": solvent = parameters.get("smx", {}).get("solvent", "water") if solvent == "other": if custom_smd is None: raise ValueError( "SMD calculation with solvent=other requires custom_smd!" ) names = ["DIELECTRIC", "N", "ALPHA", "BETA", "GAMMA", "PHI", "PSI"] numbers = [float(x) for x in custom_smd.split(",")] string = "" for name, number in zip(names, numbers): string += f"{name}={number:.3f};" return string.rstrip(",").rstrip(";").replace(".", ",") else: return f"SOLVENT={solvent.upper()}" else: return "NONE" def lot_solvent_string( parameters: CalculationInput, custom_smd: str | None = None ) -> str: """ Returns a string representation of the level of theory and solvent used for this calculation. Args: parameters: dict of Q-Chem input parameters custom_smd: (Optional) string representing SMD parameters for a non-standard solvent """ lot = level_of_theory(parameters).value solv = solvent(parameters, custom_smd=custom_smd) return f"{lot}({solv})" def task_type( parameters: CalculationInput, special_run_type: str | None = None ) -> TaskType: if special_run_type == "frequency_flattener": return TaskType("Frequency Flattening Geometry Optimization") elif special_run_type == "ts_frequency_flattener": return TaskType("Frequency Flattening Transition State Geometry Optimization") if parameters.job_type == "sp": return TaskType("Single Point") elif parameters.job_type == "force": return TaskType("Force") elif parameters.job_type == "opt": return TaskType("Geometry Optimization") elif parameters.job_type == "ts": return TaskType("Transition State Geometry Optimization") elif parameters.job_type == "freq": return TaskType("Frequency Analysis") return TaskType("Unknown") def calc_type( parameters: CalculationInput, special_run_type: str | None = None ) -> CalcType: """ Determines the calc type Args: inputs: inputs dict with an incar, kpoints, potcar, and poscar dictionaries parameters: Dictionary of VASP parameters from Vasprun.xml """ rt = level_of_theory(parameters).value tt = task_type(parameters, special_run_type=special_run_type).value return CalcType(f"{rt} {tt}") ././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1787011023.825055 emmet_core-0.87.2/emmet/core/qchem/calc_types/enums.py0000644000175100017510000076106715240717720022406 0ustar00runnerrunner""" Autogenerated Enums for Q-Chem LevelOfTheory, TaskType, and CalcType. Do not edit this by hand to add or remove enums. Instead, edit dev_scripts/generate_enums.py and/or emmet/core/qchem/calc_types/calc_types.yaml """ from emmet.core.types.enums import ValueEnum class LevelOfTheory(ValueEnum): """Levels of theory for calculations in Q-Chem.""" B3LYP_6_31g_d_PCM = "B3LYP/6-31g*/PCM" B3LYP_6_31g_d_SMD = "B3LYP/6-31g*/SMD" B3LYP_6_31g_d_VACUUM = "B3LYP/6-31g*/VACUUM" B3LYP_def2_QZVPD_PCM = "B3LYP/def2-QZVPD/PCM" B3LYP_def2_QZVPD_SMD = "B3LYP/def2-QZVPD/SMD" B3LYP_def2_QZVPD_VACUUM = "B3LYP/def2-QZVPD/VACUUM" B3LYP_def2_QZVPPD_PCM = "B3LYP/def2-QZVPPD/PCM" B3LYP_def2_QZVPPD_SMD = "B3LYP/def2-QZVPPD/SMD" B3LYP_def2_QZVPPD_VACUUM = "B3LYP/def2-QZVPPD/VACUUM" B3LYP_def2_SVPD_PCM = "B3LYP/def2-SVPD/PCM" B3LYP_def2_SVPD_SMD = "B3LYP/def2-SVPD/SMD" B3LYP_def2_SVPD_VACUUM = "B3LYP/def2-SVPD/VACUUM" B3LYP_def2_TZVPD_PCM = "B3LYP/def2-TZVPD/PCM" B3LYP_def2_TZVPD_SMD = "B3LYP/def2-TZVPD/SMD" B3LYP_def2_TZVPD_VACUUM = "B3LYP/def2-TZVPD/VACUUM" B3LYP_def2_TZVPPD_PCM = "B3LYP/def2-TZVPPD/PCM" B3LYP_def2_TZVPPD_SMD = "B3LYP/def2-TZVPPD/SMD" B3LYP_def2_TZVPPD_VACUUM = "B3LYP/def2-TZVPPD/VACUUM" B3LYP_def2_TZVPP_PCM = "B3LYP/def2-TZVPP/PCM" B3LYP_def2_TZVPP_SMD = "B3LYP/def2-TZVPP/SMD" B3LYP_def2_TZVPP_VACUUM = "B3LYP/def2-TZVPP/VACUUM" B3LYP_def2_TZVP_PCM = "B3LYP/def2-TZVP/PCM" B3LYP_def2_TZVP_SMD = "B3LYP/def2-TZVP/SMD" B3LYP_def2_TZVP_VACUUM = "B3LYP/def2-TZVP/VACUUM" B97M_V_6_31g_d_PCM = "B97M-V/6-31g*/PCM" B97M_V_6_31g_d_SMD = "B97M-V/6-31g*/SMD" B97M_V_6_31g_d_VACUUM = "B97M-V/6-31g*/VACUUM" B97M_V_def2_QZVPD_PCM = "B97M-V/def2-QZVPD/PCM" B97M_V_def2_QZVPD_SMD = "B97M-V/def2-QZVPD/SMD" B97M_V_def2_QZVPD_VACUUM = "B97M-V/def2-QZVPD/VACUUM" B97M_V_def2_QZVPPD_PCM = "B97M-V/def2-QZVPPD/PCM" B97M_V_def2_QZVPPD_SMD = "B97M-V/def2-QZVPPD/SMD" B97M_V_def2_QZVPPD_VACUUM = "B97M-V/def2-QZVPPD/VACUUM" B97M_V_def2_SVPD_PCM = "B97M-V/def2-SVPD/PCM" B97M_V_def2_SVPD_SMD = "B97M-V/def2-SVPD/SMD" B97M_V_def2_SVPD_VACUUM = "B97M-V/def2-SVPD/VACUUM" B97M_V_def2_TZVPD_PCM = "B97M-V/def2-TZVPD/PCM" B97M_V_def2_TZVPD_SMD = "B97M-V/def2-TZVPD/SMD" B97M_V_def2_TZVPD_VACUUM = "B97M-V/def2-TZVPD/VACUUM" B97M_V_def2_TZVPPD_PCM = "B97M-V/def2-TZVPPD/PCM" B97M_V_def2_TZVPPD_SMD = "B97M-V/def2-TZVPPD/SMD" B97M_V_def2_TZVPPD_VACUUM = "B97M-V/def2-TZVPPD/VACUUM" B97M_V_def2_TZVPP_PCM = "B97M-V/def2-TZVPP/PCM" B97M_V_def2_TZVPP_SMD = "B97M-V/def2-TZVPP/SMD" B97M_V_def2_TZVPP_VACUUM = "B97M-V/def2-TZVPP/VACUUM" B97M_V_def2_TZVP_PCM = "B97M-V/def2-TZVP/PCM" B97M_V_def2_TZVP_SMD = "B97M-V/def2-TZVP/SMD" B97M_V_def2_TZVP_VACUUM = "B97M-V/def2-TZVP/VACUUM" B97M_rV_6_31g_d_PCM = "B97M-rV/6-31g*/PCM" B97M_rV_6_31g_d_SMD = "B97M-rV/6-31g*/SMD" B97M_rV_6_31g_d_VACUUM = "B97M-rV/6-31g*/VACUUM" B97M_rV_def2_QZVPD_PCM = "B97M-rV/def2-QZVPD/PCM" B97M_rV_def2_QZVPD_SMD = "B97M-rV/def2-QZVPD/SMD" B97M_rV_def2_QZVPD_VACUUM = "B97M-rV/def2-QZVPD/VACUUM" B97M_rV_def2_QZVPPD_PCM = "B97M-rV/def2-QZVPPD/PCM" B97M_rV_def2_QZVPPD_SMD = "B97M-rV/def2-QZVPPD/SMD" B97M_rV_def2_QZVPPD_VACUUM = "B97M-rV/def2-QZVPPD/VACUUM" B97M_rV_def2_SVPD_PCM = "B97M-rV/def2-SVPD/PCM" B97M_rV_def2_SVPD_SMD = "B97M-rV/def2-SVPD/SMD" B97M_rV_def2_SVPD_VACUUM = "B97M-rV/def2-SVPD/VACUUM" B97M_rV_def2_TZVPD_PCM = "B97M-rV/def2-TZVPD/PCM" B97M_rV_def2_TZVPD_SMD = "B97M-rV/def2-TZVPD/SMD" B97M_rV_def2_TZVPD_VACUUM = "B97M-rV/def2-TZVPD/VACUUM" B97M_rV_def2_TZVPPD_PCM = "B97M-rV/def2-TZVPPD/PCM" B97M_rV_def2_TZVPPD_SMD = "B97M-rV/def2-TZVPPD/SMD" B97M_rV_def2_TZVPPD_VACUUM = "B97M-rV/def2-TZVPPD/VACUUM" B97M_rV_def2_TZVPP_PCM = "B97M-rV/def2-TZVPP/PCM" B97M_rV_def2_TZVPP_SMD = "B97M-rV/def2-TZVPP/SMD" B97M_rV_def2_TZVPP_VACUUM = "B97M-rV/def2-TZVPP/VACUUM" B97M_rV_def2_TZVP_PCM = "B97M-rV/def2-TZVP/PCM" B97M_rV_def2_TZVP_SMD = "B97M-rV/def2-TZVP/SMD" B97M_rV_def2_TZVP_VACUUM = "B97M-rV/def2-TZVP/VACUUM" B97_D3_6_31g_d_PCM = "B97-D3/6-31g*/PCM" B97_D3_6_31g_d_SMD = "B97-D3/6-31g*/SMD" B97_D3_6_31g_d_VACUUM = "B97-D3/6-31g*/VACUUM" B97_D3_def2_QZVPD_PCM = "B97-D3/def2-QZVPD/PCM" B97_D3_def2_QZVPD_SMD = "B97-D3/def2-QZVPD/SMD" B97_D3_def2_QZVPD_VACUUM = "B97-D3/def2-QZVPD/VACUUM" B97_D3_def2_QZVPPD_PCM = "B97-D3/def2-QZVPPD/PCM" B97_D3_def2_QZVPPD_SMD = "B97-D3/def2-QZVPPD/SMD" B97_D3_def2_QZVPPD_VACUUM = "B97-D3/def2-QZVPPD/VACUUM" B97_D3_def2_SVPD_PCM = "B97-D3/def2-SVPD/PCM" B97_D3_def2_SVPD_SMD = "B97-D3/def2-SVPD/SMD" B97_D3_def2_SVPD_VACUUM = "B97-D3/def2-SVPD/VACUUM" B97_D3_def2_TZVPD_PCM = "B97-D3/def2-TZVPD/PCM" B97_D3_def2_TZVPD_SMD = "B97-D3/def2-TZVPD/SMD" B97_D3_def2_TZVPD_VACUUM = "B97-D3/def2-TZVPD/VACUUM" B97_D3_def2_TZVPPD_PCM = "B97-D3/def2-TZVPPD/PCM" B97_D3_def2_TZVPPD_SMD = "B97-D3/def2-TZVPPD/SMD" B97_D3_def2_TZVPPD_VACUUM = "B97-D3/def2-TZVPPD/VACUUM" B97_D3_def2_TZVPP_PCM = "B97-D3/def2-TZVPP/PCM" B97_D3_def2_TZVPP_SMD = "B97-D3/def2-TZVPP/SMD" B97_D3_def2_TZVPP_VACUUM = "B97-D3/def2-TZVPP/VACUUM" B97_D3_def2_TZVP_PCM = "B97-D3/def2-TZVP/PCM" B97_D3_def2_TZVP_SMD = "B97-D3/def2-TZVP/SMD" B97_D3_def2_TZVP_VACUUM = "B97-D3/def2-TZVP/VACUUM" B97_D_6_31g_d_PCM = "B97-D/6-31g*/PCM" B97_D_6_31g_d_SMD = "B97-D/6-31g*/SMD" B97_D_6_31g_d_VACUUM = "B97-D/6-31g*/VACUUM" B97_D_def2_QZVPD_PCM = "B97-D/def2-QZVPD/PCM" B97_D_def2_QZVPD_SMD = "B97-D/def2-QZVPD/SMD" B97_D_def2_QZVPD_VACUUM = "B97-D/def2-QZVPD/VACUUM" B97_D_def2_QZVPPD_PCM = "B97-D/def2-QZVPPD/PCM" B97_D_def2_QZVPPD_SMD = "B97-D/def2-QZVPPD/SMD" B97_D_def2_QZVPPD_VACUUM = "B97-D/def2-QZVPPD/VACUUM" B97_D_def2_SVPD_PCM = "B97-D/def2-SVPD/PCM" B97_D_def2_SVPD_SMD = "B97-D/def2-SVPD/SMD" B97_D_def2_SVPD_VACUUM = "B97-D/def2-SVPD/VACUUM" B97_D_def2_TZVPD_PCM = "B97-D/def2-TZVPD/PCM" B97_D_def2_TZVPD_SMD = "B97-D/def2-TZVPD/SMD" B97_D_def2_TZVPD_VACUUM = "B97-D/def2-TZVPD/VACUUM" B97_D_def2_TZVPPD_PCM = "B97-D/def2-TZVPPD/PCM" B97_D_def2_TZVPPD_SMD = "B97-D/def2-TZVPPD/SMD" B97_D_def2_TZVPPD_VACUUM = "B97-D/def2-TZVPPD/VACUUM" B97_D_def2_TZVPP_PCM = "B97-D/def2-TZVPP/PCM" B97_D_def2_TZVPP_SMD = "B97-D/def2-TZVPP/SMD" B97_D_def2_TZVPP_VACUUM = "B97-D/def2-TZVPP/VACUUM" B97_D_def2_TZVP_PCM = "B97-D/def2-TZVP/PCM" B97_D_def2_TZVP_SMD = "B97-D/def2-TZVP/SMD" B97_D_def2_TZVP_VACUUM = "B97-D/def2-TZVP/VACUUM" MN12_L_6_31g_d_PCM = "MN12-L/6-31g*/PCM" MN12_L_6_31g_d_SMD = "MN12-L/6-31g*/SMD" MN12_L_6_31g_d_VACUUM = "MN12-L/6-31g*/VACUUM" MN12_L_def2_QZVPD_PCM = "MN12-L/def2-QZVPD/PCM" MN12_L_def2_QZVPD_SMD = "MN12-L/def2-QZVPD/SMD" MN12_L_def2_QZVPD_VACUUM = "MN12-L/def2-QZVPD/VACUUM" MN12_L_def2_QZVPPD_PCM = "MN12-L/def2-QZVPPD/PCM" MN12_L_def2_QZVPPD_SMD = "MN12-L/def2-QZVPPD/SMD" MN12_L_def2_QZVPPD_VACUUM = "MN12-L/def2-QZVPPD/VACUUM" MN12_L_def2_SVPD_PCM = "MN12-L/def2-SVPD/PCM" MN12_L_def2_SVPD_SMD = "MN12-L/def2-SVPD/SMD" MN12_L_def2_SVPD_VACUUM = "MN12-L/def2-SVPD/VACUUM" MN12_L_def2_TZVPD_PCM = "MN12-L/def2-TZVPD/PCM" MN12_L_def2_TZVPD_SMD = "MN12-L/def2-TZVPD/SMD" MN12_L_def2_TZVPD_VACUUM = "MN12-L/def2-TZVPD/VACUUM" MN12_L_def2_TZVPPD_PCM = "MN12-L/def2-TZVPPD/PCM" MN12_L_def2_TZVPPD_SMD = "MN12-L/def2-TZVPPD/SMD" MN12_L_def2_TZVPPD_VACUUM = "MN12-L/def2-TZVPPD/VACUUM" MN12_L_def2_TZVPP_PCM = "MN12-L/def2-TZVPP/PCM" MN12_L_def2_TZVPP_SMD = "MN12-L/def2-TZVPP/SMD" MN12_L_def2_TZVPP_VACUUM = "MN12-L/def2-TZVPP/VACUUM" MN12_L_def2_TZVP_PCM = "MN12-L/def2-TZVP/PCM" MN12_L_def2_TZVP_SMD = "MN12-L/def2-TZVP/SMD" MN12_L_def2_TZVP_VACUUM = "MN12-L/def2-TZVP/VACUUM" PBE_6_31g_d_PCM = "PBE/6-31g*/PCM" PBE_6_31g_d_SMD = "PBE/6-31g*/SMD" PBE_6_31g_d_VACUUM = "PBE/6-31g*/VACUUM" PBE_def2_QZVPD_PCM = "PBE/def2-QZVPD/PCM" PBE_def2_QZVPD_SMD = "PBE/def2-QZVPD/SMD" PBE_def2_QZVPD_VACUUM = "PBE/def2-QZVPD/VACUUM" PBE_def2_QZVPPD_PCM = "PBE/def2-QZVPPD/PCM" PBE_def2_QZVPPD_SMD = "PBE/def2-QZVPPD/SMD" PBE_def2_QZVPPD_VACUUM = "PBE/def2-QZVPPD/VACUUM" PBE_def2_SVPD_PCM = "PBE/def2-SVPD/PCM" PBE_def2_SVPD_SMD = "PBE/def2-SVPD/SMD" PBE_def2_SVPD_VACUUM = "PBE/def2-SVPD/VACUUM" PBE_def2_TZVPD_PCM = "PBE/def2-TZVPD/PCM" PBE_def2_TZVPD_SMD = "PBE/def2-TZVPD/SMD" PBE_def2_TZVPD_VACUUM = "PBE/def2-TZVPD/VACUUM" PBE_def2_TZVPPD_PCM = "PBE/def2-TZVPPD/PCM" PBE_def2_TZVPPD_SMD = "PBE/def2-TZVPPD/SMD" PBE_def2_TZVPPD_VACUUM = "PBE/def2-TZVPPD/VACUUM" PBE_def2_TZVPP_PCM = "PBE/def2-TZVPP/PCM" PBE_def2_TZVPP_SMD = "PBE/def2-TZVPP/SMD" PBE_def2_TZVPP_VACUUM = "PBE/def2-TZVPP/VACUUM" PBE_def2_TZVP_PCM = "PBE/def2-TZVP/PCM" PBE_def2_TZVP_SMD = "PBE/def2-TZVP/SMD" PBE_def2_TZVP_VACUUM = "PBE/def2-TZVP/VACUUM" wB97M_V_6_31g_d_PCM = "wB97M-V/6-31g*/PCM" wB97M_V_6_31g_d_SMD = "wB97M-V/6-31g*/SMD" wB97M_V_6_31g_d_VACUUM = "wB97M-V/6-31g*/VACUUM" wB97M_V_def2_QZVPD_PCM = "wB97M-V/def2-QZVPD/PCM" wB97M_V_def2_QZVPD_SMD = "wB97M-V/def2-QZVPD/SMD" wB97M_V_def2_QZVPD_VACUUM = "wB97M-V/def2-QZVPD/VACUUM" wB97M_V_def2_QZVPPD_PCM = "wB97M-V/def2-QZVPPD/PCM" wB97M_V_def2_QZVPPD_SMD = "wB97M-V/def2-QZVPPD/SMD" wB97M_V_def2_QZVPPD_VACUUM = "wB97M-V/def2-QZVPPD/VACUUM" wB97M_V_def2_SVPD_PCM = "wB97M-V/def2-SVPD/PCM" wB97M_V_def2_SVPD_SMD = "wB97M-V/def2-SVPD/SMD" wB97M_V_def2_SVPD_VACUUM = "wB97M-V/def2-SVPD/VACUUM" wB97M_V_def2_TZVPD_PCM = "wB97M-V/def2-TZVPD/PCM" wB97M_V_def2_TZVPD_SMD = "wB97M-V/def2-TZVPD/SMD" wB97M_V_def2_TZVPD_VACUUM = "wB97M-V/def2-TZVPD/VACUUM" wB97M_V_def2_TZVPPD_PCM = "wB97M-V/def2-TZVPPD/PCM" wB97M_V_def2_TZVPPD_SMD = "wB97M-V/def2-TZVPPD/SMD" wB97M_V_def2_TZVPPD_VACUUM = "wB97M-V/def2-TZVPPD/VACUUM" wB97M_V_def2_TZVPP_PCM = "wB97M-V/def2-TZVPP/PCM" wB97M_V_def2_TZVPP_SMD = "wB97M-V/def2-TZVPP/SMD" wB97M_V_def2_TZVPP_VACUUM = "wB97M-V/def2-TZVPP/VACUUM" wB97M_V_def2_TZVP_PCM = "wB97M-V/def2-TZVP/PCM" wB97M_V_def2_TZVP_SMD = "wB97M-V/def2-TZVP/SMD" wB97M_V_def2_TZVP_VACUUM = "wB97M-V/def2-TZVP/VACUUM" wB97X_D3_6_31g_d_PCM = "wB97X-D3/6-31g*/PCM" wB97X_D3_6_31g_d_SMD = "wB97X-D3/6-31g*/SMD" wB97X_D3_6_31g_d_VACUUM = "wB97X-D3/6-31g*/VACUUM" wB97X_D3_def2_QZVPD_PCM = "wB97X-D3/def2-QZVPD/PCM" wB97X_D3_def2_QZVPD_SMD = "wB97X-D3/def2-QZVPD/SMD" wB97X_D3_def2_QZVPD_VACUUM = "wB97X-D3/def2-QZVPD/VACUUM" wB97X_D3_def2_QZVPPD_PCM = "wB97X-D3/def2-QZVPPD/PCM" wB97X_D3_def2_QZVPPD_SMD = "wB97X-D3/def2-QZVPPD/SMD" wB97X_D3_def2_QZVPPD_VACUUM = "wB97X-D3/def2-QZVPPD/VACUUM" wB97X_D3_def2_SVPD_PCM = "wB97X-D3/def2-SVPD/PCM" wB97X_D3_def2_SVPD_SMD = "wB97X-D3/def2-SVPD/SMD" wB97X_D3_def2_SVPD_VACUUM = "wB97X-D3/def2-SVPD/VACUUM" wB97X_D3_def2_TZVPD_PCM = "wB97X-D3/def2-TZVPD/PCM" wB97X_D3_def2_TZVPD_SMD = "wB97X-D3/def2-TZVPD/SMD" wB97X_D3_def2_TZVPD_VACUUM = "wB97X-D3/def2-TZVPD/VACUUM" wB97X_D3_def2_TZVPPD_PCM = "wB97X-D3/def2-TZVPPD/PCM" wB97X_D3_def2_TZVPPD_SMD = "wB97X-D3/def2-TZVPPD/SMD" wB97X_D3_def2_TZVPPD_VACUUM = "wB97X-D3/def2-TZVPPD/VACUUM" wB97X_D3_def2_TZVPP_PCM = "wB97X-D3/def2-TZVPP/PCM" wB97X_D3_def2_TZVPP_SMD = "wB97X-D3/def2-TZVPP/SMD" wB97X_D3_def2_TZVPP_VACUUM = "wB97X-D3/def2-TZVPP/VACUUM" wB97X_D3_def2_TZVP_PCM = "wB97X-D3/def2-TZVP/PCM" wB97X_D3_def2_TZVP_SMD = "wB97X-D3/def2-TZVP/SMD" wB97X_D3_def2_TZVP_VACUUM = "wB97X-D3/def2-TZVP/VACUUM" wB97X_D_6_31g_d_PCM = "wB97X-D/6-31g*/PCM" wB97X_D_6_31g_d_SMD = "wB97X-D/6-31g*/SMD" wB97X_D_6_31g_d_VACUUM = "wB97X-D/6-31g*/VACUUM" wB97X_D_def2_QZVPD_PCM = "wB97X-D/def2-QZVPD/PCM" wB97X_D_def2_QZVPD_SMD = "wB97X-D/def2-QZVPD/SMD" wB97X_D_def2_QZVPD_VACUUM = "wB97X-D/def2-QZVPD/VACUUM" wB97X_D_def2_QZVPPD_PCM = "wB97X-D/def2-QZVPPD/PCM" wB97X_D_def2_QZVPPD_SMD = "wB97X-D/def2-QZVPPD/SMD" wB97X_D_def2_QZVPPD_VACUUM = "wB97X-D/def2-QZVPPD/VACUUM" wB97X_D_def2_SVPD_PCM = "wB97X-D/def2-SVPD/PCM" wB97X_D_def2_SVPD_SMD = "wB97X-D/def2-SVPD/SMD" wB97X_D_def2_SVPD_VACUUM = "wB97X-D/def2-SVPD/VACUUM" wB97X_D_def2_TZVPD_PCM = "wB97X-D/def2-TZVPD/PCM" wB97X_D_def2_TZVPD_SMD = "wB97X-D/def2-TZVPD/SMD" wB97X_D_def2_TZVPD_VACUUM = "wB97X-D/def2-TZVPD/VACUUM" wB97X_D_def2_TZVPPD_PCM = "wB97X-D/def2-TZVPPD/PCM" wB97X_D_def2_TZVPPD_SMD = "wB97X-D/def2-TZVPPD/SMD" wB97X_D_def2_TZVPPD_VACUUM = "wB97X-D/def2-TZVPPD/VACUUM" wB97X_D_def2_TZVPP_PCM = "wB97X-D/def2-TZVPP/PCM" wB97X_D_def2_TZVPP_SMD = "wB97X-D/def2-TZVPP/SMD" wB97X_D_def2_TZVPP_VACUUM = "wB97X-D/def2-TZVPP/VACUUM" wB97X_D_def2_TZVP_PCM = "wB97X-D/def2-TZVP/PCM" wB97X_D_def2_TZVP_SMD = "wB97X-D/def2-TZVP/SMD" wB97X_D_def2_TZVP_VACUUM = "wB97X-D/def2-TZVP/VACUUM" wB97X_V_6_31g_d_PCM = "wB97X-V/6-31g*/PCM" wB97X_V_6_31g_d_SMD = "wB97X-V/6-31g*/SMD" wB97X_V_6_31g_d_VACUUM = "wB97X-V/6-31g*/VACUUM" wB97X_V_def2_QZVPD_PCM = "wB97X-V/def2-QZVPD/PCM" wB97X_V_def2_QZVPD_SMD = "wB97X-V/def2-QZVPD/SMD" wB97X_V_def2_QZVPD_VACUUM = "wB97X-V/def2-QZVPD/VACUUM" wB97X_V_def2_QZVPPD_PCM = "wB97X-V/def2-QZVPPD/PCM" wB97X_V_def2_QZVPPD_SMD = "wB97X-V/def2-QZVPPD/SMD" wB97X_V_def2_QZVPPD_VACUUM = "wB97X-V/def2-QZVPPD/VACUUM" wB97X_V_def2_SVPD_PCM = "wB97X-V/def2-SVPD/PCM" wB97X_V_def2_SVPD_SMD = "wB97X-V/def2-SVPD/SMD" wB97X_V_def2_SVPD_VACUUM = "wB97X-V/def2-SVPD/VACUUM" wB97X_V_def2_TZVPD_PCM = "wB97X-V/def2-TZVPD/PCM" wB97X_V_def2_TZVPD_SMD = "wB97X-V/def2-TZVPD/SMD" wB97X_V_def2_TZVPD_VACUUM = "wB97X-V/def2-TZVPD/VACUUM" wB97X_V_def2_TZVPPD_PCM = "wB97X-V/def2-TZVPPD/PCM" wB97X_V_def2_TZVPPD_SMD = "wB97X-V/def2-TZVPPD/SMD" wB97X_V_def2_TZVPPD_VACUUM = "wB97X-V/def2-TZVPPD/VACUUM" wB97X_V_def2_TZVPP_PCM = "wB97X-V/def2-TZVPP/PCM" wB97X_V_def2_TZVPP_SMD = "wB97X-V/def2-TZVPP/SMD" wB97X_V_def2_TZVPP_VACUUM = "wB97X-V/def2-TZVPP/VACUUM" wB97X_V_def2_TZVP_PCM = "wB97X-V/def2-TZVP/PCM" wB97X_V_def2_TZVP_SMD = "wB97X-V/def2-TZVP/SMD" wB97X_V_def2_TZVP_VACUUM = "wB97X-V/def2-TZVP/VACUUM" class TaskType(ValueEnum): """Calculation task types for Q-Chem.""" Force = "Force" Frequency_Analysis = "Frequency Analysis" Frequency_Flattening_Geometry_Optimization = ( "Frequency Flattening Geometry Optimization" ) Frequency_Flattening_Transition_State_Geometry_Optimization = ( "Frequency Flattening Transition State Geometry Optimization" ) Geometry_Optimization = "Geometry Optimization" Single_Point = "Single Point" Transition_State_Geometry_Optimization = "Transition State Geometry Optimization" Unknown = "Unknown" class CalcType(ValueEnum): """Calculation types (LOT + task type) for Q-Chem.""" B3LYP_6_31g_d_PCM_Force = "B3LYP/6-31g*/PCM Force" B3LYP_6_31g_d_PCM_Frequency_Analysis = "B3LYP/6-31g*/PCM Frequency Analysis" B3LYP_6_31g_d_PCM_Frequency_Flattening_Geometry_Optimization = ( "B3LYP/6-31g*/PCM Frequency Flattening Geometry Optimization" ) B3LYP_6_31g_d_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = ( "B3LYP/6-31g*/PCM Frequency Flattening Transition State Geometry Optimization" ) B3LYP_6_31g_d_PCM_Geometry_Optimization = "B3LYP/6-31g*/PCM Geometry Optimization" B3LYP_6_31g_d_PCM_Single_Point = "B3LYP/6-31g*/PCM Single Point" B3LYP_6_31g_d_PCM_Transition_State_Geometry_Optimization = ( "B3LYP/6-31g*/PCM Transition State Geometry Optimization" ) B3LYP_6_31g_d_PCM_Unknown = "B3LYP/6-31g*/PCM Unknown" B3LYP_6_31g_d_SMD_Force = "B3LYP/6-31g*/SMD Force" B3LYP_6_31g_d_SMD_Frequency_Analysis = "B3LYP/6-31g*/SMD Frequency Analysis" B3LYP_6_31g_d_SMD_Frequency_Flattening_Geometry_Optimization = ( "B3LYP/6-31g*/SMD Frequency Flattening Geometry Optimization" ) B3LYP_6_31g_d_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = ( "B3LYP/6-31g*/SMD Frequency Flattening Transition State Geometry Optimization" ) B3LYP_6_31g_d_SMD_Geometry_Optimization = "B3LYP/6-31g*/SMD Geometry Optimization" B3LYP_6_31g_d_SMD_Single_Point = "B3LYP/6-31g*/SMD Single Point" B3LYP_6_31g_d_SMD_Transition_State_Geometry_Optimization = ( "B3LYP/6-31g*/SMD Transition State Geometry Optimization" ) B3LYP_6_31g_d_SMD_Unknown = "B3LYP/6-31g*/SMD Unknown" B3LYP_6_31g_d_VACUUM_Force = "B3LYP/6-31g*/VACUUM Force" B3LYP_6_31g_d_VACUUM_Frequency_Analysis = "B3LYP/6-31g*/VACUUM Frequency Analysis" B3LYP_6_31g_d_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "B3LYP/6-31g*/VACUUM Frequency Flattening Geometry Optimization" ) B3LYP_6_31g_d_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B3LYP/6-31g*/VACUUM Frequency Flattening Transition State Geometry Optimization" B3LYP_6_31g_d_VACUUM_Geometry_Optimization = ( "B3LYP/6-31g*/VACUUM Geometry Optimization" ) B3LYP_6_31g_d_VACUUM_Single_Point = "B3LYP/6-31g*/VACUUM Single Point" B3LYP_6_31g_d_VACUUM_Transition_State_Geometry_Optimization = ( "B3LYP/6-31g*/VACUUM Transition State Geometry Optimization" ) B3LYP_6_31g_d_VACUUM_Unknown = "B3LYP/6-31g*/VACUUM Unknown" B3LYP_def2_QZVPD_PCM_Force = "B3LYP/def2-QZVPD/PCM Force" B3LYP_def2_QZVPD_PCM_Frequency_Analysis = "B3LYP/def2-QZVPD/PCM Frequency Analysis" B3LYP_def2_QZVPD_PCM_Frequency_Flattening_Geometry_Optimization = ( "B3LYP/def2-QZVPD/PCM Frequency Flattening Geometry Optimization" ) B3LYP_def2_QZVPD_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B3LYP/def2-QZVPD/PCM Frequency Flattening Transition State Geometry Optimization" B3LYP_def2_QZVPD_PCM_Geometry_Optimization = ( "B3LYP/def2-QZVPD/PCM Geometry Optimization" ) B3LYP_def2_QZVPD_PCM_Single_Point = "B3LYP/def2-QZVPD/PCM Single Point" B3LYP_def2_QZVPD_PCM_Transition_State_Geometry_Optimization = ( "B3LYP/def2-QZVPD/PCM Transition State Geometry Optimization" ) B3LYP_def2_QZVPD_PCM_Unknown = "B3LYP/def2-QZVPD/PCM Unknown" B3LYP_def2_QZVPD_SMD_Force = "B3LYP/def2-QZVPD/SMD Force" B3LYP_def2_QZVPD_SMD_Frequency_Analysis = "B3LYP/def2-QZVPD/SMD Frequency Analysis" B3LYP_def2_QZVPD_SMD_Frequency_Flattening_Geometry_Optimization = ( "B3LYP/def2-QZVPD/SMD Frequency Flattening Geometry Optimization" ) B3LYP_def2_QZVPD_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "B3LYP/def2-QZVPD/SMD Frequency Flattening Transition State Geometry Optimization" B3LYP_def2_QZVPD_SMD_Geometry_Optimization = ( "B3LYP/def2-QZVPD/SMD Geometry Optimization" ) B3LYP_def2_QZVPD_SMD_Single_Point = "B3LYP/def2-QZVPD/SMD Single Point" B3LYP_def2_QZVPD_SMD_Transition_State_Geometry_Optimization = ( "B3LYP/def2-QZVPD/SMD Transition State Geometry Optimization" ) B3LYP_def2_QZVPD_SMD_Unknown = "B3LYP/def2-QZVPD/SMD Unknown" B3LYP_def2_QZVPD_VACUUM_Force = "B3LYP/def2-QZVPD/VACUUM Force" B3LYP_def2_QZVPD_VACUUM_Frequency_Analysis = ( "B3LYP/def2-QZVPD/VACUUM Frequency Analysis" ) B3LYP_def2_QZVPD_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "B3LYP/def2-QZVPD/VACUUM Frequency Flattening Geometry Optimization" ) B3LYP_def2_QZVPD_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B3LYP/def2-QZVPD/VACUUM Frequency Flattening Transition State Geometry Optimization" B3LYP_def2_QZVPD_VACUUM_Geometry_Optimization = ( "B3LYP/def2-QZVPD/VACUUM Geometry Optimization" ) B3LYP_def2_QZVPD_VACUUM_Single_Point = "B3LYP/def2-QZVPD/VACUUM Single Point" B3LYP_def2_QZVPD_VACUUM_Transition_State_Geometry_Optimization = ( "B3LYP/def2-QZVPD/VACUUM Transition State Geometry Optimization" ) B3LYP_def2_QZVPD_VACUUM_Unknown = "B3LYP/def2-QZVPD/VACUUM Unknown" B3LYP_def2_QZVPPD_PCM_Force = "B3LYP/def2-QZVPPD/PCM Force" B3LYP_def2_QZVPPD_PCM_Frequency_Analysis = ( "B3LYP/def2-QZVPPD/PCM Frequency Analysis" ) B3LYP_def2_QZVPPD_PCM_Frequency_Flattening_Geometry_Optimization = ( "B3LYP/def2-QZVPPD/PCM Frequency Flattening Geometry Optimization" ) B3LYP_def2_QZVPPD_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B3LYP/def2-QZVPPD/PCM Frequency Flattening Transition State Geometry Optimization" B3LYP_def2_QZVPPD_PCM_Geometry_Optimization = ( "B3LYP/def2-QZVPPD/PCM Geometry Optimization" ) B3LYP_def2_QZVPPD_PCM_Single_Point = "B3LYP/def2-QZVPPD/PCM Single Point" B3LYP_def2_QZVPPD_PCM_Transition_State_Geometry_Optimization = ( "B3LYP/def2-QZVPPD/PCM Transition State Geometry Optimization" ) B3LYP_def2_QZVPPD_PCM_Unknown = "B3LYP/def2-QZVPPD/PCM Unknown" B3LYP_def2_QZVPPD_SMD_Force = "B3LYP/def2-QZVPPD/SMD Force" B3LYP_def2_QZVPPD_SMD_Frequency_Analysis = ( "B3LYP/def2-QZVPPD/SMD Frequency Analysis" ) B3LYP_def2_QZVPPD_SMD_Frequency_Flattening_Geometry_Optimization = ( "B3LYP/def2-QZVPPD/SMD Frequency Flattening Geometry Optimization" ) B3LYP_def2_QZVPPD_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "B3LYP/def2-QZVPPD/SMD Frequency Flattening Transition State Geometry Optimization" B3LYP_def2_QZVPPD_SMD_Geometry_Optimization = ( "B3LYP/def2-QZVPPD/SMD Geometry Optimization" ) B3LYP_def2_QZVPPD_SMD_Single_Point = "B3LYP/def2-QZVPPD/SMD Single Point" B3LYP_def2_QZVPPD_SMD_Transition_State_Geometry_Optimization = ( "B3LYP/def2-QZVPPD/SMD Transition State Geometry Optimization" ) B3LYP_def2_QZVPPD_SMD_Unknown = "B3LYP/def2-QZVPPD/SMD Unknown" B3LYP_def2_QZVPPD_VACUUM_Force = "B3LYP/def2-QZVPPD/VACUUM Force" B3LYP_def2_QZVPPD_VACUUM_Frequency_Analysis = ( "B3LYP/def2-QZVPPD/VACUUM Frequency Analysis" ) B3LYP_def2_QZVPPD_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "B3LYP/def2-QZVPPD/VACUUM Frequency Flattening Geometry Optimization" ) B3LYP_def2_QZVPPD_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B3LYP/def2-QZVPPD/VACUUM Frequency Flattening Transition State Geometry Optimization" B3LYP_def2_QZVPPD_VACUUM_Geometry_Optimization = ( "B3LYP/def2-QZVPPD/VACUUM Geometry Optimization" ) B3LYP_def2_QZVPPD_VACUUM_Single_Point = "B3LYP/def2-QZVPPD/VACUUM Single Point" B3LYP_def2_QZVPPD_VACUUM_Transition_State_Geometry_Optimization = ( "B3LYP/def2-QZVPPD/VACUUM Transition State Geometry Optimization" ) B3LYP_def2_QZVPPD_VACUUM_Unknown = "B3LYP/def2-QZVPPD/VACUUM Unknown" B3LYP_def2_SVPD_PCM_Force = "B3LYP/def2-SVPD/PCM Force" B3LYP_def2_SVPD_PCM_Frequency_Analysis = "B3LYP/def2-SVPD/PCM Frequency Analysis" B3LYP_def2_SVPD_PCM_Frequency_Flattening_Geometry_Optimization = ( "B3LYP/def2-SVPD/PCM Frequency Flattening Geometry Optimization" ) B3LYP_def2_SVPD_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B3LYP/def2-SVPD/PCM Frequency Flattening Transition State Geometry Optimization" B3LYP_def2_SVPD_PCM_Geometry_Optimization = ( "B3LYP/def2-SVPD/PCM Geometry Optimization" ) B3LYP_def2_SVPD_PCM_Single_Point = "B3LYP/def2-SVPD/PCM Single Point" B3LYP_def2_SVPD_PCM_Transition_State_Geometry_Optimization = ( "B3LYP/def2-SVPD/PCM Transition State Geometry Optimization" ) B3LYP_def2_SVPD_PCM_Unknown = "B3LYP/def2-SVPD/PCM Unknown" B3LYP_def2_SVPD_SMD_Force = "B3LYP/def2-SVPD/SMD Force" B3LYP_def2_SVPD_SMD_Frequency_Analysis = "B3LYP/def2-SVPD/SMD Frequency Analysis" B3LYP_def2_SVPD_SMD_Frequency_Flattening_Geometry_Optimization = ( "B3LYP/def2-SVPD/SMD Frequency Flattening Geometry Optimization" ) B3LYP_def2_SVPD_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "B3LYP/def2-SVPD/SMD Frequency Flattening Transition State Geometry Optimization" B3LYP_def2_SVPD_SMD_Geometry_Optimization = ( "B3LYP/def2-SVPD/SMD Geometry Optimization" ) B3LYP_def2_SVPD_SMD_Single_Point = "B3LYP/def2-SVPD/SMD Single Point" B3LYP_def2_SVPD_SMD_Transition_State_Geometry_Optimization = ( "B3LYP/def2-SVPD/SMD Transition State Geometry Optimization" ) B3LYP_def2_SVPD_SMD_Unknown = "B3LYP/def2-SVPD/SMD Unknown" B3LYP_def2_SVPD_VACUUM_Force = "B3LYP/def2-SVPD/VACUUM Force" B3LYP_def2_SVPD_VACUUM_Frequency_Analysis = ( "B3LYP/def2-SVPD/VACUUM Frequency Analysis" ) B3LYP_def2_SVPD_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "B3LYP/def2-SVPD/VACUUM Frequency Flattening Geometry Optimization" ) B3LYP_def2_SVPD_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B3LYP/def2-SVPD/VACUUM Frequency Flattening Transition State Geometry Optimization" B3LYP_def2_SVPD_VACUUM_Geometry_Optimization = ( "B3LYP/def2-SVPD/VACUUM Geometry Optimization" ) B3LYP_def2_SVPD_VACUUM_Single_Point = "B3LYP/def2-SVPD/VACUUM Single Point" B3LYP_def2_SVPD_VACUUM_Transition_State_Geometry_Optimization = ( "B3LYP/def2-SVPD/VACUUM Transition State Geometry Optimization" ) B3LYP_def2_SVPD_VACUUM_Unknown = "B3LYP/def2-SVPD/VACUUM Unknown" B3LYP_def2_TZVPD_PCM_Force = "B3LYP/def2-TZVPD/PCM Force" B3LYP_def2_TZVPD_PCM_Frequency_Analysis = "B3LYP/def2-TZVPD/PCM Frequency Analysis" B3LYP_def2_TZVPD_PCM_Frequency_Flattening_Geometry_Optimization = ( "B3LYP/def2-TZVPD/PCM Frequency Flattening Geometry Optimization" ) B3LYP_def2_TZVPD_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B3LYP/def2-TZVPD/PCM Frequency Flattening Transition State Geometry Optimization" B3LYP_def2_TZVPD_PCM_Geometry_Optimization = ( "B3LYP/def2-TZVPD/PCM Geometry Optimization" ) B3LYP_def2_TZVPD_PCM_Single_Point = "B3LYP/def2-TZVPD/PCM Single Point" B3LYP_def2_TZVPD_PCM_Transition_State_Geometry_Optimization = ( "B3LYP/def2-TZVPD/PCM Transition State Geometry Optimization" ) B3LYP_def2_TZVPD_PCM_Unknown = "B3LYP/def2-TZVPD/PCM Unknown" B3LYP_def2_TZVPD_SMD_Force = "B3LYP/def2-TZVPD/SMD Force" B3LYP_def2_TZVPD_SMD_Frequency_Analysis = "B3LYP/def2-TZVPD/SMD Frequency Analysis" B3LYP_def2_TZVPD_SMD_Frequency_Flattening_Geometry_Optimization = ( "B3LYP/def2-TZVPD/SMD Frequency Flattening Geometry Optimization" ) B3LYP_def2_TZVPD_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "B3LYP/def2-TZVPD/SMD Frequency Flattening Transition State Geometry Optimization" B3LYP_def2_TZVPD_SMD_Geometry_Optimization = ( "B3LYP/def2-TZVPD/SMD Geometry Optimization" ) B3LYP_def2_TZVPD_SMD_Single_Point = "B3LYP/def2-TZVPD/SMD Single Point" B3LYP_def2_TZVPD_SMD_Transition_State_Geometry_Optimization = ( "B3LYP/def2-TZVPD/SMD Transition State Geometry Optimization" ) B3LYP_def2_TZVPD_SMD_Unknown = "B3LYP/def2-TZVPD/SMD Unknown" B3LYP_def2_TZVPD_VACUUM_Force = "B3LYP/def2-TZVPD/VACUUM Force" B3LYP_def2_TZVPD_VACUUM_Frequency_Analysis = ( "B3LYP/def2-TZVPD/VACUUM Frequency Analysis" ) B3LYP_def2_TZVPD_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "B3LYP/def2-TZVPD/VACUUM Frequency Flattening Geometry Optimization" ) B3LYP_def2_TZVPD_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B3LYP/def2-TZVPD/VACUUM Frequency Flattening Transition State Geometry Optimization" B3LYP_def2_TZVPD_VACUUM_Geometry_Optimization = ( "B3LYP/def2-TZVPD/VACUUM Geometry Optimization" ) B3LYP_def2_TZVPD_VACUUM_Single_Point = "B3LYP/def2-TZVPD/VACUUM Single Point" B3LYP_def2_TZVPD_VACUUM_Transition_State_Geometry_Optimization = ( "B3LYP/def2-TZVPD/VACUUM Transition State Geometry Optimization" ) B3LYP_def2_TZVPD_VACUUM_Unknown = "B3LYP/def2-TZVPD/VACUUM Unknown" B3LYP_def2_TZVPPD_PCM_Force = "B3LYP/def2-TZVPPD/PCM Force" B3LYP_def2_TZVPPD_PCM_Frequency_Analysis = ( "B3LYP/def2-TZVPPD/PCM Frequency Analysis" ) B3LYP_def2_TZVPPD_PCM_Frequency_Flattening_Geometry_Optimization = ( "B3LYP/def2-TZVPPD/PCM Frequency Flattening Geometry Optimization" ) B3LYP_def2_TZVPPD_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B3LYP/def2-TZVPPD/PCM Frequency Flattening Transition State Geometry Optimization" B3LYP_def2_TZVPPD_PCM_Geometry_Optimization = ( "B3LYP/def2-TZVPPD/PCM Geometry Optimization" ) B3LYP_def2_TZVPPD_PCM_Single_Point = "B3LYP/def2-TZVPPD/PCM Single Point" B3LYP_def2_TZVPPD_PCM_Transition_State_Geometry_Optimization = ( "B3LYP/def2-TZVPPD/PCM Transition State Geometry Optimization" ) B3LYP_def2_TZVPPD_PCM_Unknown = "B3LYP/def2-TZVPPD/PCM Unknown" B3LYP_def2_TZVPPD_SMD_Force = "B3LYP/def2-TZVPPD/SMD Force" B3LYP_def2_TZVPPD_SMD_Frequency_Analysis = ( "B3LYP/def2-TZVPPD/SMD Frequency Analysis" ) B3LYP_def2_TZVPPD_SMD_Frequency_Flattening_Geometry_Optimization = ( "B3LYP/def2-TZVPPD/SMD Frequency Flattening Geometry Optimization" ) B3LYP_def2_TZVPPD_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "B3LYP/def2-TZVPPD/SMD Frequency Flattening Transition State Geometry Optimization" B3LYP_def2_TZVPPD_SMD_Geometry_Optimization = ( "B3LYP/def2-TZVPPD/SMD Geometry Optimization" ) B3LYP_def2_TZVPPD_SMD_Single_Point = "B3LYP/def2-TZVPPD/SMD Single Point" B3LYP_def2_TZVPPD_SMD_Transition_State_Geometry_Optimization = ( "B3LYP/def2-TZVPPD/SMD Transition State Geometry Optimization" ) B3LYP_def2_TZVPPD_SMD_Unknown = "B3LYP/def2-TZVPPD/SMD Unknown" B3LYP_def2_TZVPPD_VACUUM_Force = "B3LYP/def2-TZVPPD/VACUUM Force" B3LYP_def2_TZVPPD_VACUUM_Frequency_Analysis = ( "B3LYP/def2-TZVPPD/VACUUM Frequency Analysis" ) B3LYP_def2_TZVPPD_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "B3LYP/def2-TZVPPD/VACUUM Frequency Flattening Geometry Optimization" ) B3LYP_def2_TZVPPD_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B3LYP/def2-TZVPPD/VACUUM Frequency Flattening Transition State Geometry Optimization" B3LYP_def2_TZVPPD_VACUUM_Geometry_Optimization = ( "B3LYP/def2-TZVPPD/VACUUM Geometry Optimization" ) B3LYP_def2_TZVPPD_VACUUM_Single_Point = "B3LYP/def2-TZVPPD/VACUUM Single Point" B3LYP_def2_TZVPPD_VACUUM_Transition_State_Geometry_Optimization = ( "B3LYP/def2-TZVPPD/VACUUM Transition State Geometry Optimization" ) B3LYP_def2_TZVPPD_VACUUM_Unknown = "B3LYP/def2-TZVPPD/VACUUM Unknown" B3LYP_def2_TZVPP_PCM_Force = "B3LYP/def2-TZVPP/PCM Force" B3LYP_def2_TZVPP_PCM_Frequency_Analysis = "B3LYP/def2-TZVPP/PCM Frequency Analysis" B3LYP_def2_TZVPP_PCM_Frequency_Flattening_Geometry_Optimization = ( "B3LYP/def2-TZVPP/PCM Frequency Flattening Geometry Optimization" ) B3LYP_def2_TZVPP_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B3LYP/def2-TZVPP/PCM Frequency Flattening Transition State Geometry Optimization" B3LYP_def2_TZVPP_PCM_Geometry_Optimization = ( "B3LYP/def2-TZVPP/PCM Geometry Optimization" ) B3LYP_def2_TZVPP_PCM_Single_Point = "B3LYP/def2-TZVPP/PCM Single Point" B3LYP_def2_TZVPP_PCM_Transition_State_Geometry_Optimization = ( "B3LYP/def2-TZVPP/PCM Transition State Geometry Optimization" ) B3LYP_def2_TZVPP_PCM_Unknown = "B3LYP/def2-TZVPP/PCM Unknown" B3LYP_def2_TZVPP_SMD_Force = "B3LYP/def2-TZVPP/SMD Force" B3LYP_def2_TZVPP_SMD_Frequency_Analysis = "B3LYP/def2-TZVPP/SMD Frequency Analysis" B3LYP_def2_TZVPP_SMD_Frequency_Flattening_Geometry_Optimization = ( "B3LYP/def2-TZVPP/SMD Frequency Flattening Geometry Optimization" ) B3LYP_def2_TZVPP_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "B3LYP/def2-TZVPP/SMD Frequency Flattening Transition State Geometry Optimization" B3LYP_def2_TZVPP_SMD_Geometry_Optimization = ( "B3LYP/def2-TZVPP/SMD Geometry Optimization" ) B3LYP_def2_TZVPP_SMD_Single_Point = "B3LYP/def2-TZVPP/SMD Single Point" B3LYP_def2_TZVPP_SMD_Transition_State_Geometry_Optimization = ( "B3LYP/def2-TZVPP/SMD Transition State Geometry Optimization" ) B3LYP_def2_TZVPP_SMD_Unknown = "B3LYP/def2-TZVPP/SMD Unknown" B3LYP_def2_TZVPP_VACUUM_Force = "B3LYP/def2-TZVPP/VACUUM Force" B3LYP_def2_TZVPP_VACUUM_Frequency_Analysis = ( "B3LYP/def2-TZVPP/VACUUM Frequency Analysis" ) B3LYP_def2_TZVPP_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "B3LYP/def2-TZVPP/VACUUM Frequency Flattening Geometry Optimization" ) B3LYP_def2_TZVPP_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B3LYP/def2-TZVPP/VACUUM Frequency Flattening Transition State Geometry Optimization" B3LYP_def2_TZVPP_VACUUM_Geometry_Optimization = ( "B3LYP/def2-TZVPP/VACUUM Geometry Optimization" ) B3LYP_def2_TZVPP_VACUUM_Single_Point = "B3LYP/def2-TZVPP/VACUUM Single Point" B3LYP_def2_TZVPP_VACUUM_Transition_State_Geometry_Optimization = ( "B3LYP/def2-TZVPP/VACUUM Transition State Geometry Optimization" ) B3LYP_def2_TZVPP_VACUUM_Unknown = "B3LYP/def2-TZVPP/VACUUM Unknown" B3LYP_def2_TZVP_PCM_Force = "B3LYP/def2-TZVP/PCM Force" B3LYP_def2_TZVP_PCM_Frequency_Analysis = "B3LYP/def2-TZVP/PCM Frequency Analysis" B3LYP_def2_TZVP_PCM_Frequency_Flattening_Geometry_Optimization = ( "B3LYP/def2-TZVP/PCM Frequency Flattening Geometry Optimization" ) B3LYP_def2_TZVP_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B3LYP/def2-TZVP/PCM Frequency Flattening Transition State Geometry Optimization" B3LYP_def2_TZVP_PCM_Geometry_Optimization = ( "B3LYP/def2-TZVP/PCM Geometry Optimization" ) B3LYP_def2_TZVP_PCM_Single_Point = "B3LYP/def2-TZVP/PCM Single Point" B3LYP_def2_TZVP_PCM_Transition_State_Geometry_Optimization = ( "B3LYP/def2-TZVP/PCM Transition State Geometry Optimization" ) B3LYP_def2_TZVP_PCM_Unknown = "B3LYP/def2-TZVP/PCM Unknown" B3LYP_def2_TZVP_SMD_Force = "B3LYP/def2-TZVP/SMD Force" B3LYP_def2_TZVP_SMD_Frequency_Analysis = "B3LYP/def2-TZVP/SMD Frequency Analysis" B3LYP_def2_TZVP_SMD_Frequency_Flattening_Geometry_Optimization = ( "B3LYP/def2-TZVP/SMD Frequency Flattening Geometry Optimization" ) B3LYP_def2_TZVP_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "B3LYP/def2-TZVP/SMD Frequency Flattening Transition State Geometry Optimization" B3LYP_def2_TZVP_SMD_Geometry_Optimization = ( "B3LYP/def2-TZVP/SMD Geometry Optimization" ) B3LYP_def2_TZVP_SMD_Single_Point = "B3LYP/def2-TZVP/SMD Single Point" B3LYP_def2_TZVP_SMD_Transition_State_Geometry_Optimization = ( "B3LYP/def2-TZVP/SMD Transition State Geometry Optimization" ) B3LYP_def2_TZVP_SMD_Unknown = "B3LYP/def2-TZVP/SMD Unknown" B3LYP_def2_TZVP_VACUUM_Force = "B3LYP/def2-TZVP/VACUUM Force" B3LYP_def2_TZVP_VACUUM_Frequency_Analysis = ( "B3LYP/def2-TZVP/VACUUM Frequency Analysis" ) B3LYP_def2_TZVP_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "B3LYP/def2-TZVP/VACUUM Frequency Flattening Geometry Optimization" ) B3LYP_def2_TZVP_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B3LYP/def2-TZVP/VACUUM Frequency Flattening Transition State Geometry Optimization" B3LYP_def2_TZVP_VACUUM_Geometry_Optimization = ( "B3LYP/def2-TZVP/VACUUM Geometry Optimization" ) B3LYP_def2_TZVP_VACUUM_Single_Point = "B3LYP/def2-TZVP/VACUUM Single Point" B3LYP_def2_TZVP_VACUUM_Transition_State_Geometry_Optimization = ( "B3LYP/def2-TZVP/VACUUM Transition State Geometry Optimization" ) B3LYP_def2_TZVP_VACUUM_Unknown = "B3LYP/def2-TZVP/VACUUM Unknown" B97M_V_6_31g_d_PCM_Force = "B97M-V/6-31g*/PCM Force" B97M_V_6_31g_d_PCM_Frequency_Analysis = "B97M-V/6-31g*/PCM Frequency Analysis" B97M_V_6_31g_d_PCM_Frequency_Flattening_Geometry_Optimization = ( "B97M-V/6-31g*/PCM Frequency Flattening Geometry Optimization" ) B97M_V_6_31g_d_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = ( "B97M-V/6-31g*/PCM Frequency Flattening Transition State Geometry Optimization" ) B97M_V_6_31g_d_PCM_Geometry_Optimization = "B97M-V/6-31g*/PCM Geometry Optimization" B97M_V_6_31g_d_PCM_Single_Point = "B97M-V/6-31g*/PCM Single Point" B97M_V_6_31g_d_PCM_Transition_State_Geometry_Optimization = ( "B97M-V/6-31g*/PCM Transition State Geometry Optimization" ) B97M_V_6_31g_d_PCM_Unknown = "B97M-V/6-31g*/PCM Unknown" B97M_V_6_31g_d_SMD_Force = "B97M-V/6-31g*/SMD Force" B97M_V_6_31g_d_SMD_Frequency_Analysis = "B97M-V/6-31g*/SMD Frequency Analysis" B97M_V_6_31g_d_SMD_Frequency_Flattening_Geometry_Optimization = ( "B97M-V/6-31g*/SMD Frequency Flattening Geometry Optimization" ) B97M_V_6_31g_d_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = ( "B97M-V/6-31g*/SMD Frequency Flattening Transition State Geometry Optimization" ) B97M_V_6_31g_d_SMD_Geometry_Optimization = "B97M-V/6-31g*/SMD Geometry Optimization" B97M_V_6_31g_d_SMD_Single_Point = "B97M-V/6-31g*/SMD Single Point" B97M_V_6_31g_d_SMD_Transition_State_Geometry_Optimization = ( "B97M-V/6-31g*/SMD Transition State Geometry Optimization" ) B97M_V_6_31g_d_SMD_Unknown = "B97M-V/6-31g*/SMD Unknown" B97M_V_6_31g_d_VACUUM_Force = "B97M-V/6-31g*/VACUUM Force" B97M_V_6_31g_d_VACUUM_Frequency_Analysis = "B97M-V/6-31g*/VACUUM Frequency Analysis" B97M_V_6_31g_d_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "B97M-V/6-31g*/VACUUM Frequency Flattening Geometry Optimization" ) B97M_V_6_31g_d_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97M-V/6-31g*/VACUUM Frequency Flattening Transition State Geometry Optimization" B97M_V_6_31g_d_VACUUM_Geometry_Optimization = ( "B97M-V/6-31g*/VACUUM Geometry Optimization" ) B97M_V_6_31g_d_VACUUM_Single_Point = "B97M-V/6-31g*/VACUUM Single Point" B97M_V_6_31g_d_VACUUM_Transition_State_Geometry_Optimization = ( "B97M-V/6-31g*/VACUUM Transition State Geometry Optimization" ) B97M_V_6_31g_d_VACUUM_Unknown = "B97M-V/6-31g*/VACUUM Unknown" B97M_V_def2_QZVPD_PCM_Force = "B97M-V/def2-QZVPD/PCM Force" B97M_V_def2_QZVPD_PCM_Frequency_Analysis = ( "B97M-V/def2-QZVPD/PCM Frequency Analysis" ) B97M_V_def2_QZVPD_PCM_Frequency_Flattening_Geometry_Optimization = ( "B97M-V/def2-QZVPD/PCM Frequency Flattening Geometry Optimization" ) B97M_V_def2_QZVPD_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97M-V/def2-QZVPD/PCM Frequency Flattening Transition State Geometry Optimization" B97M_V_def2_QZVPD_PCM_Geometry_Optimization = ( "B97M-V/def2-QZVPD/PCM Geometry Optimization" ) B97M_V_def2_QZVPD_PCM_Single_Point = "B97M-V/def2-QZVPD/PCM Single Point" B97M_V_def2_QZVPD_PCM_Transition_State_Geometry_Optimization = ( "B97M-V/def2-QZVPD/PCM Transition State Geometry Optimization" ) B97M_V_def2_QZVPD_PCM_Unknown = "B97M-V/def2-QZVPD/PCM Unknown" B97M_V_def2_QZVPD_SMD_Force = "B97M-V/def2-QZVPD/SMD Force" B97M_V_def2_QZVPD_SMD_Frequency_Analysis = ( "B97M-V/def2-QZVPD/SMD Frequency Analysis" ) B97M_V_def2_QZVPD_SMD_Frequency_Flattening_Geometry_Optimization = ( "B97M-V/def2-QZVPD/SMD Frequency Flattening Geometry Optimization" ) B97M_V_def2_QZVPD_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97M-V/def2-QZVPD/SMD Frequency Flattening Transition State Geometry Optimization" B97M_V_def2_QZVPD_SMD_Geometry_Optimization = ( "B97M-V/def2-QZVPD/SMD Geometry Optimization" ) B97M_V_def2_QZVPD_SMD_Single_Point = "B97M-V/def2-QZVPD/SMD Single Point" B97M_V_def2_QZVPD_SMD_Transition_State_Geometry_Optimization = ( "B97M-V/def2-QZVPD/SMD Transition State Geometry Optimization" ) B97M_V_def2_QZVPD_SMD_Unknown = "B97M-V/def2-QZVPD/SMD Unknown" B97M_V_def2_QZVPD_VACUUM_Force = "B97M-V/def2-QZVPD/VACUUM Force" B97M_V_def2_QZVPD_VACUUM_Frequency_Analysis = ( "B97M-V/def2-QZVPD/VACUUM Frequency Analysis" ) B97M_V_def2_QZVPD_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "B97M-V/def2-QZVPD/VACUUM Frequency Flattening Geometry Optimization" ) B97M_V_def2_QZVPD_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97M-V/def2-QZVPD/VACUUM Frequency Flattening Transition State Geometry Optimization" B97M_V_def2_QZVPD_VACUUM_Geometry_Optimization = ( "B97M-V/def2-QZVPD/VACUUM Geometry Optimization" ) B97M_V_def2_QZVPD_VACUUM_Single_Point = "B97M-V/def2-QZVPD/VACUUM Single Point" B97M_V_def2_QZVPD_VACUUM_Transition_State_Geometry_Optimization = ( "B97M-V/def2-QZVPD/VACUUM Transition State Geometry Optimization" ) B97M_V_def2_QZVPD_VACUUM_Unknown = "B97M-V/def2-QZVPD/VACUUM Unknown" B97M_V_def2_QZVPPD_PCM_Force = "B97M-V/def2-QZVPPD/PCM Force" B97M_V_def2_QZVPPD_PCM_Frequency_Analysis = ( "B97M-V/def2-QZVPPD/PCM Frequency Analysis" ) B97M_V_def2_QZVPPD_PCM_Frequency_Flattening_Geometry_Optimization = ( "B97M-V/def2-QZVPPD/PCM Frequency Flattening Geometry Optimization" ) B97M_V_def2_QZVPPD_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97M-V/def2-QZVPPD/PCM Frequency Flattening Transition State Geometry Optimization" B97M_V_def2_QZVPPD_PCM_Geometry_Optimization = ( "B97M-V/def2-QZVPPD/PCM Geometry Optimization" ) B97M_V_def2_QZVPPD_PCM_Single_Point = "B97M-V/def2-QZVPPD/PCM Single Point" B97M_V_def2_QZVPPD_PCM_Transition_State_Geometry_Optimization = ( "B97M-V/def2-QZVPPD/PCM Transition State Geometry Optimization" ) B97M_V_def2_QZVPPD_PCM_Unknown = "B97M-V/def2-QZVPPD/PCM Unknown" B97M_V_def2_QZVPPD_SMD_Force = "B97M-V/def2-QZVPPD/SMD Force" B97M_V_def2_QZVPPD_SMD_Frequency_Analysis = ( "B97M-V/def2-QZVPPD/SMD Frequency Analysis" ) B97M_V_def2_QZVPPD_SMD_Frequency_Flattening_Geometry_Optimization = ( "B97M-V/def2-QZVPPD/SMD Frequency Flattening Geometry Optimization" ) B97M_V_def2_QZVPPD_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97M-V/def2-QZVPPD/SMD Frequency Flattening Transition State Geometry Optimization" B97M_V_def2_QZVPPD_SMD_Geometry_Optimization = ( "B97M-V/def2-QZVPPD/SMD Geometry Optimization" ) B97M_V_def2_QZVPPD_SMD_Single_Point = "B97M-V/def2-QZVPPD/SMD Single Point" B97M_V_def2_QZVPPD_SMD_Transition_State_Geometry_Optimization = ( "B97M-V/def2-QZVPPD/SMD Transition State Geometry Optimization" ) B97M_V_def2_QZVPPD_SMD_Unknown = "B97M-V/def2-QZVPPD/SMD Unknown" B97M_V_def2_QZVPPD_VACUUM_Force = "B97M-V/def2-QZVPPD/VACUUM Force" B97M_V_def2_QZVPPD_VACUUM_Frequency_Analysis = ( "B97M-V/def2-QZVPPD/VACUUM Frequency Analysis" ) B97M_V_def2_QZVPPD_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "B97M-V/def2-QZVPPD/VACUUM Frequency Flattening Geometry Optimization" ) B97M_V_def2_QZVPPD_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97M-V/def2-QZVPPD/VACUUM Frequency Flattening Transition State Geometry Optimization" B97M_V_def2_QZVPPD_VACUUM_Geometry_Optimization = ( "B97M-V/def2-QZVPPD/VACUUM Geometry Optimization" ) B97M_V_def2_QZVPPD_VACUUM_Single_Point = "B97M-V/def2-QZVPPD/VACUUM Single Point" B97M_V_def2_QZVPPD_VACUUM_Transition_State_Geometry_Optimization = ( "B97M-V/def2-QZVPPD/VACUUM Transition State Geometry Optimization" ) B97M_V_def2_QZVPPD_VACUUM_Unknown = "B97M-V/def2-QZVPPD/VACUUM Unknown" B97M_V_def2_SVPD_PCM_Force = "B97M-V/def2-SVPD/PCM Force" B97M_V_def2_SVPD_PCM_Frequency_Analysis = "B97M-V/def2-SVPD/PCM Frequency Analysis" B97M_V_def2_SVPD_PCM_Frequency_Flattening_Geometry_Optimization = ( "B97M-V/def2-SVPD/PCM Frequency Flattening Geometry Optimization" ) B97M_V_def2_SVPD_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97M-V/def2-SVPD/PCM Frequency Flattening Transition State Geometry Optimization" B97M_V_def2_SVPD_PCM_Geometry_Optimization = ( "B97M-V/def2-SVPD/PCM Geometry Optimization" ) B97M_V_def2_SVPD_PCM_Single_Point = "B97M-V/def2-SVPD/PCM Single Point" B97M_V_def2_SVPD_PCM_Transition_State_Geometry_Optimization = ( "B97M-V/def2-SVPD/PCM Transition State Geometry Optimization" ) B97M_V_def2_SVPD_PCM_Unknown = "B97M-V/def2-SVPD/PCM Unknown" B97M_V_def2_SVPD_SMD_Force = "B97M-V/def2-SVPD/SMD Force" B97M_V_def2_SVPD_SMD_Frequency_Analysis = "B97M-V/def2-SVPD/SMD Frequency Analysis" B97M_V_def2_SVPD_SMD_Frequency_Flattening_Geometry_Optimization = ( "B97M-V/def2-SVPD/SMD Frequency Flattening Geometry Optimization" ) B97M_V_def2_SVPD_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97M-V/def2-SVPD/SMD Frequency Flattening Transition State Geometry Optimization" B97M_V_def2_SVPD_SMD_Geometry_Optimization = ( "B97M-V/def2-SVPD/SMD Geometry Optimization" ) B97M_V_def2_SVPD_SMD_Single_Point = "B97M-V/def2-SVPD/SMD Single Point" B97M_V_def2_SVPD_SMD_Transition_State_Geometry_Optimization = ( "B97M-V/def2-SVPD/SMD Transition State Geometry Optimization" ) B97M_V_def2_SVPD_SMD_Unknown = "B97M-V/def2-SVPD/SMD Unknown" B97M_V_def2_SVPD_VACUUM_Force = "B97M-V/def2-SVPD/VACUUM Force" B97M_V_def2_SVPD_VACUUM_Frequency_Analysis = ( "B97M-V/def2-SVPD/VACUUM Frequency Analysis" ) B97M_V_def2_SVPD_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "B97M-V/def2-SVPD/VACUUM Frequency Flattening Geometry Optimization" ) B97M_V_def2_SVPD_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97M-V/def2-SVPD/VACUUM Frequency Flattening Transition State Geometry Optimization" B97M_V_def2_SVPD_VACUUM_Geometry_Optimization = ( "B97M-V/def2-SVPD/VACUUM Geometry Optimization" ) B97M_V_def2_SVPD_VACUUM_Single_Point = "B97M-V/def2-SVPD/VACUUM Single Point" B97M_V_def2_SVPD_VACUUM_Transition_State_Geometry_Optimization = ( "B97M-V/def2-SVPD/VACUUM Transition State Geometry Optimization" ) B97M_V_def2_SVPD_VACUUM_Unknown = "B97M-V/def2-SVPD/VACUUM Unknown" B97M_V_def2_TZVPD_PCM_Force = "B97M-V/def2-TZVPD/PCM Force" B97M_V_def2_TZVPD_PCM_Frequency_Analysis = ( "B97M-V/def2-TZVPD/PCM Frequency Analysis" ) B97M_V_def2_TZVPD_PCM_Frequency_Flattening_Geometry_Optimization = ( "B97M-V/def2-TZVPD/PCM Frequency Flattening Geometry Optimization" ) B97M_V_def2_TZVPD_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97M-V/def2-TZVPD/PCM Frequency Flattening Transition State Geometry Optimization" B97M_V_def2_TZVPD_PCM_Geometry_Optimization = ( "B97M-V/def2-TZVPD/PCM Geometry Optimization" ) B97M_V_def2_TZVPD_PCM_Single_Point = "B97M-V/def2-TZVPD/PCM Single Point" B97M_V_def2_TZVPD_PCM_Transition_State_Geometry_Optimization = ( "B97M-V/def2-TZVPD/PCM Transition State Geometry Optimization" ) B97M_V_def2_TZVPD_PCM_Unknown = "B97M-V/def2-TZVPD/PCM Unknown" B97M_V_def2_TZVPD_SMD_Force = "B97M-V/def2-TZVPD/SMD Force" B97M_V_def2_TZVPD_SMD_Frequency_Analysis = ( "B97M-V/def2-TZVPD/SMD Frequency Analysis" ) B97M_V_def2_TZVPD_SMD_Frequency_Flattening_Geometry_Optimization = ( "B97M-V/def2-TZVPD/SMD Frequency Flattening Geometry Optimization" ) B97M_V_def2_TZVPD_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97M-V/def2-TZVPD/SMD Frequency Flattening Transition State Geometry Optimization" B97M_V_def2_TZVPD_SMD_Geometry_Optimization = ( "B97M-V/def2-TZVPD/SMD Geometry Optimization" ) B97M_V_def2_TZVPD_SMD_Single_Point = "B97M-V/def2-TZVPD/SMD Single Point" B97M_V_def2_TZVPD_SMD_Transition_State_Geometry_Optimization = ( "B97M-V/def2-TZVPD/SMD Transition State Geometry Optimization" ) B97M_V_def2_TZVPD_SMD_Unknown = "B97M-V/def2-TZVPD/SMD Unknown" B97M_V_def2_TZVPD_VACUUM_Force = "B97M-V/def2-TZVPD/VACUUM Force" B97M_V_def2_TZVPD_VACUUM_Frequency_Analysis = ( "B97M-V/def2-TZVPD/VACUUM Frequency Analysis" ) B97M_V_def2_TZVPD_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "B97M-V/def2-TZVPD/VACUUM Frequency Flattening Geometry Optimization" ) B97M_V_def2_TZVPD_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97M-V/def2-TZVPD/VACUUM Frequency Flattening Transition State Geometry Optimization" B97M_V_def2_TZVPD_VACUUM_Geometry_Optimization = ( "B97M-V/def2-TZVPD/VACUUM Geometry Optimization" ) B97M_V_def2_TZVPD_VACUUM_Single_Point = "B97M-V/def2-TZVPD/VACUUM Single Point" B97M_V_def2_TZVPD_VACUUM_Transition_State_Geometry_Optimization = ( "B97M-V/def2-TZVPD/VACUUM Transition State Geometry Optimization" ) B97M_V_def2_TZVPD_VACUUM_Unknown = "B97M-V/def2-TZVPD/VACUUM Unknown" B97M_V_def2_TZVPPD_PCM_Force = "B97M-V/def2-TZVPPD/PCM Force" B97M_V_def2_TZVPPD_PCM_Frequency_Analysis = ( "B97M-V/def2-TZVPPD/PCM Frequency Analysis" ) B97M_V_def2_TZVPPD_PCM_Frequency_Flattening_Geometry_Optimization = ( "B97M-V/def2-TZVPPD/PCM Frequency Flattening Geometry Optimization" ) B97M_V_def2_TZVPPD_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97M-V/def2-TZVPPD/PCM Frequency Flattening Transition State Geometry Optimization" B97M_V_def2_TZVPPD_PCM_Geometry_Optimization = ( "B97M-V/def2-TZVPPD/PCM Geometry Optimization" ) B97M_V_def2_TZVPPD_PCM_Single_Point = "B97M-V/def2-TZVPPD/PCM Single Point" B97M_V_def2_TZVPPD_PCM_Transition_State_Geometry_Optimization = ( "B97M-V/def2-TZVPPD/PCM Transition State Geometry Optimization" ) B97M_V_def2_TZVPPD_PCM_Unknown = "B97M-V/def2-TZVPPD/PCM Unknown" B97M_V_def2_TZVPPD_SMD_Force = "B97M-V/def2-TZVPPD/SMD Force" B97M_V_def2_TZVPPD_SMD_Frequency_Analysis = ( "B97M-V/def2-TZVPPD/SMD Frequency Analysis" ) B97M_V_def2_TZVPPD_SMD_Frequency_Flattening_Geometry_Optimization = ( "B97M-V/def2-TZVPPD/SMD Frequency Flattening Geometry Optimization" ) B97M_V_def2_TZVPPD_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97M-V/def2-TZVPPD/SMD Frequency Flattening Transition State Geometry Optimization" B97M_V_def2_TZVPPD_SMD_Geometry_Optimization = ( "B97M-V/def2-TZVPPD/SMD Geometry Optimization" ) B97M_V_def2_TZVPPD_SMD_Single_Point = "B97M-V/def2-TZVPPD/SMD Single Point" B97M_V_def2_TZVPPD_SMD_Transition_State_Geometry_Optimization = ( "B97M-V/def2-TZVPPD/SMD Transition State Geometry Optimization" ) B97M_V_def2_TZVPPD_SMD_Unknown = "B97M-V/def2-TZVPPD/SMD Unknown" B97M_V_def2_TZVPPD_VACUUM_Force = "B97M-V/def2-TZVPPD/VACUUM Force" B97M_V_def2_TZVPPD_VACUUM_Frequency_Analysis = ( "B97M-V/def2-TZVPPD/VACUUM Frequency Analysis" ) B97M_V_def2_TZVPPD_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "B97M-V/def2-TZVPPD/VACUUM Frequency Flattening Geometry Optimization" ) B97M_V_def2_TZVPPD_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97M-V/def2-TZVPPD/VACUUM Frequency Flattening Transition State Geometry Optimization" B97M_V_def2_TZVPPD_VACUUM_Geometry_Optimization = ( "B97M-V/def2-TZVPPD/VACUUM Geometry Optimization" ) B97M_V_def2_TZVPPD_VACUUM_Single_Point = "B97M-V/def2-TZVPPD/VACUUM Single Point" B97M_V_def2_TZVPPD_VACUUM_Transition_State_Geometry_Optimization = ( "B97M-V/def2-TZVPPD/VACUUM Transition State Geometry Optimization" ) B97M_V_def2_TZVPPD_VACUUM_Unknown = "B97M-V/def2-TZVPPD/VACUUM Unknown" B97M_V_def2_TZVPP_PCM_Force = "B97M-V/def2-TZVPP/PCM Force" B97M_V_def2_TZVPP_PCM_Frequency_Analysis = ( "B97M-V/def2-TZVPP/PCM Frequency Analysis" ) B97M_V_def2_TZVPP_PCM_Frequency_Flattening_Geometry_Optimization = ( "B97M-V/def2-TZVPP/PCM Frequency Flattening Geometry Optimization" ) B97M_V_def2_TZVPP_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97M-V/def2-TZVPP/PCM Frequency Flattening Transition State Geometry Optimization" B97M_V_def2_TZVPP_PCM_Geometry_Optimization = ( "B97M-V/def2-TZVPP/PCM Geometry Optimization" ) B97M_V_def2_TZVPP_PCM_Single_Point = "B97M-V/def2-TZVPP/PCM Single Point" B97M_V_def2_TZVPP_PCM_Transition_State_Geometry_Optimization = ( "B97M-V/def2-TZVPP/PCM Transition State Geometry Optimization" ) B97M_V_def2_TZVPP_PCM_Unknown = "B97M-V/def2-TZVPP/PCM Unknown" B97M_V_def2_TZVPP_SMD_Force = "B97M-V/def2-TZVPP/SMD Force" B97M_V_def2_TZVPP_SMD_Frequency_Analysis = ( "B97M-V/def2-TZVPP/SMD Frequency Analysis" ) B97M_V_def2_TZVPP_SMD_Frequency_Flattening_Geometry_Optimization = ( "B97M-V/def2-TZVPP/SMD Frequency Flattening Geometry Optimization" ) B97M_V_def2_TZVPP_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97M-V/def2-TZVPP/SMD Frequency Flattening Transition State Geometry Optimization" B97M_V_def2_TZVPP_SMD_Geometry_Optimization = ( "B97M-V/def2-TZVPP/SMD Geometry Optimization" ) B97M_V_def2_TZVPP_SMD_Single_Point = "B97M-V/def2-TZVPP/SMD Single Point" B97M_V_def2_TZVPP_SMD_Transition_State_Geometry_Optimization = ( "B97M-V/def2-TZVPP/SMD Transition State Geometry Optimization" ) B97M_V_def2_TZVPP_SMD_Unknown = "B97M-V/def2-TZVPP/SMD Unknown" B97M_V_def2_TZVPP_VACUUM_Force = "B97M-V/def2-TZVPP/VACUUM Force" B97M_V_def2_TZVPP_VACUUM_Frequency_Analysis = ( "B97M-V/def2-TZVPP/VACUUM Frequency Analysis" ) B97M_V_def2_TZVPP_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "B97M-V/def2-TZVPP/VACUUM Frequency Flattening Geometry Optimization" ) B97M_V_def2_TZVPP_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97M-V/def2-TZVPP/VACUUM Frequency Flattening Transition State Geometry Optimization" B97M_V_def2_TZVPP_VACUUM_Geometry_Optimization = ( "B97M-V/def2-TZVPP/VACUUM Geometry Optimization" ) B97M_V_def2_TZVPP_VACUUM_Single_Point = "B97M-V/def2-TZVPP/VACUUM Single Point" B97M_V_def2_TZVPP_VACUUM_Transition_State_Geometry_Optimization = ( "B97M-V/def2-TZVPP/VACUUM Transition State Geometry Optimization" ) B97M_V_def2_TZVPP_VACUUM_Unknown = "B97M-V/def2-TZVPP/VACUUM Unknown" B97M_V_def2_TZVP_PCM_Force = "B97M-V/def2-TZVP/PCM Force" B97M_V_def2_TZVP_PCM_Frequency_Analysis = "B97M-V/def2-TZVP/PCM Frequency Analysis" B97M_V_def2_TZVP_PCM_Frequency_Flattening_Geometry_Optimization = ( "B97M-V/def2-TZVP/PCM Frequency Flattening Geometry Optimization" ) B97M_V_def2_TZVP_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97M-V/def2-TZVP/PCM Frequency Flattening Transition State Geometry Optimization" B97M_V_def2_TZVP_PCM_Geometry_Optimization = ( "B97M-V/def2-TZVP/PCM Geometry Optimization" ) B97M_V_def2_TZVP_PCM_Single_Point = "B97M-V/def2-TZVP/PCM Single Point" B97M_V_def2_TZVP_PCM_Transition_State_Geometry_Optimization = ( "B97M-V/def2-TZVP/PCM Transition State Geometry Optimization" ) B97M_V_def2_TZVP_PCM_Unknown = "B97M-V/def2-TZVP/PCM Unknown" B97M_V_def2_TZVP_SMD_Force = "B97M-V/def2-TZVP/SMD Force" B97M_V_def2_TZVP_SMD_Frequency_Analysis = "B97M-V/def2-TZVP/SMD Frequency Analysis" B97M_V_def2_TZVP_SMD_Frequency_Flattening_Geometry_Optimization = ( "B97M-V/def2-TZVP/SMD Frequency Flattening Geometry Optimization" ) B97M_V_def2_TZVP_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97M-V/def2-TZVP/SMD Frequency Flattening Transition State Geometry Optimization" B97M_V_def2_TZVP_SMD_Geometry_Optimization = ( "B97M-V/def2-TZVP/SMD Geometry Optimization" ) B97M_V_def2_TZVP_SMD_Single_Point = "B97M-V/def2-TZVP/SMD Single Point" B97M_V_def2_TZVP_SMD_Transition_State_Geometry_Optimization = ( "B97M-V/def2-TZVP/SMD Transition State Geometry Optimization" ) B97M_V_def2_TZVP_SMD_Unknown = "B97M-V/def2-TZVP/SMD Unknown" B97M_V_def2_TZVP_VACUUM_Force = "B97M-V/def2-TZVP/VACUUM Force" B97M_V_def2_TZVP_VACUUM_Frequency_Analysis = ( "B97M-V/def2-TZVP/VACUUM Frequency Analysis" ) B97M_V_def2_TZVP_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "B97M-V/def2-TZVP/VACUUM Frequency Flattening Geometry Optimization" ) B97M_V_def2_TZVP_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97M-V/def2-TZVP/VACUUM Frequency Flattening Transition State Geometry Optimization" B97M_V_def2_TZVP_VACUUM_Geometry_Optimization = ( "B97M-V/def2-TZVP/VACUUM Geometry Optimization" ) B97M_V_def2_TZVP_VACUUM_Single_Point = "B97M-V/def2-TZVP/VACUUM Single Point" B97M_V_def2_TZVP_VACUUM_Transition_State_Geometry_Optimization = ( "B97M-V/def2-TZVP/VACUUM Transition State Geometry Optimization" ) B97M_V_def2_TZVP_VACUUM_Unknown = "B97M-V/def2-TZVP/VACUUM Unknown" B97M_rV_6_31g_d_PCM_Force = "B97M-rV/6-31g*/PCM Force" B97M_rV_6_31g_d_PCM_Frequency_Analysis = "B97M-rV/6-31g*/PCM Frequency Analysis" B97M_rV_6_31g_d_PCM_Frequency_Flattening_Geometry_Optimization = ( "B97M-rV/6-31g*/PCM Frequency Flattening Geometry Optimization" ) B97M_rV_6_31g_d_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = ( "B97M-rV/6-31g*/PCM Frequency Flattening Transition State Geometry Optimization" ) B97M_rV_6_31g_d_PCM_Geometry_Optimization = ( "B97M-rV/6-31g*/PCM Geometry Optimization" ) B97M_rV_6_31g_d_PCM_Single_Point = "B97M-rV/6-31g*/PCM Single Point" B97M_rV_6_31g_d_PCM_Transition_State_Geometry_Optimization = ( "B97M-rV/6-31g*/PCM Transition State Geometry Optimization" ) B97M_rV_6_31g_d_PCM_Unknown = "B97M-rV/6-31g*/PCM Unknown" B97M_rV_6_31g_d_SMD_Force = "B97M-rV/6-31g*/SMD Force" B97M_rV_6_31g_d_SMD_Frequency_Analysis = "B97M-rV/6-31g*/SMD Frequency Analysis" B97M_rV_6_31g_d_SMD_Frequency_Flattening_Geometry_Optimization = ( "B97M-rV/6-31g*/SMD Frequency Flattening Geometry Optimization" ) B97M_rV_6_31g_d_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = ( "B97M-rV/6-31g*/SMD Frequency Flattening Transition State Geometry Optimization" ) B97M_rV_6_31g_d_SMD_Geometry_Optimization = ( "B97M-rV/6-31g*/SMD Geometry Optimization" ) B97M_rV_6_31g_d_SMD_Single_Point = "B97M-rV/6-31g*/SMD Single Point" B97M_rV_6_31g_d_SMD_Transition_State_Geometry_Optimization = ( "B97M-rV/6-31g*/SMD Transition State Geometry Optimization" ) B97M_rV_6_31g_d_SMD_Unknown = "B97M-rV/6-31g*/SMD Unknown" B97M_rV_6_31g_d_VACUUM_Force = "B97M-rV/6-31g*/VACUUM Force" B97M_rV_6_31g_d_VACUUM_Frequency_Analysis = ( "B97M-rV/6-31g*/VACUUM Frequency Analysis" ) B97M_rV_6_31g_d_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "B97M-rV/6-31g*/VACUUM Frequency Flattening Geometry Optimization" ) B97M_rV_6_31g_d_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97M-rV/6-31g*/VACUUM Frequency Flattening Transition State Geometry Optimization" B97M_rV_6_31g_d_VACUUM_Geometry_Optimization = ( "B97M-rV/6-31g*/VACUUM Geometry Optimization" ) B97M_rV_6_31g_d_VACUUM_Single_Point = "B97M-rV/6-31g*/VACUUM Single Point" B97M_rV_6_31g_d_VACUUM_Transition_State_Geometry_Optimization = ( "B97M-rV/6-31g*/VACUUM Transition State Geometry Optimization" ) B97M_rV_6_31g_d_VACUUM_Unknown = "B97M-rV/6-31g*/VACUUM Unknown" B97M_rV_def2_QZVPD_PCM_Force = "B97M-rV/def2-QZVPD/PCM Force" B97M_rV_def2_QZVPD_PCM_Frequency_Analysis = ( "B97M-rV/def2-QZVPD/PCM Frequency Analysis" ) B97M_rV_def2_QZVPD_PCM_Frequency_Flattening_Geometry_Optimization = ( "B97M-rV/def2-QZVPD/PCM Frequency Flattening Geometry Optimization" ) B97M_rV_def2_QZVPD_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97M-rV/def2-QZVPD/PCM Frequency Flattening Transition State Geometry Optimization" B97M_rV_def2_QZVPD_PCM_Geometry_Optimization = ( "B97M-rV/def2-QZVPD/PCM Geometry Optimization" ) B97M_rV_def2_QZVPD_PCM_Single_Point = "B97M-rV/def2-QZVPD/PCM Single Point" B97M_rV_def2_QZVPD_PCM_Transition_State_Geometry_Optimization = ( "B97M-rV/def2-QZVPD/PCM Transition State Geometry Optimization" ) B97M_rV_def2_QZVPD_PCM_Unknown = "B97M-rV/def2-QZVPD/PCM Unknown" B97M_rV_def2_QZVPD_SMD_Force = "B97M-rV/def2-QZVPD/SMD Force" B97M_rV_def2_QZVPD_SMD_Frequency_Analysis = ( "B97M-rV/def2-QZVPD/SMD Frequency Analysis" ) B97M_rV_def2_QZVPD_SMD_Frequency_Flattening_Geometry_Optimization = ( "B97M-rV/def2-QZVPD/SMD Frequency Flattening Geometry Optimization" ) B97M_rV_def2_QZVPD_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97M-rV/def2-QZVPD/SMD Frequency Flattening Transition State Geometry Optimization" B97M_rV_def2_QZVPD_SMD_Geometry_Optimization = ( "B97M-rV/def2-QZVPD/SMD Geometry Optimization" ) B97M_rV_def2_QZVPD_SMD_Single_Point = "B97M-rV/def2-QZVPD/SMD Single Point" B97M_rV_def2_QZVPD_SMD_Transition_State_Geometry_Optimization = ( "B97M-rV/def2-QZVPD/SMD Transition State Geometry Optimization" ) B97M_rV_def2_QZVPD_SMD_Unknown = "B97M-rV/def2-QZVPD/SMD Unknown" B97M_rV_def2_QZVPD_VACUUM_Force = "B97M-rV/def2-QZVPD/VACUUM Force" B97M_rV_def2_QZVPD_VACUUM_Frequency_Analysis = ( "B97M-rV/def2-QZVPD/VACUUM Frequency Analysis" ) B97M_rV_def2_QZVPD_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "B97M-rV/def2-QZVPD/VACUUM Frequency Flattening Geometry Optimization" ) B97M_rV_def2_QZVPD_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97M-rV/def2-QZVPD/VACUUM Frequency Flattening Transition State Geometry Optimization" B97M_rV_def2_QZVPD_VACUUM_Geometry_Optimization = ( "B97M-rV/def2-QZVPD/VACUUM Geometry Optimization" ) B97M_rV_def2_QZVPD_VACUUM_Single_Point = "B97M-rV/def2-QZVPD/VACUUM Single Point" B97M_rV_def2_QZVPD_VACUUM_Transition_State_Geometry_Optimization = ( "B97M-rV/def2-QZVPD/VACUUM Transition State Geometry Optimization" ) B97M_rV_def2_QZVPD_VACUUM_Unknown = "B97M-rV/def2-QZVPD/VACUUM Unknown" B97M_rV_def2_QZVPPD_PCM_Force = "B97M-rV/def2-QZVPPD/PCM Force" B97M_rV_def2_QZVPPD_PCM_Frequency_Analysis = ( "B97M-rV/def2-QZVPPD/PCM Frequency Analysis" ) B97M_rV_def2_QZVPPD_PCM_Frequency_Flattening_Geometry_Optimization = ( "B97M-rV/def2-QZVPPD/PCM Frequency Flattening Geometry Optimization" ) B97M_rV_def2_QZVPPD_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97M-rV/def2-QZVPPD/PCM Frequency Flattening Transition State Geometry Optimization" B97M_rV_def2_QZVPPD_PCM_Geometry_Optimization = ( "B97M-rV/def2-QZVPPD/PCM Geometry Optimization" ) B97M_rV_def2_QZVPPD_PCM_Single_Point = "B97M-rV/def2-QZVPPD/PCM Single Point" B97M_rV_def2_QZVPPD_PCM_Transition_State_Geometry_Optimization = ( "B97M-rV/def2-QZVPPD/PCM Transition State Geometry Optimization" ) B97M_rV_def2_QZVPPD_PCM_Unknown = "B97M-rV/def2-QZVPPD/PCM Unknown" B97M_rV_def2_QZVPPD_SMD_Force = "B97M-rV/def2-QZVPPD/SMD Force" B97M_rV_def2_QZVPPD_SMD_Frequency_Analysis = ( "B97M-rV/def2-QZVPPD/SMD Frequency Analysis" ) B97M_rV_def2_QZVPPD_SMD_Frequency_Flattening_Geometry_Optimization = ( "B97M-rV/def2-QZVPPD/SMD Frequency Flattening Geometry Optimization" ) B97M_rV_def2_QZVPPD_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97M-rV/def2-QZVPPD/SMD Frequency Flattening Transition State Geometry Optimization" B97M_rV_def2_QZVPPD_SMD_Geometry_Optimization = ( "B97M-rV/def2-QZVPPD/SMD Geometry Optimization" ) B97M_rV_def2_QZVPPD_SMD_Single_Point = "B97M-rV/def2-QZVPPD/SMD Single Point" B97M_rV_def2_QZVPPD_SMD_Transition_State_Geometry_Optimization = ( "B97M-rV/def2-QZVPPD/SMD Transition State Geometry Optimization" ) B97M_rV_def2_QZVPPD_SMD_Unknown = "B97M-rV/def2-QZVPPD/SMD Unknown" B97M_rV_def2_QZVPPD_VACUUM_Force = "B97M-rV/def2-QZVPPD/VACUUM Force" B97M_rV_def2_QZVPPD_VACUUM_Frequency_Analysis = ( "B97M-rV/def2-QZVPPD/VACUUM Frequency Analysis" ) B97M_rV_def2_QZVPPD_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "B97M-rV/def2-QZVPPD/VACUUM Frequency Flattening Geometry Optimization" ) B97M_rV_def2_QZVPPD_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97M-rV/def2-QZVPPD/VACUUM Frequency Flattening Transition State Geometry Optimization" B97M_rV_def2_QZVPPD_VACUUM_Geometry_Optimization = ( "B97M-rV/def2-QZVPPD/VACUUM Geometry Optimization" ) B97M_rV_def2_QZVPPD_VACUUM_Single_Point = "B97M-rV/def2-QZVPPD/VACUUM Single Point" B97M_rV_def2_QZVPPD_VACUUM_Transition_State_Geometry_Optimization = ( "B97M-rV/def2-QZVPPD/VACUUM Transition State Geometry Optimization" ) B97M_rV_def2_QZVPPD_VACUUM_Unknown = "B97M-rV/def2-QZVPPD/VACUUM Unknown" B97M_rV_def2_SVPD_PCM_Force = "B97M-rV/def2-SVPD/PCM Force" B97M_rV_def2_SVPD_PCM_Frequency_Analysis = ( "B97M-rV/def2-SVPD/PCM Frequency Analysis" ) B97M_rV_def2_SVPD_PCM_Frequency_Flattening_Geometry_Optimization = ( "B97M-rV/def2-SVPD/PCM Frequency Flattening Geometry Optimization" ) B97M_rV_def2_SVPD_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97M-rV/def2-SVPD/PCM Frequency Flattening Transition State Geometry Optimization" B97M_rV_def2_SVPD_PCM_Geometry_Optimization = ( "B97M-rV/def2-SVPD/PCM Geometry Optimization" ) B97M_rV_def2_SVPD_PCM_Single_Point = "B97M-rV/def2-SVPD/PCM Single Point" B97M_rV_def2_SVPD_PCM_Transition_State_Geometry_Optimization = ( "B97M-rV/def2-SVPD/PCM Transition State Geometry Optimization" ) B97M_rV_def2_SVPD_PCM_Unknown = "B97M-rV/def2-SVPD/PCM Unknown" B97M_rV_def2_SVPD_SMD_Force = "B97M-rV/def2-SVPD/SMD Force" B97M_rV_def2_SVPD_SMD_Frequency_Analysis = ( "B97M-rV/def2-SVPD/SMD Frequency Analysis" ) B97M_rV_def2_SVPD_SMD_Frequency_Flattening_Geometry_Optimization = ( "B97M-rV/def2-SVPD/SMD Frequency Flattening Geometry Optimization" ) B97M_rV_def2_SVPD_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97M-rV/def2-SVPD/SMD Frequency Flattening Transition State Geometry Optimization" B97M_rV_def2_SVPD_SMD_Geometry_Optimization = ( "B97M-rV/def2-SVPD/SMD Geometry Optimization" ) B97M_rV_def2_SVPD_SMD_Single_Point = "B97M-rV/def2-SVPD/SMD Single Point" B97M_rV_def2_SVPD_SMD_Transition_State_Geometry_Optimization = ( "B97M-rV/def2-SVPD/SMD Transition State Geometry Optimization" ) B97M_rV_def2_SVPD_SMD_Unknown = "B97M-rV/def2-SVPD/SMD Unknown" B97M_rV_def2_SVPD_VACUUM_Force = "B97M-rV/def2-SVPD/VACUUM Force" B97M_rV_def2_SVPD_VACUUM_Frequency_Analysis = ( "B97M-rV/def2-SVPD/VACUUM Frequency Analysis" ) B97M_rV_def2_SVPD_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "B97M-rV/def2-SVPD/VACUUM Frequency Flattening Geometry Optimization" ) B97M_rV_def2_SVPD_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97M-rV/def2-SVPD/VACUUM Frequency Flattening Transition State Geometry Optimization" B97M_rV_def2_SVPD_VACUUM_Geometry_Optimization = ( "B97M-rV/def2-SVPD/VACUUM Geometry Optimization" ) B97M_rV_def2_SVPD_VACUUM_Single_Point = "B97M-rV/def2-SVPD/VACUUM Single Point" B97M_rV_def2_SVPD_VACUUM_Transition_State_Geometry_Optimization = ( "B97M-rV/def2-SVPD/VACUUM Transition State Geometry Optimization" ) B97M_rV_def2_SVPD_VACUUM_Unknown = "B97M-rV/def2-SVPD/VACUUM Unknown" B97M_rV_def2_TZVPD_PCM_Force = "B97M-rV/def2-TZVPD/PCM Force" B97M_rV_def2_TZVPD_PCM_Frequency_Analysis = ( "B97M-rV/def2-TZVPD/PCM Frequency Analysis" ) B97M_rV_def2_TZVPD_PCM_Frequency_Flattening_Geometry_Optimization = ( "B97M-rV/def2-TZVPD/PCM Frequency Flattening Geometry Optimization" ) B97M_rV_def2_TZVPD_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97M-rV/def2-TZVPD/PCM Frequency Flattening Transition State Geometry Optimization" B97M_rV_def2_TZVPD_PCM_Geometry_Optimization = ( "B97M-rV/def2-TZVPD/PCM Geometry Optimization" ) B97M_rV_def2_TZVPD_PCM_Single_Point = "B97M-rV/def2-TZVPD/PCM Single Point" B97M_rV_def2_TZVPD_PCM_Transition_State_Geometry_Optimization = ( "B97M-rV/def2-TZVPD/PCM Transition State Geometry Optimization" ) B97M_rV_def2_TZVPD_PCM_Unknown = "B97M-rV/def2-TZVPD/PCM Unknown" B97M_rV_def2_TZVPD_SMD_Force = "B97M-rV/def2-TZVPD/SMD Force" B97M_rV_def2_TZVPD_SMD_Frequency_Analysis = ( "B97M-rV/def2-TZVPD/SMD Frequency Analysis" ) B97M_rV_def2_TZVPD_SMD_Frequency_Flattening_Geometry_Optimization = ( "B97M-rV/def2-TZVPD/SMD Frequency Flattening Geometry Optimization" ) B97M_rV_def2_TZVPD_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97M-rV/def2-TZVPD/SMD Frequency Flattening Transition State Geometry Optimization" B97M_rV_def2_TZVPD_SMD_Geometry_Optimization = ( "B97M-rV/def2-TZVPD/SMD Geometry Optimization" ) B97M_rV_def2_TZVPD_SMD_Single_Point = "B97M-rV/def2-TZVPD/SMD Single Point" B97M_rV_def2_TZVPD_SMD_Transition_State_Geometry_Optimization = ( "B97M-rV/def2-TZVPD/SMD Transition State Geometry Optimization" ) B97M_rV_def2_TZVPD_SMD_Unknown = "B97M-rV/def2-TZVPD/SMD Unknown" B97M_rV_def2_TZVPD_VACUUM_Force = "B97M-rV/def2-TZVPD/VACUUM Force" B97M_rV_def2_TZVPD_VACUUM_Frequency_Analysis = ( "B97M-rV/def2-TZVPD/VACUUM Frequency Analysis" ) B97M_rV_def2_TZVPD_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "B97M-rV/def2-TZVPD/VACUUM Frequency Flattening Geometry Optimization" ) B97M_rV_def2_TZVPD_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97M-rV/def2-TZVPD/VACUUM Frequency Flattening Transition State Geometry Optimization" B97M_rV_def2_TZVPD_VACUUM_Geometry_Optimization = ( "B97M-rV/def2-TZVPD/VACUUM Geometry Optimization" ) B97M_rV_def2_TZVPD_VACUUM_Single_Point = "B97M-rV/def2-TZVPD/VACUUM Single Point" B97M_rV_def2_TZVPD_VACUUM_Transition_State_Geometry_Optimization = ( "B97M-rV/def2-TZVPD/VACUUM Transition State Geometry Optimization" ) B97M_rV_def2_TZVPD_VACUUM_Unknown = "B97M-rV/def2-TZVPD/VACUUM Unknown" B97M_rV_def2_TZVPPD_PCM_Force = "B97M-rV/def2-TZVPPD/PCM Force" B97M_rV_def2_TZVPPD_PCM_Frequency_Analysis = ( "B97M-rV/def2-TZVPPD/PCM Frequency Analysis" ) B97M_rV_def2_TZVPPD_PCM_Frequency_Flattening_Geometry_Optimization = ( "B97M-rV/def2-TZVPPD/PCM Frequency Flattening Geometry Optimization" ) B97M_rV_def2_TZVPPD_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97M-rV/def2-TZVPPD/PCM Frequency Flattening Transition State Geometry Optimization" B97M_rV_def2_TZVPPD_PCM_Geometry_Optimization = ( "B97M-rV/def2-TZVPPD/PCM Geometry Optimization" ) B97M_rV_def2_TZVPPD_PCM_Single_Point = "B97M-rV/def2-TZVPPD/PCM Single Point" B97M_rV_def2_TZVPPD_PCM_Transition_State_Geometry_Optimization = ( "B97M-rV/def2-TZVPPD/PCM Transition State Geometry Optimization" ) B97M_rV_def2_TZVPPD_PCM_Unknown = "B97M-rV/def2-TZVPPD/PCM Unknown" B97M_rV_def2_TZVPPD_SMD_Force = "B97M-rV/def2-TZVPPD/SMD Force" B97M_rV_def2_TZVPPD_SMD_Frequency_Analysis = ( "B97M-rV/def2-TZVPPD/SMD Frequency Analysis" ) B97M_rV_def2_TZVPPD_SMD_Frequency_Flattening_Geometry_Optimization = ( "B97M-rV/def2-TZVPPD/SMD Frequency Flattening Geometry Optimization" ) B97M_rV_def2_TZVPPD_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97M-rV/def2-TZVPPD/SMD Frequency Flattening Transition State Geometry Optimization" B97M_rV_def2_TZVPPD_SMD_Geometry_Optimization = ( "B97M-rV/def2-TZVPPD/SMD Geometry Optimization" ) B97M_rV_def2_TZVPPD_SMD_Single_Point = "B97M-rV/def2-TZVPPD/SMD Single Point" B97M_rV_def2_TZVPPD_SMD_Transition_State_Geometry_Optimization = ( "B97M-rV/def2-TZVPPD/SMD Transition State Geometry Optimization" ) B97M_rV_def2_TZVPPD_SMD_Unknown = "B97M-rV/def2-TZVPPD/SMD Unknown" B97M_rV_def2_TZVPPD_VACUUM_Force = "B97M-rV/def2-TZVPPD/VACUUM Force" B97M_rV_def2_TZVPPD_VACUUM_Frequency_Analysis = ( "B97M-rV/def2-TZVPPD/VACUUM Frequency Analysis" ) B97M_rV_def2_TZVPPD_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "B97M-rV/def2-TZVPPD/VACUUM Frequency Flattening Geometry Optimization" ) B97M_rV_def2_TZVPPD_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97M-rV/def2-TZVPPD/VACUUM Frequency Flattening Transition State Geometry Optimization" B97M_rV_def2_TZVPPD_VACUUM_Geometry_Optimization = ( "B97M-rV/def2-TZVPPD/VACUUM Geometry Optimization" ) B97M_rV_def2_TZVPPD_VACUUM_Single_Point = "B97M-rV/def2-TZVPPD/VACUUM Single Point" B97M_rV_def2_TZVPPD_VACUUM_Transition_State_Geometry_Optimization = ( "B97M-rV/def2-TZVPPD/VACUUM Transition State Geometry Optimization" ) B97M_rV_def2_TZVPPD_VACUUM_Unknown = "B97M-rV/def2-TZVPPD/VACUUM Unknown" B97M_rV_def2_TZVPP_PCM_Force = "B97M-rV/def2-TZVPP/PCM Force" B97M_rV_def2_TZVPP_PCM_Frequency_Analysis = ( "B97M-rV/def2-TZVPP/PCM Frequency Analysis" ) B97M_rV_def2_TZVPP_PCM_Frequency_Flattening_Geometry_Optimization = ( "B97M-rV/def2-TZVPP/PCM Frequency Flattening Geometry Optimization" ) B97M_rV_def2_TZVPP_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97M-rV/def2-TZVPP/PCM Frequency Flattening Transition State Geometry Optimization" B97M_rV_def2_TZVPP_PCM_Geometry_Optimization = ( "B97M-rV/def2-TZVPP/PCM Geometry Optimization" ) B97M_rV_def2_TZVPP_PCM_Single_Point = "B97M-rV/def2-TZVPP/PCM Single Point" B97M_rV_def2_TZVPP_PCM_Transition_State_Geometry_Optimization = ( "B97M-rV/def2-TZVPP/PCM Transition State Geometry Optimization" ) B97M_rV_def2_TZVPP_PCM_Unknown = "B97M-rV/def2-TZVPP/PCM Unknown" B97M_rV_def2_TZVPP_SMD_Force = "B97M-rV/def2-TZVPP/SMD Force" B97M_rV_def2_TZVPP_SMD_Frequency_Analysis = ( "B97M-rV/def2-TZVPP/SMD Frequency Analysis" ) B97M_rV_def2_TZVPP_SMD_Frequency_Flattening_Geometry_Optimization = ( "B97M-rV/def2-TZVPP/SMD Frequency Flattening Geometry Optimization" ) B97M_rV_def2_TZVPP_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97M-rV/def2-TZVPP/SMD Frequency Flattening Transition State Geometry Optimization" B97M_rV_def2_TZVPP_SMD_Geometry_Optimization = ( "B97M-rV/def2-TZVPP/SMD Geometry Optimization" ) B97M_rV_def2_TZVPP_SMD_Single_Point = "B97M-rV/def2-TZVPP/SMD Single Point" B97M_rV_def2_TZVPP_SMD_Transition_State_Geometry_Optimization = ( "B97M-rV/def2-TZVPP/SMD Transition State Geometry Optimization" ) B97M_rV_def2_TZVPP_SMD_Unknown = "B97M-rV/def2-TZVPP/SMD Unknown" B97M_rV_def2_TZVPP_VACUUM_Force = "B97M-rV/def2-TZVPP/VACUUM Force" B97M_rV_def2_TZVPP_VACUUM_Frequency_Analysis = ( "B97M-rV/def2-TZVPP/VACUUM Frequency Analysis" ) B97M_rV_def2_TZVPP_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "B97M-rV/def2-TZVPP/VACUUM Frequency Flattening Geometry Optimization" ) B97M_rV_def2_TZVPP_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97M-rV/def2-TZVPP/VACUUM Frequency Flattening Transition State Geometry Optimization" B97M_rV_def2_TZVPP_VACUUM_Geometry_Optimization = ( "B97M-rV/def2-TZVPP/VACUUM Geometry Optimization" ) B97M_rV_def2_TZVPP_VACUUM_Single_Point = "B97M-rV/def2-TZVPP/VACUUM Single Point" B97M_rV_def2_TZVPP_VACUUM_Transition_State_Geometry_Optimization = ( "B97M-rV/def2-TZVPP/VACUUM Transition State Geometry Optimization" ) B97M_rV_def2_TZVPP_VACUUM_Unknown = "B97M-rV/def2-TZVPP/VACUUM Unknown" B97M_rV_def2_TZVP_PCM_Force = "B97M-rV/def2-TZVP/PCM Force" B97M_rV_def2_TZVP_PCM_Frequency_Analysis = ( "B97M-rV/def2-TZVP/PCM Frequency Analysis" ) B97M_rV_def2_TZVP_PCM_Frequency_Flattening_Geometry_Optimization = ( "B97M-rV/def2-TZVP/PCM Frequency Flattening Geometry Optimization" ) B97M_rV_def2_TZVP_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97M-rV/def2-TZVP/PCM Frequency Flattening Transition State Geometry Optimization" B97M_rV_def2_TZVP_PCM_Geometry_Optimization = ( "B97M-rV/def2-TZVP/PCM Geometry Optimization" ) B97M_rV_def2_TZVP_PCM_Single_Point = "B97M-rV/def2-TZVP/PCM Single Point" B97M_rV_def2_TZVP_PCM_Transition_State_Geometry_Optimization = ( "B97M-rV/def2-TZVP/PCM Transition State Geometry Optimization" ) B97M_rV_def2_TZVP_PCM_Unknown = "B97M-rV/def2-TZVP/PCM Unknown" B97M_rV_def2_TZVP_SMD_Force = "B97M-rV/def2-TZVP/SMD Force" B97M_rV_def2_TZVP_SMD_Frequency_Analysis = ( "B97M-rV/def2-TZVP/SMD Frequency Analysis" ) B97M_rV_def2_TZVP_SMD_Frequency_Flattening_Geometry_Optimization = ( "B97M-rV/def2-TZVP/SMD Frequency Flattening Geometry Optimization" ) B97M_rV_def2_TZVP_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97M-rV/def2-TZVP/SMD Frequency Flattening Transition State Geometry Optimization" B97M_rV_def2_TZVP_SMD_Geometry_Optimization = ( "B97M-rV/def2-TZVP/SMD Geometry Optimization" ) B97M_rV_def2_TZVP_SMD_Single_Point = "B97M-rV/def2-TZVP/SMD Single Point" B97M_rV_def2_TZVP_SMD_Transition_State_Geometry_Optimization = ( "B97M-rV/def2-TZVP/SMD Transition State Geometry Optimization" ) B97M_rV_def2_TZVP_SMD_Unknown = "B97M-rV/def2-TZVP/SMD Unknown" B97M_rV_def2_TZVP_VACUUM_Force = "B97M-rV/def2-TZVP/VACUUM Force" B97M_rV_def2_TZVP_VACUUM_Frequency_Analysis = ( "B97M-rV/def2-TZVP/VACUUM Frequency Analysis" ) B97M_rV_def2_TZVP_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "B97M-rV/def2-TZVP/VACUUM Frequency Flattening Geometry Optimization" ) B97M_rV_def2_TZVP_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97M-rV/def2-TZVP/VACUUM Frequency Flattening Transition State Geometry Optimization" B97M_rV_def2_TZVP_VACUUM_Geometry_Optimization = ( "B97M-rV/def2-TZVP/VACUUM Geometry Optimization" ) B97M_rV_def2_TZVP_VACUUM_Single_Point = "B97M-rV/def2-TZVP/VACUUM Single Point" B97M_rV_def2_TZVP_VACUUM_Transition_State_Geometry_Optimization = ( "B97M-rV/def2-TZVP/VACUUM Transition State Geometry Optimization" ) B97M_rV_def2_TZVP_VACUUM_Unknown = "B97M-rV/def2-TZVP/VACUUM Unknown" B97_D3_6_31g_d_PCM_Force = "B97-D3/6-31g*/PCM Force" B97_D3_6_31g_d_PCM_Frequency_Analysis = "B97-D3/6-31g*/PCM Frequency Analysis" B97_D3_6_31g_d_PCM_Frequency_Flattening_Geometry_Optimization = ( "B97-D3/6-31g*/PCM Frequency Flattening Geometry Optimization" ) B97_D3_6_31g_d_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = ( "B97-D3/6-31g*/PCM Frequency Flattening Transition State Geometry Optimization" ) B97_D3_6_31g_d_PCM_Geometry_Optimization = "B97-D3/6-31g*/PCM Geometry Optimization" B97_D3_6_31g_d_PCM_Single_Point = "B97-D3/6-31g*/PCM Single Point" B97_D3_6_31g_d_PCM_Transition_State_Geometry_Optimization = ( "B97-D3/6-31g*/PCM Transition State Geometry Optimization" ) B97_D3_6_31g_d_PCM_Unknown = "B97-D3/6-31g*/PCM Unknown" B97_D3_6_31g_d_SMD_Force = "B97-D3/6-31g*/SMD Force" B97_D3_6_31g_d_SMD_Frequency_Analysis = "B97-D3/6-31g*/SMD Frequency Analysis" B97_D3_6_31g_d_SMD_Frequency_Flattening_Geometry_Optimization = ( "B97-D3/6-31g*/SMD Frequency Flattening Geometry Optimization" ) B97_D3_6_31g_d_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = ( "B97-D3/6-31g*/SMD Frequency Flattening Transition State Geometry Optimization" ) B97_D3_6_31g_d_SMD_Geometry_Optimization = "B97-D3/6-31g*/SMD Geometry Optimization" B97_D3_6_31g_d_SMD_Single_Point = "B97-D3/6-31g*/SMD Single Point" B97_D3_6_31g_d_SMD_Transition_State_Geometry_Optimization = ( "B97-D3/6-31g*/SMD Transition State Geometry Optimization" ) B97_D3_6_31g_d_SMD_Unknown = "B97-D3/6-31g*/SMD Unknown" B97_D3_6_31g_d_VACUUM_Force = "B97-D3/6-31g*/VACUUM Force" B97_D3_6_31g_d_VACUUM_Frequency_Analysis = "B97-D3/6-31g*/VACUUM Frequency Analysis" B97_D3_6_31g_d_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "B97-D3/6-31g*/VACUUM Frequency Flattening Geometry Optimization" ) B97_D3_6_31g_d_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97-D3/6-31g*/VACUUM Frequency Flattening Transition State Geometry Optimization" B97_D3_6_31g_d_VACUUM_Geometry_Optimization = ( "B97-D3/6-31g*/VACUUM Geometry Optimization" ) B97_D3_6_31g_d_VACUUM_Single_Point = "B97-D3/6-31g*/VACUUM Single Point" B97_D3_6_31g_d_VACUUM_Transition_State_Geometry_Optimization = ( "B97-D3/6-31g*/VACUUM Transition State Geometry Optimization" ) B97_D3_6_31g_d_VACUUM_Unknown = "B97-D3/6-31g*/VACUUM Unknown" B97_D3_def2_QZVPD_PCM_Force = "B97-D3/def2-QZVPD/PCM Force" B97_D3_def2_QZVPD_PCM_Frequency_Analysis = ( "B97-D3/def2-QZVPD/PCM Frequency Analysis" ) B97_D3_def2_QZVPD_PCM_Frequency_Flattening_Geometry_Optimization = ( "B97-D3/def2-QZVPD/PCM Frequency Flattening Geometry Optimization" ) B97_D3_def2_QZVPD_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97-D3/def2-QZVPD/PCM Frequency Flattening Transition State Geometry Optimization" B97_D3_def2_QZVPD_PCM_Geometry_Optimization = ( "B97-D3/def2-QZVPD/PCM Geometry Optimization" ) B97_D3_def2_QZVPD_PCM_Single_Point = "B97-D3/def2-QZVPD/PCM Single Point" B97_D3_def2_QZVPD_PCM_Transition_State_Geometry_Optimization = ( "B97-D3/def2-QZVPD/PCM Transition State Geometry Optimization" ) B97_D3_def2_QZVPD_PCM_Unknown = "B97-D3/def2-QZVPD/PCM Unknown" B97_D3_def2_QZVPD_SMD_Force = "B97-D3/def2-QZVPD/SMD Force" B97_D3_def2_QZVPD_SMD_Frequency_Analysis = ( "B97-D3/def2-QZVPD/SMD Frequency Analysis" ) B97_D3_def2_QZVPD_SMD_Frequency_Flattening_Geometry_Optimization = ( "B97-D3/def2-QZVPD/SMD Frequency Flattening Geometry Optimization" ) B97_D3_def2_QZVPD_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97-D3/def2-QZVPD/SMD Frequency Flattening Transition State Geometry Optimization" B97_D3_def2_QZVPD_SMD_Geometry_Optimization = ( "B97-D3/def2-QZVPD/SMD Geometry Optimization" ) B97_D3_def2_QZVPD_SMD_Single_Point = "B97-D3/def2-QZVPD/SMD Single Point" B97_D3_def2_QZVPD_SMD_Transition_State_Geometry_Optimization = ( "B97-D3/def2-QZVPD/SMD Transition State Geometry Optimization" ) B97_D3_def2_QZVPD_SMD_Unknown = "B97-D3/def2-QZVPD/SMD Unknown" B97_D3_def2_QZVPD_VACUUM_Force = "B97-D3/def2-QZVPD/VACUUM Force" B97_D3_def2_QZVPD_VACUUM_Frequency_Analysis = ( "B97-D3/def2-QZVPD/VACUUM Frequency Analysis" ) B97_D3_def2_QZVPD_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "B97-D3/def2-QZVPD/VACUUM Frequency Flattening Geometry Optimization" ) B97_D3_def2_QZVPD_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97-D3/def2-QZVPD/VACUUM Frequency Flattening Transition State Geometry Optimization" B97_D3_def2_QZVPD_VACUUM_Geometry_Optimization = ( "B97-D3/def2-QZVPD/VACUUM Geometry Optimization" ) B97_D3_def2_QZVPD_VACUUM_Single_Point = "B97-D3/def2-QZVPD/VACUUM Single Point" B97_D3_def2_QZVPD_VACUUM_Transition_State_Geometry_Optimization = ( "B97-D3/def2-QZVPD/VACUUM Transition State Geometry Optimization" ) B97_D3_def2_QZVPD_VACUUM_Unknown = "B97-D3/def2-QZVPD/VACUUM Unknown" B97_D3_def2_QZVPPD_PCM_Force = "B97-D3/def2-QZVPPD/PCM Force" B97_D3_def2_QZVPPD_PCM_Frequency_Analysis = ( "B97-D3/def2-QZVPPD/PCM Frequency Analysis" ) B97_D3_def2_QZVPPD_PCM_Frequency_Flattening_Geometry_Optimization = ( "B97-D3/def2-QZVPPD/PCM Frequency Flattening Geometry Optimization" ) B97_D3_def2_QZVPPD_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97-D3/def2-QZVPPD/PCM Frequency Flattening Transition State Geometry Optimization" B97_D3_def2_QZVPPD_PCM_Geometry_Optimization = ( "B97-D3/def2-QZVPPD/PCM Geometry Optimization" ) B97_D3_def2_QZVPPD_PCM_Single_Point = "B97-D3/def2-QZVPPD/PCM Single Point" B97_D3_def2_QZVPPD_PCM_Transition_State_Geometry_Optimization = ( "B97-D3/def2-QZVPPD/PCM Transition State Geometry Optimization" ) B97_D3_def2_QZVPPD_PCM_Unknown = "B97-D3/def2-QZVPPD/PCM Unknown" B97_D3_def2_QZVPPD_SMD_Force = "B97-D3/def2-QZVPPD/SMD Force" B97_D3_def2_QZVPPD_SMD_Frequency_Analysis = ( "B97-D3/def2-QZVPPD/SMD Frequency Analysis" ) B97_D3_def2_QZVPPD_SMD_Frequency_Flattening_Geometry_Optimization = ( "B97-D3/def2-QZVPPD/SMD Frequency Flattening Geometry Optimization" ) B97_D3_def2_QZVPPD_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97-D3/def2-QZVPPD/SMD Frequency Flattening Transition State Geometry Optimization" B97_D3_def2_QZVPPD_SMD_Geometry_Optimization = ( "B97-D3/def2-QZVPPD/SMD Geometry Optimization" ) B97_D3_def2_QZVPPD_SMD_Single_Point = "B97-D3/def2-QZVPPD/SMD Single Point" B97_D3_def2_QZVPPD_SMD_Transition_State_Geometry_Optimization = ( "B97-D3/def2-QZVPPD/SMD Transition State Geometry Optimization" ) B97_D3_def2_QZVPPD_SMD_Unknown = "B97-D3/def2-QZVPPD/SMD Unknown" B97_D3_def2_QZVPPD_VACUUM_Force = "B97-D3/def2-QZVPPD/VACUUM Force" B97_D3_def2_QZVPPD_VACUUM_Frequency_Analysis = ( "B97-D3/def2-QZVPPD/VACUUM Frequency Analysis" ) B97_D3_def2_QZVPPD_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "B97-D3/def2-QZVPPD/VACUUM Frequency Flattening Geometry Optimization" ) B97_D3_def2_QZVPPD_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97-D3/def2-QZVPPD/VACUUM Frequency Flattening Transition State Geometry Optimization" B97_D3_def2_QZVPPD_VACUUM_Geometry_Optimization = ( "B97-D3/def2-QZVPPD/VACUUM Geometry Optimization" ) B97_D3_def2_QZVPPD_VACUUM_Single_Point = "B97-D3/def2-QZVPPD/VACUUM Single Point" B97_D3_def2_QZVPPD_VACUUM_Transition_State_Geometry_Optimization = ( "B97-D3/def2-QZVPPD/VACUUM Transition State Geometry Optimization" ) B97_D3_def2_QZVPPD_VACUUM_Unknown = "B97-D3/def2-QZVPPD/VACUUM Unknown" B97_D3_def2_SVPD_PCM_Force = "B97-D3/def2-SVPD/PCM Force" B97_D3_def2_SVPD_PCM_Frequency_Analysis = "B97-D3/def2-SVPD/PCM Frequency Analysis" B97_D3_def2_SVPD_PCM_Frequency_Flattening_Geometry_Optimization = ( "B97-D3/def2-SVPD/PCM Frequency Flattening Geometry Optimization" ) B97_D3_def2_SVPD_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97-D3/def2-SVPD/PCM Frequency Flattening Transition State Geometry Optimization" B97_D3_def2_SVPD_PCM_Geometry_Optimization = ( "B97-D3/def2-SVPD/PCM Geometry Optimization" ) B97_D3_def2_SVPD_PCM_Single_Point = "B97-D3/def2-SVPD/PCM Single Point" B97_D3_def2_SVPD_PCM_Transition_State_Geometry_Optimization = ( "B97-D3/def2-SVPD/PCM Transition State Geometry Optimization" ) B97_D3_def2_SVPD_PCM_Unknown = "B97-D3/def2-SVPD/PCM Unknown" B97_D3_def2_SVPD_SMD_Force = "B97-D3/def2-SVPD/SMD Force" B97_D3_def2_SVPD_SMD_Frequency_Analysis = "B97-D3/def2-SVPD/SMD Frequency Analysis" B97_D3_def2_SVPD_SMD_Frequency_Flattening_Geometry_Optimization = ( "B97-D3/def2-SVPD/SMD Frequency Flattening Geometry Optimization" ) B97_D3_def2_SVPD_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97-D3/def2-SVPD/SMD Frequency Flattening Transition State Geometry Optimization" B97_D3_def2_SVPD_SMD_Geometry_Optimization = ( "B97-D3/def2-SVPD/SMD Geometry Optimization" ) B97_D3_def2_SVPD_SMD_Single_Point = "B97-D3/def2-SVPD/SMD Single Point" B97_D3_def2_SVPD_SMD_Transition_State_Geometry_Optimization = ( "B97-D3/def2-SVPD/SMD Transition State Geometry Optimization" ) B97_D3_def2_SVPD_SMD_Unknown = "B97-D3/def2-SVPD/SMD Unknown" B97_D3_def2_SVPD_VACUUM_Force = "B97-D3/def2-SVPD/VACUUM Force" B97_D3_def2_SVPD_VACUUM_Frequency_Analysis = ( "B97-D3/def2-SVPD/VACUUM Frequency Analysis" ) B97_D3_def2_SVPD_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "B97-D3/def2-SVPD/VACUUM Frequency Flattening Geometry Optimization" ) B97_D3_def2_SVPD_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97-D3/def2-SVPD/VACUUM Frequency Flattening Transition State Geometry Optimization" B97_D3_def2_SVPD_VACUUM_Geometry_Optimization = ( "B97-D3/def2-SVPD/VACUUM Geometry Optimization" ) B97_D3_def2_SVPD_VACUUM_Single_Point = "B97-D3/def2-SVPD/VACUUM Single Point" B97_D3_def2_SVPD_VACUUM_Transition_State_Geometry_Optimization = ( "B97-D3/def2-SVPD/VACUUM Transition State Geometry Optimization" ) B97_D3_def2_SVPD_VACUUM_Unknown = "B97-D3/def2-SVPD/VACUUM Unknown" B97_D3_def2_TZVPD_PCM_Force = "B97-D3/def2-TZVPD/PCM Force" B97_D3_def2_TZVPD_PCM_Frequency_Analysis = ( "B97-D3/def2-TZVPD/PCM Frequency Analysis" ) B97_D3_def2_TZVPD_PCM_Frequency_Flattening_Geometry_Optimization = ( "B97-D3/def2-TZVPD/PCM Frequency Flattening Geometry Optimization" ) B97_D3_def2_TZVPD_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97-D3/def2-TZVPD/PCM Frequency Flattening Transition State Geometry Optimization" B97_D3_def2_TZVPD_PCM_Geometry_Optimization = ( "B97-D3/def2-TZVPD/PCM Geometry Optimization" ) B97_D3_def2_TZVPD_PCM_Single_Point = "B97-D3/def2-TZVPD/PCM Single Point" B97_D3_def2_TZVPD_PCM_Transition_State_Geometry_Optimization = ( "B97-D3/def2-TZVPD/PCM Transition State Geometry Optimization" ) B97_D3_def2_TZVPD_PCM_Unknown = "B97-D3/def2-TZVPD/PCM Unknown" B97_D3_def2_TZVPD_SMD_Force = "B97-D3/def2-TZVPD/SMD Force" B97_D3_def2_TZVPD_SMD_Frequency_Analysis = ( "B97-D3/def2-TZVPD/SMD Frequency Analysis" ) B97_D3_def2_TZVPD_SMD_Frequency_Flattening_Geometry_Optimization = ( "B97-D3/def2-TZVPD/SMD Frequency Flattening Geometry Optimization" ) B97_D3_def2_TZVPD_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97-D3/def2-TZVPD/SMD Frequency Flattening Transition State Geometry Optimization" B97_D3_def2_TZVPD_SMD_Geometry_Optimization = ( "B97-D3/def2-TZVPD/SMD Geometry Optimization" ) B97_D3_def2_TZVPD_SMD_Single_Point = "B97-D3/def2-TZVPD/SMD Single Point" B97_D3_def2_TZVPD_SMD_Transition_State_Geometry_Optimization = ( "B97-D3/def2-TZVPD/SMD Transition State Geometry Optimization" ) B97_D3_def2_TZVPD_SMD_Unknown = "B97-D3/def2-TZVPD/SMD Unknown" B97_D3_def2_TZVPD_VACUUM_Force = "B97-D3/def2-TZVPD/VACUUM Force" B97_D3_def2_TZVPD_VACUUM_Frequency_Analysis = ( "B97-D3/def2-TZVPD/VACUUM Frequency Analysis" ) B97_D3_def2_TZVPD_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "B97-D3/def2-TZVPD/VACUUM Frequency Flattening Geometry Optimization" ) B97_D3_def2_TZVPD_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97-D3/def2-TZVPD/VACUUM Frequency Flattening Transition State Geometry Optimization" B97_D3_def2_TZVPD_VACUUM_Geometry_Optimization = ( "B97-D3/def2-TZVPD/VACUUM Geometry Optimization" ) B97_D3_def2_TZVPD_VACUUM_Single_Point = "B97-D3/def2-TZVPD/VACUUM Single Point" B97_D3_def2_TZVPD_VACUUM_Transition_State_Geometry_Optimization = ( "B97-D3/def2-TZVPD/VACUUM Transition State Geometry Optimization" ) B97_D3_def2_TZVPD_VACUUM_Unknown = "B97-D3/def2-TZVPD/VACUUM Unknown" B97_D3_def2_TZVPPD_PCM_Force = "B97-D3/def2-TZVPPD/PCM Force" B97_D3_def2_TZVPPD_PCM_Frequency_Analysis = ( "B97-D3/def2-TZVPPD/PCM Frequency Analysis" ) B97_D3_def2_TZVPPD_PCM_Frequency_Flattening_Geometry_Optimization = ( "B97-D3/def2-TZVPPD/PCM Frequency Flattening Geometry Optimization" ) B97_D3_def2_TZVPPD_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97-D3/def2-TZVPPD/PCM Frequency Flattening Transition State Geometry Optimization" B97_D3_def2_TZVPPD_PCM_Geometry_Optimization = ( "B97-D3/def2-TZVPPD/PCM Geometry Optimization" ) B97_D3_def2_TZVPPD_PCM_Single_Point = "B97-D3/def2-TZVPPD/PCM Single Point" B97_D3_def2_TZVPPD_PCM_Transition_State_Geometry_Optimization = ( "B97-D3/def2-TZVPPD/PCM Transition State Geometry Optimization" ) B97_D3_def2_TZVPPD_PCM_Unknown = "B97-D3/def2-TZVPPD/PCM Unknown" B97_D3_def2_TZVPPD_SMD_Force = "B97-D3/def2-TZVPPD/SMD Force" B97_D3_def2_TZVPPD_SMD_Frequency_Analysis = ( "B97-D3/def2-TZVPPD/SMD Frequency Analysis" ) B97_D3_def2_TZVPPD_SMD_Frequency_Flattening_Geometry_Optimization = ( "B97-D3/def2-TZVPPD/SMD Frequency Flattening Geometry Optimization" ) B97_D3_def2_TZVPPD_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97-D3/def2-TZVPPD/SMD Frequency Flattening Transition State Geometry Optimization" B97_D3_def2_TZVPPD_SMD_Geometry_Optimization = ( "B97-D3/def2-TZVPPD/SMD Geometry Optimization" ) B97_D3_def2_TZVPPD_SMD_Single_Point = "B97-D3/def2-TZVPPD/SMD Single Point" B97_D3_def2_TZVPPD_SMD_Transition_State_Geometry_Optimization = ( "B97-D3/def2-TZVPPD/SMD Transition State Geometry Optimization" ) B97_D3_def2_TZVPPD_SMD_Unknown = "B97-D3/def2-TZVPPD/SMD Unknown" B97_D3_def2_TZVPPD_VACUUM_Force = "B97-D3/def2-TZVPPD/VACUUM Force" B97_D3_def2_TZVPPD_VACUUM_Frequency_Analysis = ( "B97-D3/def2-TZVPPD/VACUUM Frequency Analysis" ) B97_D3_def2_TZVPPD_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "B97-D3/def2-TZVPPD/VACUUM Frequency Flattening Geometry Optimization" ) B97_D3_def2_TZVPPD_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97-D3/def2-TZVPPD/VACUUM Frequency Flattening Transition State Geometry Optimization" B97_D3_def2_TZVPPD_VACUUM_Geometry_Optimization = ( "B97-D3/def2-TZVPPD/VACUUM Geometry Optimization" ) B97_D3_def2_TZVPPD_VACUUM_Single_Point = "B97-D3/def2-TZVPPD/VACUUM Single Point" B97_D3_def2_TZVPPD_VACUUM_Transition_State_Geometry_Optimization = ( "B97-D3/def2-TZVPPD/VACUUM Transition State Geometry Optimization" ) B97_D3_def2_TZVPPD_VACUUM_Unknown = "B97-D3/def2-TZVPPD/VACUUM Unknown" B97_D3_def2_TZVPP_PCM_Force = "B97-D3/def2-TZVPP/PCM Force" B97_D3_def2_TZVPP_PCM_Frequency_Analysis = ( "B97-D3/def2-TZVPP/PCM Frequency Analysis" ) B97_D3_def2_TZVPP_PCM_Frequency_Flattening_Geometry_Optimization = ( "B97-D3/def2-TZVPP/PCM Frequency Flattening Geometry Optimization" ) B97_D3_def2_TZVPP_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97-D3/def2-TZVPP/PCM Frequency Flattening Transition State Geometry Optimization" B97_D3_def2_TZVPP_PCM_Geometry_Optimization = ( "B97-D3/def2-TZVPP/PCM Geometry Optimization" ) B97_D3_def2_TZVPP_PCM_Single_Point = "B97-D3/def2-TZVPP/PCM Single Point" B97_D3_def2_TZVPP_PCM_Transition_State_Geometry_Optimization = ( "B97-D3/def2-TZVPP/PCM Transition State Geometry Optimization" ) B97_D3_def2_TZVPP_PCM_Unknown = "B97-D3/def2-TZVPP/PCM Unknown" B97_D3_def2_TZVPP_SMD_Force = "B97-D3/def2-TZVPP/SMD Force" B97_D3_def2_TZVPP_SMD_Frequency_Analysis = ( "B97-D3/def2-TZVPP/SMD Frequency Analysis" ) B97_D3_def2_TZVPP_SMD_Frequency_Flattening_Geometry_Optimization = ( "B97-D3/def2-TZVPP/SMD Frequency Flattening Geometry Optimization" ) B97_D3_def2_TZVPP_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97-D3/def2-TZVPP/SMD Frequency Flattening Transition State Geometry Optimization" B97_D3_def2_TZVPP_SMD_Geometry_Optimization = ( "B97-D3/def2-TZVPP/SMD Geometry Optimization" ) B97_D3_def2_TZVPP_SMD_Single_Point = "B97-D3/def2-TZVPP/SMD Single Point" B97_D3_def2_TZVPP_SMD_Transition_State_Geometry_Optimization = ( "B97-D3/def2-TZVPP/SMD Transition State Geometry Optimization" ) B97_D3_def2_TZVPP_SMD_Unknown = "B97-D3/def2-TZVPP/SMD Unknown" B97_D3_def2_TZVPP_VACUUM_Force = "B97-D3/def2-TZVPP/VACUUM Force" B97_D3_def2_TZVPP_VACUUM_Frequency_Analysis = ( "B97-D3/def2-TZVPP/VACUUM Frequency Analysis" ) B97_D3_def2_TZVPP_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "B97-D3/def2-TZVPP/VACUUM Frequency Flattening Geometry Optimization" ) B97_D3_def2_TZVPP_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97-D3/def2-TZVPP/VACUUM Frequency Flattening Transition State Geometry Optimization" B97_D3_def2_TZVPP_VACUUM_Geometry_Optimization = ( "B97-D3/def2-TZVPP/VACUUM Geometry Optimization" ) B97_D3_def2_TZVPP_VACUUM_Single_Point = "B97-D3/def2-TZVPP/VACUUM Single Point" B97_D3_def2_TZVPP_VACUUM_Transition_State_Geometry_Optimization = ( "B97-D3/def2-TZVPP/VACUUM Transition State Geometry Optimization" ) B97_D3_def2_TZVPP_VACUUM_Unknown = "B97-D3/def2-TZVPP/VACUUM Unknown" B97_D3_def2_TZVP_PCM_Force = "B97-D3/def2-TZVP/PCM Force" B97_D3_def2_TZVP_PCM_Frequency_Analysis = "B97-D3/def2-TZVP/PCM Frequency Analysis" B97_D3_def2_TZVP_PCM_Frequency_Flattening_Geometry_Optimization = ( "B97-D3/def2-TZVP/PCM Frequency Flattening Geometry Optimization" ) B97_D3_def2_TZVP_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97-D3/def2-TZVP/PCM Frequency Flattening Transition State Geometry Optimization" B97_D3_def2_TZVP_PCM_Geometry_Optimization = ( "B97-D3/def2-TZVP/PCM Geometry Optimization" ) B97_D3_def2_TZVP_PCM_Single_Point = "B97-D3/def2-TZVP/PCM Single Point" B97_D3_def2_TZVP_PCM_Transition_State_Geometry_Optimization = ( "B97-D3/def2-TZVP/PCM Transition State Geometry Optimization" ) B97_D3_def2_TZVP_PCM_Unknown = "B97-D3/def2-TZVP/PCM Unknown" B97_D3_def2_TZVP_SMD_Force = "B97-D3/def2-TZVP/SMD Force" B97_D3_def2_TZVP_SMD_Frequency_Analysis = "B97-D3/def2-TZVP/SMD Frequency Analysis" B97_D3_def2_TZVP_SMD_Frequency_Flattening_Geometry_Optimization = ( "B97-D3/def2-TZVP/SMD Frequency Flattening Geometry Optimization" ) B97_D3_def2_TZVP_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97-D3/def2-TZVP/SMD Frequency Flattening Transition State Geometry Optimization" B97_D3_def2_TZVP_SMD_Geometry_Optimization = ( "B97-D3/def2-TZVP/SMD Geometry Optimization" ) B97_D3_def2_TZVP_SMD_Single_Point = "B97-D3/def2-TZVP/SMD Single Point" B97_D3_def2_TZVP_SMD_Transition_State_Geometry_Optimization = ( "B97-D3/def2-TZVP/SMD Transition State Geometry Optimization" ) B97_D3_def2_TZVP_SMD_Unknown = "B97-D3/def2-TZVP/SMD Unknown" B97_D3_def2_TZVP_VACUUM_Force = "B97-D3/def2-TZVP/VACUUM Force" B97_D3_def2_TZVP_VACUUM_Frequency_Analysis = ( "B97-D3/def2-TZVP/VACUUM Frequency Analysis" ) B97_D3_def2_TZVP_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "B97-D3/def2-TZVP/VACUUM Frequency Flattening Geometry Optimization" ) B97_D3_def2_TZVP_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97-D3/def2-TZVP/VACUUM Frequency Flattening Transition State Geometry Optimization" B97_D3_def2_TZVP_VACUUM_Geometry_Optimization = ( "B97-D3/def2-TZVP/VACUUM Geometry Optimization" ) B97_D3_def2_TZVP_VACUUM_Single_Point = "B97-D3/def2-TZVP/VACUUM Single Point" B97_D3_def2_TZVP_VACUUM_Transition_State_Geometry_Optimization = ( "B97-D3/def2-TZVP/VACUUM Transition State Geometry Optimization" ) B97_D3_def2_TZVP_VACUUM_Unknown = "B97-D3/def2-TZVP/VACUUM Unknown" B97_D_6_31g_d_PCM_Force = "B97-D/6-31g*/PCM Force" B97_D_6_31g_d_PCM_Frequency_Analysis = "B97-D/6-31g*/PCM Frequency Analysis" B97_D_6_31g_d_PCM_Frequency_Flattening_Geometry_Optimization = ( "B97-D/6-31g*/PCM Frequency Flattening Geometry Optimization" ) B97_D_6_31g_d_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = ( "B97-D/6-31g*/PCM Frequency Flattening Transition State Geometry Optimization" ) B97_D_6_31g_d_PCM_Geometry_Optimization = "B97-D/6-31g*/PCM Geometry Optimization" B97_D_6_31g_d_PCM_Single_Point = "B97-D/6-31g*/PCM Single Point" B97_D_6_31g_d_PCM_Transition_State_Geometry_Optimization = ( "B97-D/6-31g*/PCM Transition State Geometry Optimization" ) B97_D_6_31g_d_PCM_Unknown = "B97-D/6-31g*/PCM Unknown" B97_D_6_31g_d_SMD_Force = "B97-D/6-31g*/SMD Force" B97_D_6_31g_d_SMD_Frequency_Analysis = "B97-D/6-31g*/SMD Frequency Analysis" B97_D_6_31g_d_SMD_Frequency_Flattening_Geometry_Optimization = ( "B97-D/6-31g*/SMD Frequency Flattening Geometry Optimization" ) B97_D_6_31g_d_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = ( "B97-D/6-31g*/SMD Frequency Flattening Transition State Geometry Optimization" ) B97_D_6_31g_d_SMD_Geometry_Optimization = "B97-D/6-31g*/SMD Geometry Optimization" B97_D_6_31g_d_SMD_Single_Point = "B97-D/6-31g*/SMD Single Point" B97_D_6_31g_d_SMD_Transition_State_Geometry_Optimization = ( "B97-D/6-31g*/SMD Transition State Geometry Optimization" ) B97_D_6_31g_d_SMD_Unknown = "B97-D/6-31g*/SMD Unknown" B97_D_6_31g_d_VACUUM_Force = "B97-D/6-31g*/VACUUM Force" B97_D_6_31g_d_VACUUM_Frequency_Analysis = "B97-D/6-31g*/VACUUM Frequency Analysis" B97_D_6_31g_d_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "B97-D/6-31g*/VACUUM Frequency Flattening Geometry Optimization" ) B97_D_6_31g_d_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97-D/6-31g*/VACUUM Frequency Flattening Transition State Geometry Optimization" B97_D_6_31g_d_VACUUM_Geometry_Optimization = ( "B97-D/6-31g*/VACUUM Geometry Optimization" ) B97_D_6_31g_d_VACUUM_Single_Point = "B97-D/6-31g*/VACUUM Single Point" B97_D_6_31g_d_VACUUM_Transition_State_Geometry_Optimization = ( "B97-D/6-31g*/VACUUM Transition State Geometry Optimization" ) B97_D_6_31g_d_VACUUM_Unknown = "B97-D/6-31g*/VACUUM Unknown" B97_D_def2_QZVPD_PCM_Force = "B97-D/def2-QZVPD/PCM Force" B97_D_def2_QZVPD_PCM_Frequency_Analysis = "B97-D/def2-QZVPD/PCM Frequency Analysis" B97_D_def2_QZVPD_PCM_Frequency_Flattening_Geometry_Optimization = ( "B97-D/def2-QZVPD/PCM Frequency Flattening Geometry Optimization" ) B97_D_def2_QZVPD_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97-D/def2-QZVPD/PCM Frequency Flattening Transition State Geometry Optimization" B97_D_def2_QZVPD_PCM_Geometry_Optimization = ( "B97-D/def2-QZVPD/PCM Geometry Optimization" ) B97_D_def2_QZVPD_PCM_Single_Point = "B97-D/def2-QZVPD/PCM Single Point" B97_D_def2_QZVPD_PCM_Transition_State_Geometry_Optimization = ( "B97-D/def2-QZVPD/PCM Transition State Geometry Optimization" ) B97_D_def2_QZVPD_PCM_Unknown = "B97-D/def2-QZVPD/PCM Unknown" B97_D_def2_QZVPD_SMD_Force = "B97-D/def2-QZVPD/SMD Force" B97_D_def2_QZVPD_SMD_Frequency_Analysis = "B97-D/def2-QZVPD/SMD Frequency Analysis" B97_D_def2_QZVPD_SMD_Frequency_Flattening_Geometry_Optimization = ( "B97-D/def2-QZVPD/SMD Frequency Flattening Geometry Optimization" ) B97_D_def2_QZVPD_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97-D/def2-QZVPD/SMD Frequency Flattening Transition State Geometry Optimization" B97_D_def2_QZVPD_SMD_Geometry_Optimization = ( "B97-D/def2-QZVPD/SMD Geometry Optimization" ) B97_D_def2_QZVPD_SMD_Single_Point = "B97-D/def2-QZVPD/SMD Single Point" B97_D_def2_QZVPD_SMD_Transition_State_Geometry_Optimization = ( "B97-D/def2-QZVPD/SMD Transition State Geometry Optimization" ) B97_D_def2_QZVPD_SMD_Unknown = "B97-D/def2-QZVPD/SMD Unknown" B97_D_def2_QZVPD_VACUUM_Force = "B97-D/def2-QZVPD/VACUUM Force" B97_D_def2_QZVPD_VACUUM_Frequency_Analysis = ( "B97-D/def2-QZVPD/VACUUM Frequency Analysis" ) B97_D_def2_QZVPD_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "B97-D/def2-QZVPD/VACUUM Frequency Flattening Geometry Optimization" ) B97_D_def2_QZVPD_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97-D/def2-QZVPD/VACUUM Frequency Flattening Transition State Geometry Optimization" B97_D_def2_QZVPD_VACUUM_Geometry_Optimization = ( "B97-D/def2-QZVPD/VACUUM Geometry Optimization" ) B97_D_def2_QZVPD_VACUUM_Single_Point = "B97-D/def2-QZVPD/VACUUM Single Point" B97_D_def2_QZVPD_VACUUM_Transition_State_Geometry_Optimization = ( "B97-D/def2-QZVPD/VACUUM Transition State Geometry Optimization" ) B97_D_def2_QZVPD_VACUUM_Unknown = "B97-D/def2-QZVPD/VACUUM Unknown" B97_D_def2_QZVPPD_PCM_Force = "B97-D/def2-QZVPPD/PCM Force" B97_D_def2_QZVPPD_PCM_Frequency_Analysis = ( "B97-D/def2-QZVPPD/PCM Frequency Analysis" ) B97_D_def2_QZVPPD_PCM_Frequency_Flattening_Geometry_Optimization = ( "B97-D/def2-QZVPPD/PCM Frequency Flattening Geometry Optimization" ) B97_D_def2_QZVPPD_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97-D/def2-QZVPPD/PCM Frequency Flattening Transition State Geometry Optimization" B97_D_def2_QZVPPD_PCM_Geometry_Optimization = ( "B97-D/def2-QZVPPD/PCM Geometry Optimization" ) B97_D_def2_QZVPPD_PCM_Single_Point = "B97-D/def2-QZVPPD/PCM Single Point" B97_D_def2_QZVPPD_PCM_Transition_State_Geometry_Optimization = ( "B97-D/def2-QZVPPD/PCM Transition State Geometry Optimization" ) B97_D_def2_QZVPPD_PCM_Unknown = "B97-D/def2-QZVPPD/PCM Unknown" B97_D_def2_QZVPPD_SMD_Force = "B97-D/def2-QZVPPD/SMD Force" B97_D_def2_QZVPPD_SMD_Frequency_Analysis = ( "B97-D/def2-QZVPPD/SMD Frequency Analysis" ) B97_D_def2_QZVPPD_SMD_Frequency_Flattening_Geometry_Optimization = ( "B97-D/def2-QZVPPD/SMD Frequency Flattening Geometry Optimization" ) B97_D_def2_QZVPPD_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97-D/def2-QZVPPD/SMD Frequency Flattening Transition State Geometry Optimization" B97_D_def2_QZVPPD_SMD_Geometry_Optimization = ( "B97-D/def2-QZVPPD/SMD Geometry Optimization" ) B97_D_def2_QZVPPD_SMD_Single_Point = "B97-D/def2-QZVPPD/SMD Single Point" B97_D_def2_QZVPPD_SMD_Transition_State_Geometry_Optimization = ( "B97-D/def2-QZVPPD/SMD Transition State Geometry Optimization" ) B97_D_def2_QZVPPD_SMD_Unknown = "B97-D/def2-QZVPPD/SMD Unknown" B97_D_def2_QZVPPD_VACUUM_Force = "B97-D/def2-QZVPPD/VACUUM Force" B97_D_def2_QZVPPD_VACUUM_Frequency_Analysis = ( "B97-D/def2-QZVPPD/VACUUM Frequency Analysis" ) B97_D_def2_QZVPPD_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "B97-D/def2-QZVPPD/VACUUM Frequency Flattening Geometry Optimization" ) B97_D_def2_QZVPPD_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97-D/def2-QZVPPD/VACUUM Frequency Flattening Transition State Geometry Optimization" B97_D_def2_QZVPPD_VACUUM_Geometry_Optimization = ( "B97-D/def2-QZVPPD/VACUUM Geometry Optimization" ) B97_D_def2_QZVPPD_VACUUM_Single_Point = "B97-D/def2-QZVPPD/VACUUM Single Point" B97_D_def2_QZVPPD_VACUUM_Transition_State_Geometry_Optimization = ( "B97-D/def2-QZVPPD/VACUUM Transition State Geometry Optimization" ) B97_D_def2_QZVPPD_VACUUM_Unknown = "B97-D/def2-QZVPPD/VACUUM Unknown" B97_D_def2_SVPD_PCM_Force = "B97-D/def2-SVPD/PCM Force" B97_D_def2_SVPD_PCM_Frequency_Analysis = "B97-D/def2-SVPD/PCM Frequency Analysis" B97_D_def2_SVPD_PCM_Frequency_Flattening_Geometry_Optimization = ( "B97-D/def2-SVPD/PCM Frequency Flattening Geometry Optimization" ) B97_D_def2_SVPD_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97-D/def2-SVPD/PCM Frequency Flattening Transition State Geometry Optimization" B97_D_def2_SVPD_PCM_Geometry_Optimization = ( "B97-D/def2-SVPD/PCM Geometry Optimization" ) B97_D_def2_SVPD_PCM_Single_Point = "B97-D/def2-SVPD/PCM Single Point" B97_D_def2_SVPD_PCM_Transition_State_Geometry_Optimization = ( "B97-D/def2-SVPD/PCM Transition State Geometry Optimization" ) B97_D_def2_SVPD_PCM_Unknown = "B97-D/def2-SVPD/PCM Unknown" B97_D_def2_SVPD_SMD_Force = "B97-D/def2-SVPD/SMD Force" B97_D_def2_SVPD_SMD_Frequency_Analysis = "B97-D/def2-SVPD/SMD Frequency Analysis" B97_D_def2_SVPD_SMD_Frequency_Flattening_Geometry_Optimization = ( "B97-D/def2-SVPD/SMD Frequency Flattening Geometry Optimization" ) B97_D_def2_SVPD_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97-D/def2-SVPD/SMD Frequency Flattening Transition State Geometry Optimization" B97_D_def2_SVPD_SMD_Geometry_Optimization = ( "B97-D/def2-SVPD/SMD Geometry Optimization" ) B97_D_def2_SVPD_SMD_Single_Point = "B97-D/def2-SVPD/SMD Single Point" B97_D_def2_SVPD_SMD_Transition_State_Geometry_Optimization = ( "B97-D/def2-SVPD/SMD Transition State Geometry Optimization" ) B97_D_def2_SVPD_SMD_Unknown = "B97-D/def2-SVPD/SMD Unknown" B97_D_def2_SVPD_VACUUM_Force = "B97-D/def2-SVPD/VACUUM Force" B97_D_def2_SVPD_VACUUM_Frequency_Analysis = ( "B97-D/def2-SVPD/VACUUM Frequency Analysis" ) B97_D_def2_SVPD_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "B97-D/def2-SVPD/VACUUM Frequency Flattening Geometry Optimization" ) B97_D_def2_SVPD_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97-D/def2-SVPD/VACUUM Frequency Flattening Transition State Geometry Optimization" B97_D_def2_SVPD_VACUUM_Geometry_Optimization = ( "B97-D/def2-SVPD/VACUUM Geometry Optimization" ) B97_D_def2_SVPD_VACUUM_Single_Point = "B97-D/def2-SVPD/VACUUM Single Point" B97_D_def2_SVPD_VACUUM_Transition_State_Geometry_Optimization = ( "B97-D/def2-SVPD/VACUUM Transition State Geometry Optimization" ) B97_D_def2_SVPD_VACUUM_Unknown = "B97-D/def2-SVPD/VACUUM Unknown" B97_D_def2_TZVPD_PCM_Force = "B97-D/def2-TZVPD/PCM Force" B97_D_def2_TZVPD_PCM_Frequency_Analysis = "B97-D/def2-TZVPD/PCM Frequency Analysis" B97_D_def2_TZVPD_PCM_Frequency_Flattening_Geometry_Optimization = ( "B97-D/def2-TZVPD/PCM Frequency Flattening Geometry Optimization" ) B97_D_def2_TZVPD_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97-D/def2-TZVPD/PCM Frequency Flattening Transition State Geometry Optimization" B97_D_def2_TZVPD_PCM_Geometry_Optimization = ( "B97-D/def2-TZVPD/PCM Geometry Optimization" ) B97_D_def2_TZVPD_PCM_Single_Point = "B97-D/def2-TZVPD/PCM Single Point" B97_D_def2_TZVPD_PCM_Transition_State_Geometry_Optimization = ( "B97-D/def2-TZVPD/PCM Transition State Geometry Optimization" ) B97_D_def2_TZVPD_PCM_Unknown = "B97-D/def2-TZVPD/PCM Unknown" B97_D_def2_TZVPD_SMD_Force = "B97-D/def2-TZVPD/SMD Force" B97_D_def2_TZVPD_SMD_Frequency_Analysis = "B97-D/def2-TZVPD/SMD Frequency Analysis" B97_D_def2_TZVPD_SMD_Frequency_Flattening_Geometry_Optimization = ( "B97-D/def2-TZVPD/SMD Frequency Flattening Geometry Optimization" ) B97_D_def2_TZVPD_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97-D/def2-TZVPD/SMD Frequency Flattening Transition State Geometry Optimization" B97_D_def2_TZVPD_SMD_Geometry_Optimization = ( "B97-D/def2-TZVPD/SMD Geometry Optimization" ) B97_D_def2_TZVPD_SMD_Single_Point = "B97-D/def2-TZVPD/SMD Single Point" B97_D_def2_TZVPD_SMD_Transition_State_Geometry_Optimization = ( "B97-D/def2-TZVPD/SMD Transition State Geometry Optimization" ) B97_D_def2_TZVPD_SMD_Unknown = "B97-D/def2-TZVPD/SMD Unknown" B97_D_def2_TZVPD_VACUUM_Force = "B97-D/def2-TZVPD/VACUUM Force" B97_D_def2_TZVPD_VACUUM_Frequency_Analysis = ( "B97-D/def2-TZVPD/VACUUM Frequency Analysis" ) B97_D_def2_TZVPD_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "B97-D/def2-TZVPD/VACUUM Frequency Flattening Geometry Optimization" ) B97_D_def2_TZVPD_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97-D/def2-TZVPD/VACUUM Frequency Flattening Transition State Geometry Optimization" B97_D_def2_TZVPD_VACUUM_Geometry_Optimization = ( "B97-D/def2-TZVPD/VACUUM Geometry Optimization" ) B97_D_def2_TZVPD_VACUUM_Single_Point = "B97-D/def2-TZVPD/VACUUM Single Point" B97_D_def2_TZVPD_VACUUM_Transition_State_Geometry_Optimization = ( "B97-D/def2-TZVPD/VACUUM Transition State Geometry Optimization" ) B97_D_def2_TZVPD_VACUUM_Unknown = "B97-D/def2-TZVPD/VACUUM Unknown" B97_D_def2_TZVPPD_PCM_Force = "B97-D/def2-TZVPPD/PCM Force" B97_D_def2_TZVPPD_PCM_Frequency_Analysis = ( "B97-D/def2-TZVPPD/PCM Frequency Analysis" ) B97_D_def2_TZVPPD_PCM_Frequency_Flattening_Geometry_Optimization = ( "B97-D/def2-TZVPPD/PCM Frequency Flattening Geometry Optimization" ) B97_D_def2_TZVPPD_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97-D/def2-TZVPPD/PCM Frequency Flattening Transition State Geometry Optimization" B97_D_def2_TZVPPD_PCM_Geometry_Optimization = ( "B97-D/def2-TZVPPD/PCM Geometry Optimization" ) B97_D_def2_TZVPPD_PCM_Single_Point = "B97-D/def2-TZVPPD/PCM Single Point" B97_D_def2_TZVPPD_PCM_Transition_State_Geometry_Optimization = ( "B97-D/def2-TZVPPD/PCM Transition State Geometry Optimization" ) B97_D_def2_TZVPPD_PCM_Unknown = "B97-D/def2-TZVPPD/PCM Unknown" B97_D_def2_TZVPPD_SMD_Force = "B97-D/def2-TZVPPD/SMD Force" B97_D_def2_TZVPPD_SMD_Frequency_Analysis = ( "B97-D/def2-TZVPPD/SMD Frequency Analysis" ) B97_D_def2_TZVPPD_SMD_Frequency_Flattening_Geometry_Optimization = ( "B97-D/def2-TZVPPD/SMD Frequency Flattening Geometry Optimization" ) B97_D_def2_TZVPPD_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97-D/def2-TZVPPD/SMD Frequency Flattening Transition State Geometry Optimization" B97_D_def2_TZVPPD_SMD_Geometry_Optimization = ( "B97-D/def2-TZVPPD/SMD Geometry Optimization" ) B97_D_def2_TZVPPD_SMD_Single_Point = "B97-D/def2-TZVPPD/SMD Single Point" B97_D_def2_TZVPPD_SMD_Transition_State_Geometry_Optimization = ( "B97-D/def2-TZVPPD/SMD Transition State Geometry Optimization" ) B97_D_def2_TZVPPD_SMD_Unknown = "B97-D/def2-TZVPPD/SMD Unknown" B97_D_def2_TZVPPD_VACUUM_Force = "B97-D/def2-TZVPPD/VACUUM Force" B97_D_def2_TZVPPD_VACUUM_Frequency_Analysis = ( "B97-D/def2-TZVPPD/VACUUM Frequency Analysis" ) B97_D_def2_TZVPPD_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "B97-D/def2-TZVPPD/VACUUM Frequency Flattening Geometry Optimization" ) B97_D_def2_TZVPPD_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97-D/def2-TZVPPD/VACUUM Frequency Flattening Transition State Geometry Optimization" B97_D_def2_TZVPPD_VACUUM_Geometry_Optimization = ( "B97-D/def2-TZVPPD/VACUUM Geometry Optimization" ) B97_D_def2_TZVPPD_VACUUM_Single_Point = "B97-D/def2-TZVPPD/VACUUM Single Point" B97_D_def2_TZVPPD_VACUUM_Transition_State_Geometry_Optimization = ( "B97-D/def2-TZVPPD/VACUUM Transition State Geometry Optimization" ) B97_D_def2_TZVPPD_VACUUM_Unknown = "B97-D/def2-TZVPPD/VACUUM Unknown" B97_D_def2_TZVPP_PCM_Force = "B97-D/def2-TZVPP/PCM Force" B97_D_def2_TZVPP_PCM_Frequency_Analysis = "B97-D/def2-TZVPP/PCM Frequency Analysis" B97_D_def2_TZVPP_PCM_Frequency_Flattening_Geometry_Optimization = ( "B97-D/def2-TZVPP/PCM Frequency Flattening Geometry Optimization" ) B97_D_def2_TZVPP_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97-D/def2-TZVPP/PCM Frequency Flattening Transition State Geometry Optimization" B97_D_def2_TZVPP_PCM_Geometry_Optimization = ( "B97-D/def2-TZVPP/PCM Geometry Optimization" ) B97_D_def2_TZVPP_PCM_Single_Point = "B97-D/def2-TZVPP/PCM Single Point" B97_D_def2_TZVPP_PCM_Transition_State_Geometry_Optimization = ( "B97-D/def2-TZVPP/PCM Transition State Geometry Optimization" ) B97_D_def2_TZVPP_PCM_Unknown = "B97-D/def2-TZVPP/PCM Unknown" B97_D_def2_TZVPP_SMD_Force = "B97-D/def2-TZVPP/SMD Force" B97_D_def2_TZVPP_SMD_Frequency_Analysis = "B97-D/def2-TZVPP/SMD Frequency Analysis" B97_D_def2_TZVPP_SMD_Frequency_Flattening_Geometry_Optimization = ( "B97-D/def2-TZVPP/SMD Frequency Flattening Geometry Optimization" ) B97_D_def2_TZVPP_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97-D/def2-TZVPP/SMD Frequency Flattening Transition State Geometry Optimization" B97_D_def2_TZVPP_SMD_Geometry_Optimization = ( "B97-D/def2-TZVPP/SMD Geometry Optimization" ) B97_D_def2_TZVPP_SMD_Single_Point = "B97-D/def2-TZVPP/SMD Single Point" B97_D_def2_TZVPP_SMD_Transition_State_Geometry_Optimization = ( "B97-D/def2-TZVPP/SMD Transition State Geometry Optimization" ) B97_D_def2_TZVPP_SMD_Unknown = "B97-D/def2-TZVPP/SMD Unknown" B97_D_def2_TZVPP_VACUUM_Force = "B97-D/def2-TZVPP/VACUUM Force" B97_D_def2_TZVPP_VACUUM_Frequency_Analysis = ( "B97-D/def2-TZVPP/VACUUM Frequency Analysis" ) B97_D_def2_TZVPP_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "B97-D/def2-TZVPP/VACUUM Frequency Flattening Geometry Optimization" ) B97_D_def2_TZVPP_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97-D/def2-TZVPP/VACUUM Frequency Flattening Transition State Geometry Optimization" B97_D_def2_TZVPP_VACUUM_Geometry_Optimization = ( "B97-D/def2-TZVPP/VACUUM Geometry Optimization" ) B97_D_def2_TZVPP_VACUUM_Single_Point = "B97-D/def2-TZVPP/VACUUM Single Point" B97_D_def2_TZVPP_VACUUM_Transition_State_Geometry_Optimization = ( "B97-D/def2-TZVPP/VACUUM Transition State Geometry Optimization" ) B97_D_def2_TZVPP_VACUUM_Unknown = "B97-D/def2-TZVPP/VACUUM Unknown" B97_D_def2_TZVP_PCM_Force = "B97-D/def2-TZVP/PCM Force" B97_D_def2_TZVP_PCM_Frequency_Analysis = "B97-D/def2-TZVP/PCM Frequency Analysis" B97_D_def2_TZVP_PCM_Frequency_Flattening_Geometry_Optimization = ( "B97-D/def2-TZVP/PCM Frequency Flattening Geometry Optimization" ) B97_D_def2_TZVP_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97-D/def2-TZVP/PCM Frequency Flattening Transition State Geometry Optimization" B97_D_def2_TZVP_PCM_Geometry_Optimization = ( "B97-D/def2-TZVP/PCM Geometry Optimization" ) B97_D_def2_TZVP_PCM_Single_Point = "B97-D/def2-TZVP/PCM Single Point" B97_D_def2_TZVP_PCM_Transition_State_Geometry_Optimization = ( "B97-D/def2-TZVP/PCM Transition State Geometry Optimization" ) B97_D_def2_TZVP_PCM_Unknown = "B97-D/def2-TZVP/PCM Unknown" B97_D_def2_TZVP_SMD_Force = "B97-D/def2-TZVP/SMD Force" B97_D_def2_TZVP_SMD_Frequency_Analysis = "B97-D/def2-TZVP/SMD Frequency Analysis" B97_D_def2_TZVP_SMD_Frequency_Flattening_Geometry_Optimization = ( "B97-D/def2-TZVP/SMD Frequency Flattening Geometry Optimization" ) B97_D_def2_TZVP_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97-D/def2-TZVP/SMD Frequency Flattening Transition State Geometry Optimization" B97_D_def2_TZVP_SMD_Geometry_Optimization = ( "B97-D/def2-TZVP/SMD Geometry Optimization" ) B97_D_def2_TZVP_SMD_Single_Point = "B97-D/def2-TZVP/SMD Single Point" B97_D_def2_TZVP_SMD_Transition_State_Geometry_Optimization = ( "B97-D/def2-TZVP/SMD Transition State Geometry Optimization" ) B97_D_def2_TZVP_SMD_Unknown = "B97-D/def2-TZVP/SMD Unknown" B97_D_def2_TZVP_VACUUM_Force = "B97-D/def2-TZVP/VACUUM Force" B97_D_def2_TZVP_VACUUM_Frequency_Analysis = ( "B97-D/def2-TZVP/VACUUM Frequency Analysis" ) B97_D_def2_TZVP_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "B97-D/def2-TZVP/VACUUM Frequency Flattening Geometry Optimization" ) B97_D_def2_TZVP_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "B97-D/def2-TZVP/VACUUM Frequency Flattening Transition State Geometry Optimization" B97_D_def2_TZVP_VACUUM_Geometry_Optimization = ( "B97-D/def2-TZVP/VACUUM Geometry Optimization" ) B97_D_def2_TZVP_VACUUM_Single_Point = "B97-D/def2-TZVP/VACUUM Single Point" B97_D_def2_TZVP_VACUUM_Transition_State_Geometry_Optimization = ( "B97-D/def2-TZVP/VACUUM Transition State Geometry Optimization" ) B97_D_def2_TZVP_VACUUM_Unknown = "B97-D/def2-TZVP/VACUUM Unknown" MN12_L_6_31g_d_PCM_Force = "MN12-L/6-31g*/PCM Force" MN12_L_6_31g_d_PCM_Frequency_Analysis = "MN12-L/6-31g*/PCM Frequency Analysis" MN12_L_6_31g_d_PCM_Frequency_Flattening_Geometry_Optimization = ( "MN12-L/6-31g*/PCM Frequency Flattening Geometry Optimization" ) MN12_L_6_31g_d_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = ( "MN12-L/6-31g*/PCM Frequency Flattening Transition State Geometry Optimization" ) MN12_L_6_31g_d_PCM_Geometry_Optimization = "MN12-L/6-31g*/PCM Geometry Optimization" MN12_L_6_31g_d_PCM_Single_Point = "MN12-L/6-31g*/PCM Single Point" MN12_L_6_31g_d_PCM_Transition_State_Geometry_Optimization = ( "MN12-L/6-31g*/PCM Transition State Geometry Optimization" ) MN12_L_6_31g_d_PCM_Unknown = "MN12-L/6-31g*/PCM Unknown" MN12_L_6_31g_d_SMD_Force = "MN12-L/6-31g*/SMD Force" MN12_L_6_31g_d_SMD_Frequency_Analysis = "MN12-L/6-31g*/SMD Frequency Analysis" MN12_L_6_31g_d_SMD_Frequency_Flattening_Geometry_Optimization = ( "MN12-L/6-31g*/SMD Frequency Flattening Geometry Optimization" ) MN12_L_6_31g_d_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = ( "MN12-L/6-31g*/SMD Frequency Flattening Transition State Geometry Optimization" ) MN12_L_6_31g_d_SMD_Geometry_Optimization = "MN12-L/6-31g*/SMD Geometry Optimization" MN12_L_6_31g_d_SMD_Single_Point = "MN12-L/6-31g*/SMD Single Point" MN12_L_6_31g_d_SMD_Transition_State_Geometry_Optimization = ( "MN12-L/6-31g*/SMD Transition State Geometry Optimization" ) MN12_L_6_31g_d_SMD_Unknown = "MN12-L/6-31g*/SMD Unknown" MN12_L_6_31g_d_VACUUM_Force = "MN12-L/6-31g*/VACUUM Force" MN12_L_6_31g_d_VACUUM_Frequency_Analysis = "MN12-L/6-31g*/VACUUM Frequency Analysis" MN12_L_6_31g_d_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "MN12-L/6-31g*/VACUUM Frequency Flattening Geometry Optimization" ) MN12_L_6_31g_d_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "MN12-L/6-31g*/VACUUM Frequency Flattening Transition State Geometry Optimization" MN12_L_6_31g_d_VACUUM_Geometry_Optimization = ( "MN12-L/6-31g*/VACUUM Geometry Optimization" ) MN12_L_6_31g_d_VACUUM_Single_Point = "MN12-L/6-31g*/VACUUM Single Point" MN12_L_6_31g_d_VACUUM_Transition_State_Geometry_Optimization = ( "MN12-L/6-31g*/VACUUM Transition State Geometry Optimization" ) MN12_L_6_31g_d_VACUUM_Unknown = "MN12-L/6-31g*/VACUUM Unknown" MN12_L_def2_QZVPD_PCM_Force = "MN12-L/def2-QZVPD/PCM Force" MN12_L_def2_QZVPD_PCM_Frequency_Analysis = ( "MN12-L/def2-QZVPD/PCM Frequency Analysis" ) MN12_L_def2_QZVPD_PCM_Frequency_Flattening_Geometry_Optimization = ( "MN12-L/def2-QZVPD/PCM Frequency Flattening Geometry Optimization" ) MN12_L_def2_QZVPD_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "MN12-L/def2-QZVPD/PCM Frequency Flattening Transition State Geometry Optimization" MN12_L_def2_QZVPD_PCM_Geometry_Optimization = ( "MN12-L/def2-QZVPD/PCM Geometry Optimization" ) MN12_L_def2_QZVPD_PCM_Single_Point = "MN12-L/def2-QZVPD/PCM Single Point" MN12_L_def2_QZVPD_PCM_Transition_State_Geometry_Optimization = ( "MN12-L/def2-QZVPD/PCM Transition State Geometry Optimization" ) MN12_L_def2_QZVPD_PCM_Unknown = "MN12-L/def2-QZVPD/PCM Unknown" MN12_L_def2_QZVPD_SMD_Force = "MN12-L/def2-QZVPD/SMD Force" MN12_L_def2_QZVPD_SMD_Frequency_Analysis = ( "MN12-L/def2-QZVPD/SMD Frequency Analysis" ) MN12_L_def2_QZVPD_SMD_Frequency_Flattening_Geometry_Optimization = ( "MN12-L/def2-QZVPD/SMD Frequency Flattening Geometry Optimization" ) MN12_L_def2_QZVPD_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "MN12-L/def2-QZVPD/SMD Frequency Flattening Transition State Geometry Optimization" MN12_L_def2_QZVPD_SMD_Geometry_Optimization = ( "MN12-L/def2-QZVPD/SMD Geometry Optimization" ) MN12_L_def2_QZVPD_SMD_Single_Point = "MN12-L/def2-QZVPD/SMD Single Point" MN12_L_def2_QZVPD_SMD_Transition_State_Geometry_Optimization = ( "MN12-L/def2-QZVPD/SMD Transition State Geometry Optimization" ) MN12_L_def2_QZVPD_SMD_Unknown = "MN12-L/def2-QZVPD/SMD Unknown" MN12_L_def2_QZVPD_VACUUM_Force = "MN12-L/def2-QZVPD/VACUUM Force" MN12_L_def2_QZVPD_VACUUM_Frequency_Analysis = ( "MN12-L/def2-QZVPD/VACUUM Frequency Analysis" ) MN12_L_def2_QZVPD_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "MN12-L/def2-QZVPD/VACUUM Frequency Flattening Geometry Optimization" ) MN12_L_def2_QZVPD_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "MN12-L/def2-QZVPD/VACUUM Frequency Flattening Transition State Geometry Optimization" MN12_L_def2_QZVPD_VACUUM_Geometry_Optimization = ( "MN12-L/def2-QZVPD/VACUUM Geometry Optimization" ) MN12_L_def2_QZVPD_VACUUM_Single_Point = "MN12-L/def2-QZVPD/VACUUM Single Point" MN12_L_def2_QZVPD_VACUUM_Transition_State_Geometry_Optimization = ( "MN12-L/def2-QZVPD/VACUUM Transition State Geometry Optimization" ) MN12_L_def2_QZVPD_VACUUM_Unknown = "MN12-L/def2-QZVPD/VACUUM Unknown" MN12_L_def2_QZVPPD_PCM_Force = "MN12-L/def2-QZVPPD/PCM Force" MN12_L_def2_QZVPPD_PCM_Frequency_Analysis = ( "MN12-L/def2-QZVPPD/PCM Frequency Analysis" ) MN12_L_def2_QZVPPD_PCM_Frequency_Flattening_Geometry_Optimization = ( "MN12-L/def2-QZVPPD/PCM Frequency Flattening Geometry Optimization" ) MN12_L_def2_QZVPPD_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "MN12-L/def2-QZVPPD/PCM Frequency Flattening Transition State Geometry Optimization" MN12_L_def2_QZVPPD_PCM_Geometry_Optimization = ( "MN12-L/def2-QZVPPD/PCM Geometry Optimization" ) MN12_L_def2_QZVPPD_PCM_Single_Point = "MN12-L/def2-QZVPPD/PCM Single Point" MN12_L_def2_QZVPPD_PCM_Transition_State_Geometry_Optimization = ( "MN12-L/def2-QZVPPD/PCM Transition State Geometry Optimization" ) MN12_L_def2_QZVPPD_PCM_Unknown = "MN12-L/def2-QZVPPD/PCM Unknown" MN12_L_def2_QZVPPD_SMD_Force = "MN12-L/def2-QZVPPD/SMD Force" MN12_L_def2_QZVPPD_SMD_Frequency_Analysis = ( "MN12-L/def2-QZVPPD/SMD Frequency Analysis" ) MN12_L_def2_QZVPPD_SMD_Frequency_Flattening_Geometry_Optimization = ( "MN12-L/def2-QZVPPD/SMD Frequency Flattening Geometry Optimization" ) MN12_L_def2_QZVPPD_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "MN12-L/def2-QZVPPD/SMD Frequency Flattening Transition State Geometry Optimization" MN12_L_def2_QZVPPD_SMD_Geometry_Optimization = ( "MN12-L/def2-QZVPPD/SMD Geometry Optimization" ) MN12_L_def2_QZVPPD_SMD_Single_Point = "MN12-L/def2-QZVPPD/SMD Single Point" MN12_L_def2_QZVPPD_SMD_Transition_State_Geometry_Optimization = ( "MN12-L/def2-QZVPPD/SMD Transition State Geometry Optimization" ) MN12_L_def2_QZVPPD_SMD_Unknown = "MN12-L/def2-QZVPPD/SMD Unknown" MN12_L_def2_QZVPPD_VACUUM_Force = "MN12-L/def2-QZVPPD/VACUUM Force" MN12_L_def2_QZVPPD_VACUUM_Frequency_Analysis = ( "MN12-L/def2-QZVPPD/VACUUM Frequency Analysis" ) MN12_L_def2_QZVPPD_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "MN12-L/def2-QZVPPD/VACUUM Frequency Flattening Geometry Optimization" ) MN12_L_def2_QZVPPD_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "MN12-L/def2-QZVPPD/VACUUM Frequency Flattening Transition State Geometry Optimization" MN12_L_def2_QZVPPD_VACUUM_Geometry_Optimization = ( "MN12-L/def2-QZVPPD/VACUUM Geometry Optimization" ) MN12_L_def2_QZVPPD_VACUUM_Single_Point = "MN12-L/def2-QZVPPD/VACUUM Single Point" MN12_L_def2_QZVPPD_VACUUM_Transition_State_Geometry_Optimization = ( "MN12-L/def2-QZVPPD/VACUUM Transition State Geometry Optimization" ) MN12_L_def2_QZVPPD_VACUUM_Unknown = "MN12-L/def2-QZVPPD/VACUUM Unknown" MN12_L_def2_SVPD_PCM_Force = "MN12-L/def2-SVPD/PCM Force" MN12_L_def2_SVPD_PCM_Frequency_Analysis = "MN12-L/def2-SVPD/PCM Frequency Analysis" MN12_L_def2_SVPD_PCM_Frequency_Flattening_Geometry_Optimization = ( "MN12-L/def2-SVPD/PCM Frequency Flattening Geometry Optimization" ) MN12_L_def2_SVPD_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "MN12-L/def2-SVPD/PCM Frequency Flattening Transition State Geometry Optimization" MN12_L_def2_SVPD_PCM_Geometry_Optimization = ( "MN12-L/def2-SVPD/PCM Geometry Optimization" ) MN12_L_def2_SVPD_PCM_Single_Point = "MN12-L/def2-SVPD/PCM Single Point" MN12_L_def2_SVPD_PCM_Transition_State_Geometry_Optimization = ( "MN12-L/def2-SVPD/PCM Transition State Geometry Optimization" ) MN12_L_def2_SVPD_PCM_Unknown = "MN12-L/def2-SVPD/PCM Unknown" MN12_L_def2_SVPD_SMD_Force = "MN12-L/def2-SVPD/SMD Force" MN12_L_def2_SVPD_SMD_Frequency_Analysis = "MN12-L/def2-SVPD/SMD Frequency Analysis" MN12_L_def2_SVPD_SMD_Frequency_Flattening_Geometry_Optimization = ( "MN12-L/def2-SVPD/SMD Frequency Flattening Geometry Optimization" ) MN12_L_def2_SVPD_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "MN12-L/def2-SVPD/SMD Frequency Flattening Transition State Geometry Optimization" MN12_L_def2_SVPD_SMD_Geometry_Optimization = ( "MN12-L/def2-SVPD/SMD Geometry Optimization" ) MN12_L_def2_SVPD_SMD_Single_Point = "MN12-L/def2-SVPD/SMD Single Point" MN12_L_def2_SVPD_SMD_Transition_State_Geometry_Optimization = ( "MN12-L/def2-SVPD/SMD Transition State Geometry Optimization" ) MN12_L_def2_SVPD_SMD_Unknown = "MN12-L/def2-SVPD/SMD Unknown" MN12_L_def2_SVPD_VACUUM_Force = "MN12-L/def2-SVPD/VACUUM Force" MN12_L_def2_SVPD_VACUUM_Frequency_Analysis = ( "MN12-L/def2-SVPD/VACUUM Frequency Analysis" ) MN12_L_def2_SVPD_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "MN12-L/def2-SVPD/VACUUM Frequency Flattening Geometry Optimization" ) MN12_L_def2_SVPD_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "MN12-L/def2-SVPD/VACUUM Frequency Flattening Transition State Geometry Optimization" MN12_L_def2_SVPD_VACUUM_Geometry_Optimization = ( "MN12-L/def2-SVPD/VACUUM Geometry Optimization" ) MN12_L_def2_SVPD_VACUUM_Single_Point = "MN12-L/def2-SVPD/VACUUM Single Point" MN12_L_def2_SVPD_VACUUM_Transition_State_Geometry_Optimization = ( "MN12-L/def2-SVPD/VACUUM Transition State Geometry Optimization" ) MN12_L_def2_SVPD_VACUUM_Unknown = "MN12-L/def2-SVPD/VACUUM Unknown" MN12_L_def2_TZVPD_PCM_Force = "MN12-L/def2-TZVPD/PCM Force" MN12_L_def2_TZVPD_PCM_Frequency_Analysis = ( "MN12-L/def2-TZVPD/PCM Frequency Analysis" ) MN12_L_def2_TZVPD_PCM_Frequency_Flattening_Geometry_Optimization = ( "MN12-L/def2-TZVPD/PCM Frequency Flattening Geometry Optimization" ) MN12_L_def2_TZVPD_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "MN12-L/def2-TZVPD/PCM Frequency Flattening Transition State Geometry Optimization" MN12_L_def2_TZVPD_PCM_Geometry_Optimization = ( "MN12-L/def2-TZVPD/PCM Geometry Optimization" ) MN12_L_def2_TZVPD_PCM_Single_Point = "MN12-L/def2-TZVPD/PCM Single Point" MN12_L_def2_TZVPD_PCM_Transition_State_Geometry_Optimization = ( "MN12-L/def2-TZVPD/PCM Transition State Geometry Optimization" ) MN12_L_def2_TZVPD_PCM_Unknown = "MN12-L/def2-TZVPD/PCM Unknown" MN12_L_def2_TZVPD_SMD_Force = "MN12-L/def2-TZVPD/SMD Force" MN12_L_def2_TZVPD_SMD_Frequency_Analysis = ( "MN12-L/def2-TZVPD/SMD Frequency Analysis" ) MN12_L_def2_TZVPD_SMD_Frequency_Flattening_Geometry_Optimization = ( "MN12-L/def2-TZVPD/SMD Frequency Flattening Geometry Optimization" ) MN12_L_def2_TZVPD_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "MN12-L/def2-TZVPD/SMD Frequency Flattening Transition State Geometry Optimization" MN12_L_def2_TZVPD_SMD_Geometry_Optimization = ( "MN12-L/def2-TZVPD/SMD Geometry Optimization" ) MN12_L_def2_TZVPD_SMD_Single_Point = "MN12-L/def2-TZVPD/SMD Single Point" MN12_L_def2_TZVPD_SMD_Transition_State_Geometry_Optimization = ( "MN12-L/def2-TZVPD/SMD Transition State Geometry Optimization" ) MN12_L_def2_TZVPD_SMD_Unknown = "MN12-L/def2-TZVPD/SMD Unknown" MN12_L_def2_TZVPD_VACUUM_Force = "MN12-L/def2-TZVPD/VACUUM Force" MN12_L_def2_TZVPD_VACUUM_Frequency_Analysis = ( "MN12-L/def2-TZVPD/VACUUM Frequency Analysis" ) MN12_L_def2_TZVPD_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "MN12-L/def2-TZVPD/VACUUM Frequency Flattening Geometry Optimization" ) MN12_L_def2_TZVPD_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "MN12-L/def2-TZVPD/VACUUM Frequency Flattening Transition State Geometry Optimization" MN12_L_def2_TZVPD_VACUUM_Geometry_Optimization = ( "MN12-L/def2-TZVPD/VACUUM Geometry Optimization" ) MN12_L_def2_TZVPD_VACUUM_Single_Point = "MN12-L/def2-TZVPD/VACUUM Single Point" MN12_L_def2_TZVPD_VACUUM_Transition_State_Geometry_Optimization = ( "MN12-L/def2-TZVPD/VACUUM Transition State Geometry Optimization" ) MN12_L_def2_TZVPD_VACUUM_Unknown = "MN12-L/def2-TZVPD/VACUUM Unknown" MN12_L_def2_TZVPPD_PCM_Force = "MN12-L/def2-TZVPPD/PCM Force" MN12_L_def2_TZVPPD_PCM_Frequency_Analysis = ( "MN12-L/def2-TZVPPD/PCM Frequency Analysis" ) MN12_L_def2_TZVPPD_PCM_Frequency_Flattening_Geometry_Optimization = ( "MN12-L/def2-TZVPPD/PCM Frequency Flattening Geometry Optimization" ) MN12_L_def2_TZVPPD_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "MN12-L/def2-TZVPPD/PCM Frequency Flattening Transition State Geometry Optimization" MN12_L_def2_TZVPPD_PCM_Geometry_Optimization = ( "MN12-L/def2-TZVPPD/PCM Geometry Optimization" ) MN12_L_def2_TZVPPD_PCM_Single_Point = "MN12-L/def2-TZVPPD/PCM Single Point" MN12_L_def2_TZVPPD_PCM_Transition_State_Geometry_Optimization = ( "MN12-L/def2-TZVPPD/PCM Transition State Geometry Optimization" ) MN12_L_def2_TZVPPD_PCM_Unknown = "MN12-L/def2-TZVPPD/PCM Unknown" MN12_L_def2_TZVPPD_SMD_Force = "MN12-L/def2-TZVPPD/SMD Force" MN12_L_def2_TZVPPD_SMD_Frequency_Analysis = ( "MN12-L/def2-TZVPPD/SMD Frequency Analysis" ) MN12_L_def2_TZVPPD_SMD_Frequency_Flattening_Geometry_Optimization = ( "MN12-L/def2-TZVPPD/SMD Frequency Flattening Geometry Optimization" ) MN12_L_def2_TZVPPD_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "MN12-L/def2-TZVPPD/SMD Frequency Flattening Transition State Geometry Optimization" MN12_L_def2_TZVPPD_SMD_Geometry_Optimization = ( "MN12-L/def2-TZVPPD/SMD Geometry Optimization" ) MN12_L_def2_TZVPPD_SMD_Single_Point = "MN12-L/def2-TZVPPD/SMD Single Point" MN12_L_def2_TZVPPD_SMD_Transition_State_Geometry_Optimization = ( "MN12-L/def2-TZVPPD/SMD Transition State Geometry Optimization" ) MN12_L_def2_TZVPPD_SMD_Unknown = "MN12-L/def2-TZVPPD/SMD Unknown" MN12_L_def2_TZVPPD_VACUUM_Force = "MN12-L/def2-TZVPPD/VACUUM Force" MN12_L_def2_TZVPPD_VACUUM_Frequency_Analysis = ( "MN12-L/def2-TZVPPD/VACUUM Frequency Analysis" ) MN12_L_def2_TZVPPD_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "MN12-L/def2-TZVPPD/VACUUM Frequency Flattening Geometry Optimization" ) MN12_L_def2_TZVPPD_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "MN12-L/def2-TZVPPD/VACUUM Frequency Flattening Transition State Geometry Optimization" MN12_L_def2_TZVPPD_VACUUM_Geometry_Optimization = ( "MN12-L/def2-TZVPPD/VACUUM Geometry Optimization" ) MN12_L_def2_TZVPPD_VACUUM_Single_Point = "MN12-L/def2-TZVPPD/VACUUM Single Point" MN12_L_def2_TZVPPD_VACUUM_Transition_State_Geometry_Optimization = ( "MN12-L/def2-TZVPPD/VACUUM Transition State Geometry Optimization" ) MN12_L_def2_TZVPPD_VACUUM_Unknown = "MN12-L/def2-TZVPPD/VACUUM Unknown" MN12_L_def2_TZVPP_PCM_Force = "MN12-L/def2-TZVPP/PCM Force" MN12_L_def2_TZVPP_PCM_Frequency_Analysis = ( "MN12-L/def2-TZVPP/PCM Frequency Analysis" ) MN12_L_def2_TZVPP_PCM_Frequency_Flattening_Geometry_Optimization = ( "MN12-L/def2-TZVPP/PCM Frequency Flattening Geometry Optimization" ) MN12_L_def2_TZVPP_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "MN12-L/def2-TZVPP/PCM Frequency Flattening Transition State Geometry Optimization" MN12_L_def2_TZVPP_PCM_Geometry_Optimization = ( "MN12-L/def2-TZVPP/PCM Geometry Optimization" ) MN12_L_def2_TZVPP_PCM_Single_Point = "MN12-L/def2-TZVPP/PCM Single Point" MN12_L_def2_TZVPP_PCM_Transition_State_Geometry_Optimization = ( "MN12-L/def2-TZVPP/PCM Transition State Geometry Optimization" ) MN12_L_def2_TZVPP_PCM_Unknown = "MN12-L/def2-TZVPP/PCM Unknown" MN12_L_def2_TZVPP_SMD_Force = "MN12-L/def2-TZVPP/SMD Force" MN12_L_def2_TZVPP_SMD_Frequency_Analysis = ( "MN12-L/def2-TZVPP/SMD Frequency Analysis" ) MN12_L_def2_TZVPP_SMD_Frequency_Flattening_Geometry_Optimization = ( "MN12-L/def2-TZVPP/SMD Frequency Flattening Geometry Optimization" ) MN12_L_def2_TZVPP_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "MN12-L/def2-TZVPP/SMD Frequency Flattening Transition State Geometry Optimization" MN12_L_def2_TZVPP_SMD_Geometry_Optimization = ( "MN12-L/def2-TZVPP/SMD Geometry Optimization" ) MN12_L_def2_TZVPP_SMD_Single_Point = "MN12-L/def2-TZVPP/SMD Single Point" MN12_L_def2_TZVPP_SMD_Transition_State_Geometry_Optimization = ( "MN12-L/def2-TZVPP/SMD Transition State Geometry Optimization" ) MN12_L_def2_TZVPP_SMD_Unknown = "MN12-L/def2-TZVPP/SMD Unknown" MN12_L_def2_TZVPP_VACUUM_Force = "MN12-L/def2-TZVPP/VACUUM Force" MN12_L_def2_TZVPP_VACUUM_Frequency_Analysis = ( "MN12-L/def2-TZVPP/VACUUM Frequency Analysis" ) MN12_L_def2_TZVPP_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "MN12-L/def2-TZVPP/VACUUM Frequency Flattening Geometry Optimization" ) MN12_L_def2_TZVPP_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "MN12-L/def2-TZVPP/VACUUM Frequency Flattening Transition State Geometry Optimization" MN12_L_def2_TZVPP_VACUUM_Geometry_Optimization = ( "MN12-L/def2-TZVPP/VACUUM Geometry Optimization" ) MN12_L_def2_TZVPP_VACUUM_Single_Point = "MN12-L/def2-TZVPP/VACUUM Single Point" MN12_L_def2_TZVPP_VACUUM_Transition_State_Geometry_Optimization = ( "MN12-L/def2-TZVPP/VACUUM Transition State Geometry Optimization" ) MN12_L_def2_TZVPP_VACUUM_Unknown = "MN12-L/def2-TZVPP/VACUUM Unknown" MN12_L_def2_TZVP_PCM_Force = "MN12-L/def2-TZVP/PCM Force" MN12_L_def2_TZVP_PCM_Frequency_Analysis = "MN12-L/def2-TZVP/PCM Frequency Analysis" MN12_L_def2_TZVP_PCM_Frequency_Flattening_Geometry_Optimization = ( "MN12-L/def2-TZVP/PCM Frequency Flattening Geometry Optimization" ) MN12_L_def2_TZVP_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "MN12-L/def2-TZVP/PCM Frequency Flattening Transition State Geometry Optimization" MN12_L_def2_TZVP_PCM_Geometry_Optimization = ( "MN12-L/def2-TZVP/PCM Geometry Optimization" ) MN12_L_def2_TZVP_PCM_Single_Point = "MN12-L/def2-TZVP/PCM Single Point" MN12_L_def2_TZVP_PCM_Transition_State_Geometry_Optimization = ( "MN12-L/def2-TZVP/PCM Transition State Geometry Optimization" ) MN12_L_def2_TZVP_PCM_Unknown = "MN12-L/def2-TZVP/PCM Unknown" MN12_L_def2_TZVP_SMD_Force = "MN12-L/def2-TZVP/SMD Force" MN12_L_def2_TZVP_SMD_Frequency_Analysis = "MN12-L/def2-TZVP/SMD Frequency Analysis" MN12_L_def2_TZVP_SMD_Frequency_Flattening_Geometry_Optimization = ( "MN12-L/def2-TZVP/SMD Frequency Flattening Geometry Optimization" ) MN12_L_def2_TZVP_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "MN12-L/def2-TZVP/SMD Frequency Flattening Transition State Geometry Optimization" MN12_L_def2_TZVP_SMD_Geometry_Optimization = ( "MN12-L/def2-TZVP/SMD Geometry Optimization" ) MN12_L_def2_TZVP_SMD_Single_Point = "MN12-L/def2-TZVP/SMD Single Point" MN12_L_def2_TZVP_SMD_Transition_State_Geometry_Optimization = ( "MN12-L/def2-TZVP/SMD Transition State Geometry Optimization" ) MN12_L_def2_TZVP_SMD_Unknown = "MN12-L/def2-TZVP/SMD Unknown" MN12_L_def2_TZVP_VACUUM_Force = "MN12-L/def2-TZVP/VACUUM Force" MN12_L_def2_TZVP_VACUUM_Frequency_Analysis = ( "MN12-L/def2-TZVP/VACUUM Frequency Analysis" ) MN12_L_def2_TZVP_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "MN12-L/def2-TZVP/VACUUM Frequency Flattening Geometry Optimization" ) MN12_L_def2_TZVP_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "MN12-L/def2-TZVP/VACUUM Frequency Flattening Transition State Geometry Optimization" MN12_L_def2_TZVP_VACUUM_Geometry_Optimization = ( "MN12-L/def2-TZVP/VACUUM Geometry Optimization" ) MN12_L_def2_TZVP_VACUUM_Single_Point = "MN12-L/def2-TZVP/VACUUM Single Point" MN12_L_def2_TZVP_VACUUM_Transition_State_Geometry_Optimization = ( "MN12-L/def2-TZVP/VACUUM Transition State Geometry Optimization" ) MN12_L_def2_TZVP_VACUUM_Unknown = "MN12-L/def2-TZVP/VACUUM Unknown" PBE_6_31g_d_PCM_Force = "PBE/6-31g*/PCM Force" PBE_6_31g_d_PCM_Frequency_Analysis = "PBE/6-31g*/PCM Frequency Analysis" PBE_6_31g_d_PCM_Frequency_Flattening_Geometry_Optimization = ( "PBE/6-31g*/PCM Frequency Flattening Geometry Optimization" ) PBE_6_31g_d_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = ( "PBE/6-31g*/PCM Frequency Flattening Transition State Geometry Optimization" ) PBE_6_31g_d_PCM_Geometry_Optimization = "PBE/6-31g*/PCM Geometry Optimization" PBE_6_31g_d_PCM_Single_Point = "PBE/6-31g*/PCM Single Point" PBE_6_31g_d_PCM_Transition_State_Geometry_Optimization = ( "PBE/6-31g*/PCM Transition State Geometry Optimization" ) PBE_6_31g_d_PCM_Unknown = "PBE/6-31g*/PCM Unknown" PBE_6_31g_d_SMD_Force = "PBE/6-31g*/SMD Force" PBE_6_31g_d_SMD_Frequency_Analysis = "PBE/6-31g*/SMD Frequency Analysis" PBE_6_31g_d_SMD_Frequency_Flattening_Geometry_Optimization = ( "PBE/6-31g*/SMD Frequency Flattening Geometry Optimization" ) PBE_6_31g_d_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = ( "PBE/6-31g*/SMD Frequency Flattening Transition State Geometry Optimization" ) PBE_6_31g_d_SMD_Geometry_Optimization = "PBE/6-31g*/SMD Geometry Optimization" PBE_6_31g_d_SMD_Single_Point = "PBE/6-31g*/SMD Single Point" PBE_6_31g_d_SMD_Transition_State_Geometry_Optimization = ( "PBE/6-31g*/SMD Transition State Geometry Optimization" ) PBE_6_31g_d_SMD_Unknown = "PBE/6-31g*/SMD Unknown" PBE_6_31g_d_VACUUM_Force = "PBE/6-31g*/VACUUM Force" PBE_6_31g_d_VACUUM_Frequency_Analysis = "PBE/6-31g*/VACUUM Frequency Analysis" PBE_6_31g_d_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "PBE/6-31g*/VACUUM Frequency Flattening Geometry Optimization" ) PBE_6_31g_d_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = ( "PBE/6-31g*/VACUUM Frequency Flattening Transition State Geometry Optimization" ) PBE_6_31g_d_VACUUM_Geometry_Optimization = "PBE/6-31g*/VACUUM Geometry Optimization" PBE_6_31g_d_VACUUM_Single_Point = "PBE/6-31g*/VACUUM Single Point" PBE_6_31g_d_VACUUM_Transition_State_Geometry_Optimization = ( "PBE/6-31g*/VACUUM Transition State Geometry Optimization" ) PBE_6_31g_d_VACUUM_Unknown = "PBE/6-31g*/VACUUM Unknown" PBE_def2_QZVPD_PCM_Force = "PBE/def2-QZVPD/PCM Force" PBE_def2_QZVPD_PCM_Frequency_Analysis = "PBE/def2-QZVPD/PCM Frequency Analysis" PBE_def2_QZVPD_PCM_Frequency_Flattening_Geometry_Optimization = ( "PBE/def2-QZVPD/PCM Frequency Flattening Geometry Optimization" ) PBE_def2_QZVPD_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = ( "PBE/def2-QZVPD/PCM Frequency Flattening Transition State Geometry Optimization" ) PBE_def2_QZVPD_PCM_Geometry_Optimization = ( "PBE/def2-QZVPD/PCM Geometry Optimization" ) PBE_def2_QZVPD_PCM_Single_Point = "PBE/def2-QZVPD/PCM Single Point" PBE_def2_QZVPD_PCM_Transition_State_Geometry_Optimization = ( "PBE/def2-QZVPD/PCM Transition State Geometry Optimization" ) PBE_def2_QZVPD_PCM_Unknown = "PBE/def2-QZVPD/PCM Unknown" PBE_def2_QZVPD_SMD_Force = "PBE/def2-QZVPD/SMD Force" PBE_def2_QZVPD_SMD_Frequency_Analysis = "PBE/def2-QZVPD/SMD Frequency Analysis" PBE_def2_QZVPD_SMD_Frequency_Flattening_Geometry_Optimization = ( "PBE/def2-QZVPD/SMD Frequency Flattening Geometry Optimization" ) PBE_def2_QZVPD_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = ( "PBE/def2-QZVPD/SMD Frequency Flattening Transition State Geometry Optimization" ) PBE_def2_QZVPD_SMD_Geometry_Optimization = ( "PBE/def2-QZVPD/SMD Geometry Optimization" ) PBE_def2_QZVPD_SMD_Single_Point = "PBE/def2-QZVPD/SMD Single Point" PBE_def2_QZVPD_SMD_Transition_State_Geometry_Optimization = ( "PBE/def2-QZVPD/SMD Transition State Geometry Optimization" ) PBE_def2_QZVPD_SMD_Unknown = "PBE/def2-QZVPD/SMD Unknown" PBE_def2_QZVPD_VACUUM_Force = "PBE/def2-QZVPD/VACUUM Force" PBE_def2_QZVPD_VACUUM_Frequency_Analysis = ( "PBE/def2-QZVPD/VACUUM Frequency Analysis" ) PBE_def2_QZVPD_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "PBE/def2-QZVPD/VACUUM Frequency Flattening Geometry Optimization" ) PBE_def2_QZVPD_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "PBE/def2-QZVPD/VACUUM Frequency Flattening Transition State Geometry Optimization" PBE_def2_QZVPD_VACUUM_Geometry_Optimization = ( "PBE/def2-QZVPD/VACUUM Geometry Optimization" ) PBE_def2_QZVPD_VACUUM_Single_Point = "PBE/def2-QZVPD/VACUUM Single Point" PBE_def2_QZVPD_VACUUM_Transition_State_Geometry_Optimization = ( "PBE/def2-QZVPD/VACUUM Transition State Geometry Optimization" ) PBE_def2_QZVPD_VACUUM_Unknown = "PBE/def2-QZVPD/VACUUM Unknown" PBE_def2_QZVPPD_PCM_Force = "PBE/def2-QZVPPD/PCM Force" PBE_def2_QZVPPD_PCM_Frequency_Analysis = "PBE/def2-QZVPPD/PCM Frequency Analysis" PBE_def2_QZVPPD_PCM_Frequency_Flattening_Geometry_Optimization = ( "PBE/def2-QZVPPD/PCM Frequency Flattening Geometry Optimization" ) PBE_def2_QZVPPD_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "PBE/def2-QZVPPD/PCM Frequency Flattening Transition State Geometry Optimization" PBE_def2_QZVPPD_PCM_Geometry_Optimization = ( "PBE/def2-QZVPPD/PCM Geometry Optimization" ) PBE_def2_QZVPPD_PCM_Single_Point = "PBE/def2-QZVPPD/PCM Single Point" PBE_def2_QZVPPD_PCM_Transition_State_Geometry_Optimization = ( "PBE/def2-QZVPPD/PCM Transition State Geometry Optimization" ) PBE_def2_QZVPPD_PCM_Unknown = "PBE/def2-QZVPPD/PCM Unknown" PBE_def2_QZVPPD_SMD_Force = "PBE/def2-QZVPPD/SMD Force" PBE_def2_QZVPPD_SMD_Frequency_Analysis = "PBE/def2-QZVPPD/SMD Frequency Analysis" PBE_def2_QZVPPD_SMD_Frequency_Flattening_Geometry_Optimization = ( "PBE/def2-QZVPPD/SMD Frequency Flattening Geometry Optimization" ) PBE_def2_QZVPPD_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "PBE/def2-QZVPPD/SMD Frequency Flattening Transition State Geometry Optimization" PBE_def2_QZVPPD_SMD_Geometry_Optimization = ( "PBE/def2-QZVPPD/SMD Geometry Optimization" ) PBE_def2_QZVPPD_SMD_Single_Point = "PBE/def2-QZVPPD/SMD Single Point" PBE_def2_QZVPPD_SMD_Transition_State_Geometry_Optimization = ( "PBE/def2-QZVPPD/SMD Transition State Geometry Optimization" ) PBE_def2_QZVPPD_SMD_Unknown = "PBE/def2-QZVPPD/SMD Unknown" PBE_def2_QZVPPD_VACUUM_Force = "PBE/def2-QZVPPD/VACUUM Force" PBE_def2_QZVPPD_VACUUM_Frequency_Analysis = ( "PBE/def2-QZVPPD/VACUUM Frequency Analysis" ) PBE_def2_QZVPPD_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "PBE/def2-QZVPPD/VACUUM Frequency Flattening Geometry Optimization" ) PBE_def2_QZVPPD_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "PBE/def2-QZVPPD/VACUUM Frequency Flattening Transition State Geometry Optimization" PBE_def2_QZVPPD_VACUUM_Geometry_Optimization = ( "PBE/def2-QZVPPD/VACUUM Geometry Optimization" ) PBE_def2_QZVPPD_VACUUM_Single_Point = "PBE/def2-QZVPPD/VACUUM Single Point" PBE_def2_QZVPPD_VACUUM_Transition_State_Geometry_Optimization = ( "PBE/def2-QZVPPD/VACUUM Transition State Geometry Optimization" ) PBE_def2_QZVPPD_VACUUM_Unknown = "PBE/def2-QZVPPD/VACUUM Unknown" PBE_def2_SVPD_PCM_Force = "PBE/def2-SVPD/PCM Force" PBE_def2_SVPD_PCM_Frequency_Analysis = "PBE/def2-SVPD/PCM Frequency Analysis" PBE_def2_SVPD_PCM_Frequency_Flattening_Geometry_Optimization = ( "PBE/def2-SVPD/PCM Frequency Flattening Geometry Optimization" ) PBE_def2_SVPD_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = ( "PBE/def2-SVPD/PCM Frequency Flattening Transition State Geometry Optimization" ) PBE_def2_SVPD_PCM_Geometry_Optimization = "PBE/def2-SVPD/PCM Geometry Optimization" PBE_def2_SVPD_PCM_Single_Point = "PBE/def2-SVPD/PCM Single Point" PBE_def2_SVPD_PCM_Transition_State_Geometry_Optimization = ( "PBE/def2-SVPD/PCM Transition State Geometry Optimization" ) PBE_def2_SVPD_PCM_Unknown = "PBE/def2-SVPD/PCM Unknown" PBE_def2_SVPD_SMD_Force = "PBE/def2-SVPD/SMD Force" PBE_def2_SVPD_SMD_Frequency_Analysis = "PBE/def2-SVPD/SMD Frequency Analysis" PBE_def2_SVPD_SMD_Frequency_Flattening_Geometry_Optimization = ( "PBE/def2-SVPD/SMD Frequency Flattening Geometry Optimization" ) PBE_def2_SVPD_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = ( "PBE/def2-SVPD/SMD Frequency Flattening Transition State Geometry Optimization" ) PBE_def2_SVPD_SMD_Geometry_Optimization = "PBE/def2-SVPD/SMD Geometry Optimization" PBE_def2_SVPD_SMD_Single_Point = "PBE/def2-SVPD/SMD Single Point" PBE_def2_SVPD_SMD_Transition_State_Geometry_Optimization = ( "PBE/def2-SVPD/SMD Transition State Geometry Optimization" ) PBE_def2_SVPD_SMD_Unknown = "PBE/def2-SVPD/SMD Unknown" PBE_def2_SVPD_VACUUM_Force = "PBE/def2-SVPD/VACUUM Force" PBE_def2_SVPD_VACUUM_Frequency_Analysis = "PBE/def2-SVPD/VACUUM Frequency Analysis" PBE_def2_SVPD_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "PBE/def2-SVPD/VACUUM Frequency Flattening Geometry Optimization" ) PBE_def2_SVPD_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "PBE/def2-SVPD/VACUUM Frequency Flattening Transition State Geometry Optimization" PBE_def2_SVPD_VACUUM_Geometry_Optimization = ( "PBE/def2-SVPD/VACUUM Geometry Optimization" ) PBE_def2_SVPD_VACUUM_Single_Point = "PBE/def2-SVPD/VACUUM Single Point" PBE_def2_SVPD_VACUUM_Transition_State_Geometry_Optimization = ( "PBE/def2-SVPD/VACUUM Transition State Geometry Optimization" ) PBE_def2_SVPD_VACUUM_Unknown = "PBE/def2-SVPD/VACUUM Unknown" PBE_def2_TZVPD_PCM_Force = "PBE/def2-TZVPD/PCM Force" PBE_def2_TZVPD_PCM_Frequency_Analysis = "PBE/def2-TZVPD/PCM Frequency Analysis" PBE_def2_TZVPD_PCM_Frequency_Flattening_Geometry_Optimization = ( "PBE/def2-TZVPD/PCM Frequency Flattening Geometry Optimization" ) PBE_def2_TZVPD_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = ( "PBE/def2-TZVPD/PCM Frequency Flattening Transition State Geometry Optimization" ) PBE_def2_TZVPD_PCM_Geometry_Optimization = ( "PBE/def2-TZVPD/PCM Geometry Optimization" ) PBE_def2_TZVPD_PCM_Single_Point = "PBE/def2-TZVPD/PCM Single Point" PBE_def2_TZVPD_PCM_Transition_State_Geometry_Optimization = ( "PBE/def2-TZVPD/PCM Transition State Geometry Optimization" ) PBE_def2_TZVPD_PCM_Unknown = "PBE/def2-TZVPD/PCM Unknown" PBE_def2_TZVPD_SMD_Force = "PBE/def2-TZVPD/SMD Force" PBE_def2_TZVPD_SMD_Frequency_Analysis = "PBE/def2-TZVPD/SMD Frequency Analysis" PBE_def2_TZVPD_SMD_Frequency_Flattening_Geometry_Optimization = ( "PBE/def2-TZVPD/SMD Frequency Flattening Geometry Optimization" ) PBE_def2_TZVPD_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = ( "PBE/def2-TZVPD/SMD Frequency Flattening Transition State Geometry Optimization" ) PBE_def2_TZVPD_SMD_Geometry_Optimization = ( "PBE/def2-TZVPD/SMD Geometry Optimization" ) PBE_def2_TZVPD_SMD_Single_Point = "PBE/def2-TZVPD/SMD Single Point" PBE_def2_TZVPD_SMD_Transition_State_Geometry_Optimization = ( "PBE/def2-TZVPD/SMD Transition State Geometry Optimization" ) PBE_def2_TZVPD_SMD_Unknown = "PBE/def2-TZVPD/SMD Unknown" PBE_def2_TZVPD_VACUUM_Force = "PBE/def2-TZVPD/VACUUM Force" PBE_def2_TZVPD_VACUUM_Frequency_Analysis = ( "PBE/def2-TZVPD/VACUUM Frequency Analysis" ) PBE_def2_TZVPD_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "PBE/def2-TZVPD/VACUUM Frequency Flattening Geometry Optimization" ) PBE_def2_TZVPD_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "PBE/def2-TZVPD/VACUUM Frequency Flattening Transition State Geometry Optimization" PBE_def2_TZVPD_VACUUM_Geometry_Optimization = ( "PBE/def2-TZVPD/VACUUM Geometry Optimization" ) PBE_def2_TZVPD_VACUUM_Single_Point = "PBE/def2-TZVPD/VACUUM Single Point" PBE_def2_TZVPD_VACUUM_Transition_State_Geometry_Optimization = ( "PBE/def2-TZVPD/VACUUM Transition State Geometry Optimization" ) PBE_def2_TZVPD_VACUUM_Unknown = "PBE/def2-TZVPD/VACUUM Unknown" PBE_def2_TZVPPD_PCM_Force = "PBE/def2-TZVPPD/PCM Force" PBE_def2_TZVPPD_PCM_Frequency_Analysis = "PBE/def2-TZVPPD/PCM Frequency Analysis" PBE_def2_TZVPPD_PCM_Frequency_Flattening_Geometry_Optimization = ( "PBE/def2-TZVPPD/PCM Frequency Flattening Geometry Optimization" ) PBE_def2_TZVPPD_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "PBE/def2-TZVPPD/PCM Frequency Flattening Transition State Geometry Optimization" PBE_def2_TZVPPD_PCM_Geometry_Optimization = ( "PBE/def2-TZVPPD/PCM Geometry Optimization" ) PBE_def2_TZVPPD_PCM_Single_Point = "PBE/def2-TZVPPD/PCM Single Point" PBE_def2_TZVPPD_PCM_Transition_State_Geometry_Optimization = ( "PBE/def2-TZVPPD/PCM Transition State Geometry Optimization" ) PBE_def2_TZVPPD_PCM_Unknown = "PBE/def2-TZVPPD/PCM Unknown" PBE_def2_TZVPPD_SMD_Force = "PBE/def2-TZVPPD/SMD Force" PBE_def2_TZVPPD_SMD_Frequency_Analysis = "PBE/def2-TZVPPD/SMD Frequency Analysis" PBE_def2_TZVPPD_SMD_Frequency_Flattening_Geometry_Optimization = ( "PBE/def2-TZVPPD/SMD Frequency Flattening Geometry Optimization" ) PBE_def2_TZVPPD_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "PBE/def2-TZVPPD/SMD Frequency Flattening Transition State Geometry Optimization" PBE_def2_TZVPPD_SMD_Geometry_Optimization = ( "PBE/def2-TZVPPD/SMD Geometry Optimization" ) PBE_def2_TZVPPD_SMD_Single_Point = "PBE/def2-TZVPPD/SMD Single Point" PBE_def2_TZVPPD_SMD_Transition_State_Geometry_Optimization = ( "PBE/def2-TZVPPD/SMD Transition State Geometry Optimization" ) PBE_def2_TZVPPD_SMD_Unknown = "PBE/def2-TZVPPD/SMD Unknown" PBE_def2_TZVPPD_VACUUM_Force = "PBE/def2-TZVPPD/VACUUM Force" PBE_def2_TZVPPD_VACUUM_Frequency_Analysis = ( "PBE/def2-TZVPPD/VACUUM Frequency Analysis" ) PBE_def2_TZVPPD_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "PBE/def2-TZVPPD/VACUUM Frequency Flattening Geometry Optimization" ) PBE_def2_TZVPPD_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "PBE/def2-TZVPPD/VACUUM Frequency Flattening Transition State Geometry Optimization" PBE_def2_TZVPPD_VACUUM_Geometry_Optimization = ( "PBE/def2-TZVPPD/VACUUM Geometry Optimization" ) PBE_def2_TZVPPD_VACUUM_Single_Point = "PBE/def2-TZVPPD/VACUUM Single Point" PBE_def2_TZVPPD_VACUUM_Transition_State_Geometry_Optimization = ( "PBE/def2-TZVPPD/VACUUM Transition State Geometry Optimization" ) PBE_def2_TZVPPD_VACUUM_Unknown = "PBE/def2-TZVPPD/VACUUM Unknown" PBE_def2_TZVPP_PCM_Force = "PBE/def2-TZVPP/PCM Force" PBE_def2_TZVPP_PCM_Frequency_Analysis = "PBE/def2-TZVPP/PCM Frequency Analysis" PBE_def2_TZVPP_PCM_Frequency_Flattening_Geometry_Optimization = ( "PBE/def2-TZVPP/PCM Frequency Flattening Geometry Optimization" ) PBE_def2_TZVPP_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = ( "PBE/def2-TZVPP/PCM Frequency Flattening Transition State Geometry Optimization" ) PBE_def2_TZVPP_PCM_Geometry_Optimization = ( "PBE/def2-TZVPP/PCM Geometry Optimization" ) PBE_def2_TZVPP_PCM_Single_Point = "PBE/def2-TZVPP/PCM Single Point" PBE_def2_TZVPP_PCM_Transition_State_Geometry_Optimization = ( "PBE/def2-TZVPP/PCM Transition State Geometry Optimization" ) PBE_def2_TZVPP_PCM_Unknown = "PBE/def2-TZVPP/PCM Unknown" PBE_def2_TZVPP_SMD_Force = "PBE/def2-TZVPP/SMD Force" PBE_def2_TZVPP_SMD_Frequency_Analysis = "PBE/def2-TZVPP/SMD Frequency Analysis" PBE_def2_TZVPP_SMD_Frequency_Flattening_Geometry_Optimization = ( "PBE/def2-TZVPP/SMD Frequency Flattening Geometry Optimization" ) PBE_def2_TZVPP_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = ( "PBE/def2-TZVPP/SMD Frequency Flattening Transition State Geometry Optimization" ) PBE_def2_TZVPP_SMD_Geometry_Optimization = ( "PBE/def2-TZVPP/SMD Geometry Optimization" ) PBE_def2_TZVPP_SMD_Single_Point = "PBE/def2-TZVPP/SMD Single Point" PBE_def2_TZVPP_SMD_Transition_State_Geometry_Optimization = ( "PBE/def2-TZVPP/SMD Transition State Geometry Optimization" ) PBE_def2_TZVPP_SMD_Unknown = "PBE/def2-TZVPP/SMD Unknown" PBE_def2_TZVPP_VACUUM_Force = "PBE/def2-TZVPP/VACUUM Force" PBE_def2_TZVPP_VACUUM_Frequency_Analysis = ( "PBE/def2-TZVPP/VACUUM Frequency Analysis" ) PBE_def2_TZVPP_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "PBE/def2-TZVPP/VACUUM Frequency Flattening Geometry Optimization" ) PBE_def2_TZVPP_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "PBE/def2-TZVPP/VACUUM Frequency Flattening Transition State Geometry Optimization" PBE_def2_TZVPP_VACUUM_Geometry_Optimization = ( "PBE/def2-TZVPP/VACUUM Geometry Optimization" ) PBE_def2_TZVPP_VACUUM_Single_Point = "PBE/def2-TZVPP/VACUUM Single Point" PBE_def2_TZVPP_VACUUM_Transition_State_Geometry_Optimization = ( "PBE/def2-TZVPP/VACUUM Transition State Geometry Optimization" ) PBE_def2_TZVPP_VACUUM_Unknown = "PBE/def2-TZVPP/VACUUM Unknown" PBE_def2_TZVP_PCM_Force = "PBE/def2-TZVP/PCM Force" PBE_def2_TZVP_PCM_Frequency_Analysis = "PBE/def2-TZVP/PCM Frequency Analysis" PBE_def2_TZVP_PCM_Frequency_Flattening_Geometry_Optimization = ( "PBE/def2-TZVP/PCM Frequency Flattening Geometry Optimization" ) PBE_def2_TZVP_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = ( "PBE/def2-TZVP/PCM Frequency Flattening Transition State Geometry Optimization" ) PBE_def2_TZVP_PCM_Geometry_Optimization = "PBE/def2-TZVP/PCM Geometry Optimization" PBE_def2_TZVP_PCM_Single_Point = "PBE/def2-TZVP/PCM Single Point" PBE_def2_TZVP_PCM_Transition_State_Geometry_Optimization = ( "PBE/def2-TZVP/PCM Transition State Geometry Optimization" ) PBE_def2_TZVP_PCM_Unknown = "PBE/def2-TZVP/PCM Unknown" PBE_def2_TZVP_SMD_Force = "PBE/def2-TZVP/SMD Force" PBE_def2_TZVP_SMD_Frequency_Analysis = "PBE/def2-TZVP/SMD Frequency Analysis" PBE_def2_TZVP_SMD_Frequency_Flattening_Geometry_Optimization = ( "PBE/def2-TZVP/SMD Frequency Flattening Geometry Optimization" ) PBE_def2_TZVP_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = ( "PBE/def2-TZVP/SMD Frequency Flattening Transition State Geometry Optimization" ) PBE_def2_TZVP_SMD_Geometry_Optimization = "PBE/def2-TZVP/SMD Geometry Optimization" PBE_def2_TZVP_SMD_Single_Point = "PBE/def2-TZVP/SMD Single Point" PBE_def2_TZVP_SMD_Transition_State_Geometry_Optimization = ( "PBE/def2-TZVP/SMD Transition State Geometry Optimization" ) PBE_def2_TZVP_SMD_Unknown = "PBE/def2-TZVP/SMD Unknown" PBE_def2_TZVP_VACUUM_Force = "PBE/def2-TZVP/VACUUM Force" PBE_def2_TZVP_VACUUM_Frequency_Analysis = "PBE/def2-TZVP/VACUUM Frequency Analysis" PBE_def2_TZVP_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "PBE/def2-TZVP/VACUUM Frequency Flattening Geometry Optimization" ) PBE_def2_TZVP_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "PBE/def2-TZVP/VACUUM Frequency Flattening Transition State Geometry Optimization" PBE_def2_TZVP_VACUUM_Geometry_Optimization = ( "PBE/def2-TZVP/VACUUM Geometry Optimization" ) PBE_def2_TZVP_VACUUM_Single_Point = "PBE/def2-TZVP/VACUUM Single Point" PBE_def2_TZVP_VACUUM_Transition_State_Geometry_Optimization = ( "PBE/def2-TZVP/VACUUM Transition State Geometry Optimization" ) PBE_def2_TZVP_VACUUM_Unknown = "PBE/def2-TZVP/VACUUM Unknown" wB97M_V_6_31g_d_PCM_Force = "wB97M-V/6-31g*/PCM Force" wB97M_V_6_31g_d_PCM_Frequency_Analysis = "wB97M-V/6-31g*/PCM Frequency Analysis" wB97M_V_6_31g_d_PCM_Frequency_Flattening_Geometry_Optimization = ( "wB97M-V/6-31g*/PCM Frequency Flattening Geometry Optimization" ) wB97M_V_6_31g_d_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = ( "wB97M-V/6-31g*/PCM Frequency Flattening Transition State Geometry Optimization" ) wB97M_V_6_31g_d_PCM_Geometry_Optimization = ( "wB97M-V/6-31g*/PCM Geometry Optimization" ) wB97M_V_6_31g_d_PCM_Single_Point = "wB97M-V/6-31g*/PCM Single Point" wB97M_V_6_31g_d_PCM_Transition_State_Geometry_Optimization = ( "wB97M-V/6-31g*/PCM Transition State Geometry Optimization" ) wB97M_V_6_31g_d_PCM_Unknown = "wB97M-V/6-31g*/PCM Unknown" wB97M_V_6_31g_d_SMD_Force = "wB97M-V/6-31g*/SMD Force" wB97M_V_6_31g_d_SMD_Frequency_Analysis = "wB97M-V/6-31g*/SMD Frequency Analysis" wB97M_V_6_31g_d_SMD_Frequency_Flattening_Geometry_Optimization = ( "wB97M-V/6-31g*/SMD Frequency Flattening Geometry Optimization" ) wB97M_V_6_31g_d_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = ( "wB97M-V/6-31g*/SMD Frequency Flattening Transition State Geometry Optimization" ) wB97M_V_6_31g_d_SMD_Geometry_Optimization = ( "wB97M-V/6-31g*/SMD Geometry Optimization" ) wB97M_V_6_31g_d_SMD_Single_Point = "wB97M-V/6-31g*/SMD Single Point" wB97M_V_6_31g_d_SMD_Transition_State_Geometry_Optimization = ( "wB97M-V/6-31g*/SMD Transition State Geometry Optimization" ) wB97M_V_6_31g_d_SMD_Unknown = "wB97M-V/6-31g*/SMD Unknown" wB97M_V_6_31g_d_VACUUM_Force = "wB97M-V/6-31g*/VACUUM Force" wB97M_V_6_31g_d_VACUUM_Frequency_Analysis = ( "wB97M-V/6-31g*/VACUUM Frequency Analysis" ) wB97M_V_6_31g_d_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "wB97M-V/6-31g*/VACUUM Frequency Flattening Geometry Optimization" ) wB97M_V_6_31g_d_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97M-V/6-31g*/VACUUM Frequency Flattening Transition State Geometry Optimization" wB97M_V_6_31g_d_VACUUM_Geometry_Optimization = ( "wB97M-V/6-31g*/VACUUM Geometry Optimization" ) wB97M_V_6_31g_d_VACUUM_Single_Point = "wB97M-V/6-31g*/VACUUM Single Point" wB97M_V_6_31g_d_VACUUM_Transition_State_Geometry_Optimization = ( "wB97M-V/6-31g*/VACUUM Transition State Geometry Optimization" ) wB97M_V_6_31g_d_VACUUM_Unknown = "wB97M-V/6-31g*/VACUUM Unknown" wB97M_V_def2_QZVPD_PCM_Force = "wB97M-V/def2-QZVPD/PCM Force" wB97M_V_def2_QZVPD_PCM_Frequency_Analysis = ( "wB97M-V/def2-QZVPD/PCM Frequency Analysis" ) wB97M_V_def2_QZVPD_PCM_Frequency_Flattening_Geometry_Optimization = ( "wB97M-V/def2-QZVPD/PCM Frequency Flattening Geometry Optimization" ) wB97M_V_def2_QZVPD_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97M-V/def2-QZVPD/PCM Frequency Flattening Transition State Geometry Optimization" wB97M_V_def2_QZVPD_PCM_Geometry_Optimization = ( "wB97M-V/def2-QZVPD/PCM Geometry Optimization" ) wB97M_V_def2_QZVPD_PCM_Single_Point = "wB97M-V/def2-QZVPD/PCM Single Point" wB97M_V_def2_QZVPD_PCM_Transition_State_Geometry_Optimization = ( "wB97M-V/def2-QZVPD/PCM Transition State Geometry Optimization" ) wB97M_V_def2_QZVPD_PCM_Unknown = "wB97M-V/def2-QZVPD/PCM Unknown" wB97M_V_def2_QZVPD_SMD_Force = "wB97M-V/def2-QZVPD/SMD Force" wB97M_V_def2_QZVPD_SMD_Frequency_Analysis = ( "wB97M-V/def2-QZVPD/SMD Frequency Analysis" ) wB97M_V_def2_QZVPD_SMD_Frequency_Flattening_Geometry_Optimization = ( "wB97M-V/def2-QZVPD/SMD Frequency Flattening Geometry Optimization" ) wB97M_V_def2_QZVPD_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97M-V/def2-QZVPD/SMD Frequency Flattening Transition State Geometry Optimization" wB97M_V_def2_QZVPD_SMD_Geometry_Optimization = ( "wB97M-V/def2-QZVPD/SMD Geometry Optimization" ) wB97M_V_def2_QZVPD_SMD_Single_Point = "wB97M-V/def2-QZVPD/SMD Single Point" wB97M_V_def2_QZVPD_SMD_Transition_State_Geometry_Optimization = ( "wB97M-V/def2-QZVPD/SMD Transition State Geometry Optimization" ) wB97M_V_def2_QZVPD_SMD_Unknown = "wB97M-V/def2-QZVPD/SMD Unknown" wB97M_V_def2_QZVPD_VACUUM_Force = "wB97M-V/def2-QZVPD/VACUUM Force" wB97M_V_def2_QZVPD_VACUUM_Frequency_Analysis = ( "wB97M-V/def2-QZVPD/VACUUM Frequency Analysis" ) wB97M_V_def2_QZVPD_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "wB97M-V/def2-QZVPD/VACUUM Frequency Flattening Geometry Optimization" ) wB97M_V_def2_QZVPD_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97M-V/def2-QZVPD/VACUUM Frequency Flattening Transition State Geometry Optimization" wB97M_V_def2_QZVPD_VACUUM_Geometry_Optimization = ( "wB97M-V/def2-QZVPD/VACUUM Geometry Optimization" ) wB97M_V_def2_QZVPD_VACUUM_Single_Point = "wB97M-V/def2-QZVPD/VACUUM Single Point" wB97M_V_def2_QZVPD_VACUUM_Transition_State_Geometry_Optimization = ( "wB97M-V/def2-QZVPD/VACUUM Transition State Geometry Optimization" ) wB97M_V_def2_QZVPD_VACUUM_Unknown = "wB97M-V/def2-QZVPD/VACUUM Unknown" wB97M_V_def2_QZVPPD_PCM_Force = "wB97M-V/def2-QZVPPD/PCM Force" wB97M_V_def2_QZVPPD_PCM_Frequency_Analysis = ( "wB97M-V/def2-QZVPPD/PCM Frequency Analysis" ) wB97M_V_def2_QZVPPD_PCM_Frequency_Flattening_Geometry_Optimization = ( "wB97M-V/def2-QZVPPD/PCM Frequency Flattening Geometry Optimization" ) wB97M_V_def2_QZVPPD_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97M-V/def2-QZVPPD/PCM Frequency Flattening Transition State Geometry Optimization" wB97M_V_def2_QZVPPD_PCM_Geometry_Optimization = ( "wB97M-V/def2-QZVPPD/PCM Geometry Optimization" ) wB97M_V_def2_QZVPPD_PCM_Single_Point = "wB97M-V/def2-QZVPPD/PCM Single Point" wB97M_V_def2_QZVPPD_PCM_Transition_State_Geometry_Optimization = ( "wB97M-V/def2-QZVPPD/PCM Transition State Geometry Optimization" ) wB97M_V_def2_QZVPPD_PCM_Unknown = "wB97M-V/def2-QZVPPD/PCM Unknown" wB97M_V_def2_QZVPPD_SMD_Force = "wB97M-V/def2-QZVPPD/SMD Force" wB97M_V_def2_QZVPPD_SMD_Frequency_Analysis = ( "wB97M-V/def2-QZVPPD/SMD Frequency Analysis" ) wB97M_V_def2_QZVPPD_SMD_Frequency_Flattening_Geometry_Optimization = ( "wB97M-V/def2-QZVPPD/SMD Frequency Flattening Geometry Optimization" ) wB97M_V_def2_QZVPPD_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97M-V/def2-QZVPPD/SMD Frequency Flattening Transition State Geometry Optimization" wB97M_V_def2_QZVPPD_SMD_Geometry_Optimization = ( "wB97M-V/def2-QZVPPD/SMD Geometry Optimization" ) wB97M_V_def2_QZVPPD_SMD_Single_Point = "wB97M-V/def2-QZVPPD/SMD Single Point" wB97M_V_def2_QZVPPD_SMD_Transition_State_Geometry_Optimization = ( "wB97M-V/def2-QZVPPD/SMD Transition State Geometry Optimization" ) wB97M_V_def2_QZVPPD_SMD_Unknown = "wB97M-V/def2-QZVPPD/SMD Unknown" wB97M_V_def2_QZVPPD_VACUUM_Force = "wB97M-V/def2-QZVPPD/VACUUM Force" wB97M_V_def2_QZVPPD_VACUUM_Frequency_Analysis = ( "wB97M-V/def2-QZVPPD/VACUUM Frequency Analysis" ) wB97M_V_def2_QZVPPD_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "wB97M-V/def2-QZVPPD/VACUUM Frequency Flattening Geometry Optimization" ) wB97M_V_def2_QZVPPD_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97M-V/def2-QZVPPD/VACUUM Frequency Flattening Transition State Geometry Optimization" wB97M_V_def2_QZVPPD_VACUUM_Geometry_Optimization = ( "wB97M-V/def2-QZVPPD/VACUUM Geometry Optimization" ) wB97M_V_def2_QZVPPD_VACUUM_Single_Point = "wB97M-V/def2-QZVPPD/VACUUM Single Point" wB97M_V_def2_QZVPPD_VACUUM_Transition_State_Geometry_Optimization = ( "wB97M-V/def2-QZVPPD/VACUUM Transition State Geometry Optimization" ) wB97M_V_def2_QZVPPD_VACUUM_Unknown = "wB97M-V/def2-QZVPPD/VACUUM Unknown" wB97M_V_def2_SVPD_PCM_Force = "wB97M-V/def2-SVPD/PCM Force" wB97M_V_def2_SVPD_PCM_Frequency_Analysis = ( "wB97M-V/def2-SVPD/PCM Frequency Analysis" ) wB97M_V_def2_SVPD_PCM_Frequency_Flattening_Geometry_Optimization = ( "wB97M-V/def2-SVPD/PCM Frequency Flattening Geometry Optimization" ) wB97M_V_def2_SVPD_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97M-V/def2-SVPD/PCM Frequency Flattening Transition State Geometry Optimization" wB97M_V_def2_SVPD_PCM_Geometry_Optimization = ( "wB97M-V/def2-SVPD/PCM Geometry Optimization" ) wB97M_V_def2_SVPD_PCM_Single_Point = "wB97M-V/def2-SVPD/PCM Single Point" wB97M_V_def2_SVPD_PCM_Transition_State_Geometry_Optimization = ( "wB97M-V/def2-SVPD/PCM Transition State Geometry Optimization" ) wB97M_V_def2_SVPD_PCM_Unknown = "wB97M-V/def2-SVPD/PCM Unknown" wB97M_V_def2_SVPD_SMD_Force = "wB97M-V/def2-SVPD/SMD Force" wB97M_V_def2_SVPD_SMD_Frequency_Analysis = ( "wB97M-V/def2-SVPD/SMD Frequency Analysis" ) wB97M_V_def2_SVPD_SMD_Frequency_Flattening_Geometry_Optimization = ( "wB97M-V/def2-SVPD/SMD Frequency Flattening Geometry Optimization" ) wB97M_V_def2_SVPD_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97M-V/def2-SVPD/SMD Frequency Flattening Transition State Geometry Optimization" wB97M_V_def2_SVPD_SMD_Geometry_Optimization = ( "wB97M-V/def2-SVPD/SMD Geometry Optimization" ) wB97M_V_def2_SVPD_SMD_Single_Point = "wB97M-V/def2-SVPD/SMD Single Point" wB97M_V_def2_SVPD_SMD_Transition_State_Geometry_Optimization = ( "wB97M-V/def2-SVPD/SMD Transition State Geometry Optimization" ) wB97M_V_def2_SVPD_SMD_Unknown = "wB97M-V/def2-SVPD/SMD Unknown" wB97M_V_def2_SVPD_VACUUM_Force = "wB97M-V/def2-SVPD/VACUUM Force" wB97M_V_def2_SVPD_VACUUM_Frequency_Analysis = ( "wB97M-V/def2-SVPD/VACUUM Frequency Analysis" ) wB97M_V_def2_SVPD_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "wB97M-V/def2-SVPD/VACUUM Frequency Flattening Geometry Optimization" ) wB97M_V_def2_SVPD_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97M-V/def2-SVPD/VACUUM Frequency Flattening Transition State Geometry Optimization" wB97M_V_def2_SVPD_VACUUM_Geometry_Optimization = ( "wB97M-V/def2-SVPD/VACUUM Geometry Optimization" ) wB97M_V_def2_SVPD_VACUUM_Single_Point = "wB97M-V/def2-SVPD/VACUUM Single Point" wB97M_V_def2_SVPD_VACUUM_Transition_State_Geometry_Optimization = ( "wB97M-V/def2-SVPD/VACUUM Transition State Geometry Optimization" ) wB97M_V_def2_SVPD_VACUUM_Unknown = "wB97M-V/def2-SVPD/VACUUM Unknown" wB97M_V_def2_TZVPD_PCM_Force = "wB97M-V/def2-TZVPD/PCM Force" wB97M_V_def2_TZVPD_PCM_Frequency_Analysis = ( "wB97M-V/def2-TZVPD/PCM Frequency Analysis" ) wB97M_V_def2_TZVPD_PCM_Frequency_Flattening_Geometry_Optimization = ( "wB97M-V/def2-TZVPD/PCM Frequency Flattening Geometry Optimization" ) wB97M_V_def2_TZVPD_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97M-V/def2-TZVPD/PCM Frequency Flattening Transition State Geometry Optimization" wB97M_V_def2_TZVPD_PCM_Geometry_Optimization = ( "wB97M-V/def2-TZVPD/PCM Geometry Optimization" ) wB97M_V_def2_TZVPD_PCM_Single_Point = "wB97M-V/def2-TZVPD/PCM Single Point" wB97M_V_def2_TZVPD_PCM_Transition_State_Geometry_Optimization = ( "wB97M-V/def2-TZVPD/PCM Transition State Geometry Optimization" ) wB97M_V_def2_TZVPD_PCM_Unknown = "wB97M-V/def2-TZVPD/PCM Unknown" wB97M_V_def2_TZVPD_SMD_Force = "wB97M-V/def2-TZVPD/SMD Force" wB97M_V_def2_TZVPD_SMD_Frequency_Analysis = ( "wB97M-V/def2-TZVPD/SMD Frequency Analysis" ) wB97M_V_def2_TZVPD_SMD_Frequency_Flattening_Geometry_Optimization = ( "wB97M-V/def2-TZVPD/SMD Frequency Flattening Geometry Optimization" ) wB97M_V_def2_TZVPD_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97M-V/def2-TZVPD/SMD Frequency Flattening Transition State Geometry Optimization" wB97M_V_def2_TZVPD_SMD_Geometry_Optimization = ( "wB97M-V/def2-TZVPD/SMD Geometry Optimization" ) wB97M_V_def2_TZVPD_SMD_Single_Point = "wB97M-V/def2-TZVPD/SMD Single Point" wB97M_V_def2_TZVPD_SMD_Transition_State_Geometry_Optimization = ( "wB97M-V/def2-TZVPD/SMD Transition State Geometry Optimization" ) wB97M_V_def2_TZVPD_SMD_Unknown = "wB97M-V/def2-TZVPD/SMD Unknown" wB97M_V_def2_TZVPD_VACUUM_Force = "wB97M-V/def2-TZVPD/VACUUM Force" wB97M_V_def2_TZVPD_VACUUM_Frequency_Analysis = ( "wB97M-V/def2-TZVPD/VACUUM Frequency Analysis" ) wB97M_V_def2_TZVPD_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "wB97M-V/def2-TZVPD/VACUUM Frequency Flattening Geometry Optimization" ) wB97M_V_def2_TZVPD_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97M-V/def2-TZVPD/VACUUM Frequency Flattening Transition State Geometry Optimization" wB97M_V_def2_TZVPD_VACUUM_Geometry_Optimization = ( "wB97M-V/def2-TZVPD/VACUUM Geometry Optimization" ) wB97M_V_def2_TZVPD_VACUUM_Single_Point = "wB97M-V/def2-TZVPD/VACUUM Single Point" wB97M_V_def2_TZVPD_VACUUM_Transition_State_Geometry_Optimization = ( "wB97M-V/def2-TZVPD/VACUUM Transition State Geometry Optimization" ) wB97M_V_def2_TZVPD_VACUUM_Unknown = "wB97M-V/def2-TZVPD/VACUUM Unknown" wB97M_V_def2_TZVPPD_PCM_Force = "wB97M-V/def2-TZVPPD/PCM Force" wB97M_V_def2_TZVPPD_PCM_Frequency_Analysis = ( "wB97M-V/def2-TZVPPD/PCM Frequency Analysis" ) wB97M_V_def2_TZVPPD_PCM_Frequency_Flattening_Geometry_Optimization = ( "wB97M-V/def2-TZVPPD/PCM Frequency Flattening Geometry Optimization" ) wB97M_V_def2_TZVPPD_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97M-V/def2-TZVPPD/PCM Frequency Flattening Transition State Geometry Optimization" wB97M_V_def2_TZVPPD_PCM_Geometry_Optimization = ( "wB97M-V/def2-TZVPPD/PCM Geometry Optimization" ) wB97M_V_def2_TZVPPD_PCM_Single_Point = "wB97M-V/def2-TZVPPD/PCM Single Point" wB97M_V_def2_TZVPPD_PCM_Transition_State_Geometry_Optimization = ( "wB97M-V/def2-TZVPPD/PCM Transition State Geometry Optimization" ) wB97M_V_def2_TZVPPD_PCM_Unknown = "wB97M-V/def2-TZVPPD/PCM Unknown" wB97M_V_def2_TZVPPD_SMD_Force = "wB97M-V/def2-TZVPPD/SMD Force" wB97M_V_def2_TZVPPD_SMD_Frequency_Analysis = ( "wB97M-V/def2-TZVPPD/SMD Frequency Analysis" ) wB97M_V_def2_TZVPPD_SMD_Frequency_Flattening_Geometry_Optimization = ( "wB97M-V/def2-TZVPPD/SMD Frequency Flattening Geometry Optimization" ) wB97M_V_def2_TZVPPD_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97M-V/def2-TZVPPD/SMD Frequency Flattening Transition State Geometry Optimization" wB97M_V_def2_TZVPPD_SMD_Geometry_Optimization = ( "wB97M-V/def2-TZVPPD/SMD Geometry Optimization" ) wB97M_V_def2_TZVPPD_SMD_Single_Point = "wB97M-V/def2-TZVPPD/SMD Single Point" wB97M_V_def2_TZVPPD_SMD_Transition_State_Geometry_Optimization = ( "wB97M-V/def2-TZVPPD/SMD Transition State Geometry Optimization" ) wB97M_V_def2_TZVPPD_SMD_Unknown = "wB97M-V/def2-TZVPPD/SMD Unknown" wB97M_V_def2_TZVPPD_VACUUM_Force = "wB97M-V/def2-TZVPPD/VACUUM Force" wB97M_V_def2_TZVPPD_VACUUM_Frequency_Analysis = ( "wB97M-V/def2-TZVPPD/VACUUM Frequency Analysis" ) wB97M_V_def2_TZVPPD_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "wB97M-V/def2-TZVPPD/VACUUM Frequency Flattening Geometry Optimization" ) wB97M_V_def2_TZVPPD_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97M-V/def2-TZVPPD/VACUUM Frequency Flattening Transition State Geometry Optimization" wB97M_V_def2_TZVPPD_VACUUM_Geometry_Optimization = ( "wB97M-V/def2-TZVPPD/VACUUM Geometry Optimization" ) wB97M_V_def2_TZVPPD_VACUUM_Single_Point = "wB97M-V/def2-TZVPPD/VACUUM Single Point" wB97M_V_def2_TZVPPD_VACUUM_Transition_State_Geometry_Optimization = ( "wB97M-V/def2-TZVPPD/VACUUM Transition State Geometry Optimization" ) wB97M_V_def2_TZVPPD_VACUUM_Unknown = "wB97M-V/def2-TZVPPD/VACUUM Unknown" wB97M_V_def2_TZVPP_PCM_Force = "wB97M-V/def2-TZVPP/PCM Force" wB97M_V_def2_TZVPP_PCM_Frequency_Analysis = ( "wB97M-V/def2-TZVPP/PCM Frequency Analysis" ) wB97M_V_def2_TZVPP_PCM_Frequency_Flattening_Geometry_Optimization = ( "wB97M-V/def2-TZVPP/PCM Frequency Flattening Geometry Optimization" ) wB97M_V_def2_TZVPP_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97M-V/def2-TZVPP/PCM Frequency Flattening Transition State Geometry Optimization" wB97M_V_def2_TZVPP_PCM_Geometry_Optimization = ( "wB97M-V/def2-TZVPP/PCM Geometry Optimization" ) wB97M_V_def2_TZVPP_PCM_Single_Point = "wB97M-V/def2-TZVPP/PCM Single Point" wB97M_V_def2_TZVPP_PCM_Transition_State_Geometry_Optimization = ( "wB97M-V/def2-TZVPP/PCM Transition State Geometry Optimization" ) wB97M_V_def2_TZVPP_PCM_Unknown = "wB97M-V/def2-TZVPP/PCM Unknown" wB97M_V_def2_TZVPP_SMD_Force = "wB97M-V/def2-TZVPP/SMD Force" wB97M_V_def2_TZVPP_SMD_Frequency_Analysis = ( "wB97M-V/def2-TZVPP/SMD Frequency Analysis" ) wB97M_V_def2_TZVPP_SMD_Frequency_Flattening_Geometry_Optimization = ( "wB97M-V/def2-TZVPP/SMD Frequency Flattening Geometry Optimization" ) wB97M_V_def2_TZVPP_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97M-V/def2-TZVPP/SMD Frequency Flattening Transition State Geometry Optimization" wB97M_V_def2_TZVPP_SMD_Geometry_Optimization = ( "wB97M-V/def2-TZVPP/SMD Geometry Optimization" ) wB97M_V_def2_TZVPP_SMD_Single_Point = "wB97M-V/def2-TZVPP/SMD Single Point" wB97M_V_def2_TZVPP_SMD_Transition_State_Geometry_Optimization = ( "wB97M-V/def2-TZVPP/SMD Transition State Geometry Optimization" ) wB97M_V_def2_TZVPP_SMD_Unknown = "wB97M-V/def2-TZVPP/SMD Unknown" wB97M_V_def2_TZVPP_VACUUM_Force = "wB97M-V/def2-TZVPP/VACUUM Force" wB97M_V_def2_TZVPP_VACUUM_Frequency_Analysis = ( "wB97M-V/def2-TZVPP/VACUUM Frequency Analysis" ) wB97M_V_def2_TZVPP_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "wB97M-V/def2-TZVPP/VACUUM Frequency Flattening Geometry Optimization" ) wB97M_V_def2_TZVPP_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97M-V/def2-TZVPP/VACUUM Frequency Flattening Transition State Geometry Optimization" wB97M_V_def2_TZVPP_VACUUM_Geometry_Optimization = ( "wB97M-V/def2-TZVPP/VACUUM Geometry Optimization" ) wB97M_V_def2_TZVPP_VACUUM_Single_Point = "wB97M-V/def2-TZVPP/VACUUM Single Point" wB97M_V_def2_TZVPP_VACUUM_Transition_State_Geometry_Optimization = ( "wB97M-V/def2-TZVPP/VACUUM Transition State Geometry Optimization" ) wB97M_V_def2_TZVPP_VACUUM_Unknown = "wB97M-V/def2-TZVPP/VACUUM Unknown" wB97M_V_def2_TZVP_PCM_Force = "wB97M-V/def2-TZVP/PCM Force" wB97M_V_def2_TZVP_PCM_Frequency_Analysis = ( "wB97M-V/def2-TZVP/PCM Frequency Analysis" ) wB97M_V_def2_TZVP_PCM_Frequency_Flattening_Geometry_Optimization = ( "wB97M-V/def2-TZVP/PCM Frequency Flattening Geometry Optimization" ) wB97M_V_def2_TZVP_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97M-V/def2-TZVP/PCM Frequency Flattening Transition State Geometry Optimization" wB97M_V_def2_TZVP_PCM_Geometry_Optimization = ( "wB97M-V/def2-TZVP/PCM Geometry Optimization" ) wB97M_V_def2_TZVP_PCM_Single_Point = "wB97M-V/def2-TZVP/PCM Single Point" wB97M_V_def2_TZVP_PCM_Transition_State_Geometry_Optimization = ( "wB97M-V/def2-TZVP/PCM Transition State Geometry Optimization" ) wB97M_V_def2_TZVP_PCM_Unknown = "wB97M-V/def2-TZVP/PCM Unknown" wB97M_V_def2_TZVP_SMD_Force = "wB97M-V/def2-TZVP/SMD Force" wB97M_V_def2_TZVP_SMD_Frequency_Analysis = ( "wB97M-V/def2-TZVP/SMD Frequency Analysis" ) wB97M_V_def2_TZVP_SMD_Frequency_Flattening_Geometry_Optimization = ( "wB97M-V/def2-TZVP/SMD Frequency Flattening Geometry Optimization" ) wB97M_V_def2_TZVP_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97M-V/def2-TZVP/SMD Frequency Flattening Transition State Geometry Optimization" wB97M_V_def2_TZVP_SMD_Geometry_Optimization = ( "wB97M-V/def2-TZVP/SMD Geometry Optimization" ) wB97M_V_def2_TZVP_SMD_Single_Point = "wB97M-V/def2-TZVP/SMD Single Point" wB97M_V_def2_TZVP_SMD_Transition_State_Geometry_Optimization = ( "wB97M-V/def2-TZVP/SMD Transition State Geometry Optimization" ) wB97M_V_def2_TZVP_SMD_Unknown = "wB97M-V/def2-TZVP/SMD Unknown" wB97M_V_def2_TZVP_VACUUM_Force = "wB97M-V/def2-TZVP/VACUUM Force" wB97M_V_def2_TZVP_VACUUM_Frequency_Analysis = ( "wB97M-V/def2-TZVP/VACUUM Frequency Analysis" ) wB97M_V_def2_TZVP_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "wB97M-V/def2-TZVP/VACUUM Frequency Flattening Geometry Optimization" ) wB97M_V_def2_TZVP_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97M-V/def2-TZVP/VACUUM Frequency Flattening Transition State Geometry Optimization" wB97M_V_def2_TZVP_VACUUM_Geometry_Optimization = ( "wB97M-V/def2-TZVP/VACUUM Geometry Optimization" ) wB97M_V_def2_TZVP_VACUUM_Single_Point = "wB97M-V/def2-TZVP/VACUUM Single Point" wB97M_V_def2_TZVP_VACUUM_Transition_State_Geometry_Optimization = ( "wB97M-V/def2-TZVP/VACUUM Transition State Geometry Optimization" ) wB97M_V_def2_TZVP_VACUUM_Unknown = "wB97M-V/def2-TZVP/VACUUM Unknown" wB97X_D3_6_31g_d_PCM_Force = "wB97X-D3/6-31g*/PCM Force" wB97X_D3_6_31g_d_PCM_Frequency_Analysis = "wB97X-D3/6-31g*/PCM Frequency Analysis" wB97X_D3_6_31g_d_PCM_Frequency_Flattening_Geometry_Optimization = ( "wB97X-D3/6-31g*/PCM Frequency Flattening Geometry Optimization" ) wB97X_D3_6_31g_d_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-D3/6-31g*/PCM Frequency Flattening Transition State Geometry Optimization" wB97X_D3_6_31g_d_PCM_Geometry_Optimization = ( "wB97X-D3/6-31g*/PCM Geometry Optimization" ) wB97X_D3_6_31g_d_PCM_Single_Point = "wB97X-D3/6-31g*/PCM Single Point" wB97X_D3_6_31g_d_PCM_Transition_State_Geometry_Optimization = ( "wB97X-D3/6-31g*/PCM Transition State Geometry Optimization" ) wB97X_D3_6_31g_d_PCM_Unknown = "wB97X-D3/6-31g*/PCM Unknown" wB97X_D3_6_31g_d_SMD_Force = "wB97X-D3/6-31g*/SMD Force" wB97X_D3_6_31g_d_SMD_Frequency_Analysis = "wB97X-D3/6-31g*/SMD Frequency Analysis" wB97X_D3_6_31g_d_SMD_Frequency_Flattening_Geometry_Optimization = ( "wB97X-D3/6-31g*/SMD Frequency Flattening Geometry Optimization" ) wB97X_D3_6_31g_d_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-D3/6-31g*/SMD Frequency Flattening Transition State Geometry Optimization" wB97X_D3_6_31g_d_SMD_Geometry_Optimization = ( "wB97X-D3/6-31g*/SMD Geometry Optimization" ) wB97X_D3_6_31g_d_SMD_Single_Point = "wB97X-D3/6-31g*/SMD Single Point" wB97X_D3_6_31g_d_SMD_Transition_State_Geometry_Optimization = ( "wB97X-D3/6-31g*/SMD Transition State Geometry Optimization" ) wB97X_D3_6_31g_d_SMD_Unknown = "wB97X-D3/6-31g*/SMD Unknown" wB97X_D3_6_31g_d_VACUUM_Force = "wB97X-D3/6-31g*/VACUUM Force" wB97X_D3_6_31g_d_VACUUM_Frequency_Analysis = ( "wB97X-D3/6-31g*/VACUUM Frequency Analysis" ) wB97X_D3_6_31g_d_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "wB97X-D3/6-31g*/VACUUM Frequency Flattening Geometry Optimization" ) wB97X_D3_6_31g_d_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-D3/6-31g*/VACUUM Frequency Flattening Transition State Geometry Optimization" wB97X_D3_6_31g_d_VACUUM_Geometry_Optimization = ( "wB97X-D3/6-31g*/VACUUM Geometry Optimization" ) wB97X_D3_6_31g_d_VACUUM_Single_Point = "wB97X-D3/6-31g*/VACUUM Single Point" wB97X_D3_6_31g_d_VACUUM_Transition_State_Geometry_Optimization = ( "wB97X-D3/6-31g*/VACUUM Transition State Geometry Optimization" ) wB97X_D3_6_31g_d_VACUUM_Unknown = "wB97X-D3/6-31g*/VACUUM Unknown" wB97X_D3_def2_QZVPD_PCM_Force = "wB97X-D3/def2-QZVPD/PCM Force" wB97X_D3_def2_QZVPD_PCM_Frequency_Analysis = ( "wB97X-D3/def2-QZVPD/PCM Frequency Analysis" ) wB97X_D3_def2_QZVPD_PCM_Frequency_Flattening_Geometry_Optimization = ( "wB97X-D3/def2-QZVPD/PCM Frequency Flattening Geometry Optimization" ) wB97X_D3_def2_QZVPD_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-D3/def2-QZVPD/PCM Frequency Flattening Transition State Geometry Optimization" wB97X_D3_def2_QZVPD_PCM_Geometry_Optimization = ( "wB97X-D3/def2-QZVPD/PCM Geometry Optimization" ) wB97X_D3_def2_QZVPD_PCM_Single_Point = "wB97X-D3/def2-QZVPD/PCM Single Point" wB97X_D3_def2_QZVPD_PCM_Transition_State_Geometry_Optimization = ( "wB97X-D3/def2-QZVPD/PCM Transition State Geometry Optimization" ) wB97X_D3_def2_QZVPD_PCM_Unknown = "wB97X-D3/def2-QZVPD/PCM Unknown" wB97X_D3_def2_QZVPD_SMD_Force = "wB97X-D3/def2-QZVPD/SMD Force" wB97X_D3_def2_QZVPD_SMD_Frequency_Analysis = ( "wB97X-D3/def2-QZVPD/SMD Frequency Analysis" ) wB97X_D3_def2_QZVPD_SMD_Frequency_Flattening_Geometry_Optimization = ( "wB97X-D3/def2-QZVPD/SMD Frequency Flattening Geometry Optimization" ) wB97X_D3_def2_QZVPD_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-D3/def2-QZVPD/SMD Frequency Flattening Transition State Geometry Optimization" wB97X_D3_def2_QZVPD_SMD_Geometry_Optimization = ( "wB97X-D3/def2-QZVPD/SMD Geometry Optimization" ) wB97X_D3_def2_QZVPD_SMD_Single_Point = "wB97X-D3/def2-QZVPD/SMD Single Point" wB97X_D3_def2_QZVPD_SMD_Transition_State_Geometry_Optimization = ( "wB97X-D3/def2-QZVPD/SMD Transition State Geometry Optimization" ) wB97X_D3_def2_QZVPD_SMD_Unknown = "wB97X-D3/def2-QZVPD/SMD Unknown" wB97X_D3_def2_QZVPD_VACUUM_Force = "wB97X-D3/def2-QZVPD/VACUUM Force" wB97X_D3_def2_QZVPD_VACUUM_Frequency_Analysis = ( "wB97X-D3/def2-QZVPD/VACUUM Frequency Analysis" ) wB97X_D3_def2_QZVPD_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "wB97X-D3/def2-QZVPD/VACUUM Frequency Flattening Geometry Optimization" ) wB97X_D3_def2_QZVPD_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-D3/def2-QZVPD/VACUUM Frequency Flattening Transition State Geometry Optimization" wB97X_D3_def2_QZVPD_VACUUM_Geometry_Optimization = ( "wB97X-D3/def2-QZVPD/VACUUM Geometry Optimization" ) wB97X_D3_def2_QZVPD_VACUUM_Single_Point = "wB97X-D3/def2-QZVPD/VACUUM Single Point" wB97X_D3_def2_QZVPD_VACUUM_Transition_State_Geometry_Optimization = ( "wB97X-D3/def2-QZVPD/VACUUM Transition State Geometry Optimization" ) wB97X_D3_def2_QZVPD_VACUUM_Unknown = "wB97X-D3/def2-QZVPD/VACUUM Unknown" wB97X_D3_def2_QZVPPD_PCM_Force = "wB97X-D3/def2-QZVPPD/PCM Force" wB97X_D3_def2_QZVPPD_PCM_Frequency_Analysis = ( "wB97X-D3/def2-QZVPPD/PCM Frequency Analysis" ) wB97X_D3_def2_QZVPPD_PCM_Frequency_Flattening_Geometry_Optimization = ( "wB97X-D3/def2-QZVPPD/PCM Frequency Flattening Geometry Optimization" ) wB97X_D3_def2_QZVPPD_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-D3/def2-QZVPPD/PCM Frequency Flattening Transition State Geometry Optimization" wB97X_D3_def2_QZVPPD_PCM_Geometry_Optimization = ( "wB97X-D3/def2-QZVPPD/PCM Geometry Optimization" ) wB97X_D3_def2_QZVPPD_PCM_Single_Point = "wB97X-D3/def2-QZVPPD/PCM Single Point" wB97X_D3_def2_QZVPPD_PCM_Transition_State_Geometry_Optimization = ( "wB97X-D3/def2-QZVPPD/PCM Transition State Geometry Optimization" ) wB97X_D3_def2_QZVPPD_PCM_Unknown = "wB97X-D3/def2-QZVPPD/PCM Unknown" wB97X_D3_def2_QZVPPD_SMD_Force = "wB97X-D3/def2-QZVPPD/SMD Force" wB97X_D3_def2_QZVPPD_SMD_Frequency_Analysis = ( "wB97X-D3/def2-QZVPPD/SMD Frequency Analysis" ) wB97X_D3_def2_QZVPPD_SMD_Frequency_Flattening_Geometry_Optimization = ( "wB97X-D3/def2-QZVPPD/SMD Frequency Flattening Geometry Optimization" ) wB97X_D3_def2_QZVPPD_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-D3/def2-QZVPPD/SMD Frequency Flattening Transition State Geometry Optimization" wB97X_D3_def2_QZVPPD_SMD_Geometry_Optimization = ( "wB97X-D3/def2-QZVPPD/SMD Geometry Optimization" ) wB97X_D3_def2_QZVPPD_SMD_Single_Point = "wB97X-D3/def2-QZVPPD/SMD Single Point" wB97X_D3_def2_QZVPPD_SMD_Transition_State_Geometry_Optimization = ( "wB97X-D3/def2-QZVPPD/SMD Transition State Geometry Optimization" ) wB97X_D3_def2_QZVPPD_SMD_Unknown = "wB97X-D3/def2-QZVPPD/SMD Unknown" wB97X_D3_def2_QZVPPD_VACUUM_Force = "wB97X-D3/def2-QZVPPD/VACUUM Force" wB97X_D3_def2_QZVPPD_VACUUM_Frequency_Analysis = ( "wB97X-D3/def2-QZVPPD/VACUUM Frequency Analysis" ) wB97X_D3_def2_QZVPPD_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "wB97X-D3/def2-QZVPPD/VACUUM Frequency Flattening Geometry Optimization" ) wB97X_D3_def2_QZVPPD_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-D3/def2-QZVPPD/VACUUM Frequency Flattening Transition State Geometry Optimization" wB97X_D3_def2_QZVPPD_VACUUM_Geometry_Optimization = ( "wB97X-D3/def2-QZVPPD/VACUUM Geometry Optimization" ) wB97X_D3_def2_QZVPPD_VACUUM_Single_Point = ( "wB97X-D3/def2-QZVPPD/VACUUM Single Point" ) wB97X_D3_def2_QZVPPD_VACUUM_Transition_State_Geometry_Optimization = ( "wB97X-D3/def2-QZVPPD/VACUUM Transition State Geometry Optimization" ) wB97X_D3_def2_QZVPPD_VACUUM_Unknown = "wB97X-D3/def2-QZVPPD/VACUUM Unknown" wB97X_D3_def2_SVPD_PCM_Force = "wB97X-D3/def2-SVPD/PCM Force" wB97X_D3_def2_SVPD_PCM_Frequency_Analysis = ( "wB97X-D3/def2-SVPD/PCM Frequency Analysis" ) wB97X_D3_def2_SVPD_PCM_Frequency_Flattening_Geometry_Optimization = ( "wB97X-D3/def2-SVPD/PCM Frequency Flattening Geometry Optimization" ) wB97X_D3_def2_SVPD_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-D3/def2-SVPD/PCM Frequency Flattening Transition State Geometry Optimization" wB97X_D3_def2_SVPD_PCM_Geometry_Optimization = ( "wB97X-D3/def2-SVPD/PCM Geometry Optimization" ) wB97X_D3_def2_SVPD_PCM_Single_Point = "wB97X-D3/def2-SVPD/PCM Single Point" wB97X_D3_def2_SVPD_PCM_Transition_State_Geometry_Optimization = ( "wB97X-D3/def2-SVPD/PCM Transition State Geometry Optimization" ) wB97X_D3_def2_SVPD_PCM_Unknown = "wB97X-D3/def2-SVPD/PCM Unknown" wB97X_D3_def2_SVPD_SMD_Force = "wB97X-D3/def2-SVPD/SMD Force" wB97X_D3_def2_SVPD_SMD_Frequency_Analysis = ( "wB97X-D3/def2-SVPD/SMD Frequency Analysis" ) wB97X_D3_def2_SVPD_SMD_Frequency_Flattening_Geometry_Optimization = ( "wB97X-D3/def2-SVPD/SMD Frequency Flattening Geometry Optimization" ) wB97X_D3_def2_SVPD_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-D3/def2-SVPD/SMD Frequency Flattening Transition State Geometry Optimization" wB97X_D3_def2_SVPD_SMD_Geometry_Optimization = ( "wB97X-D3/def2-SVPD/SMD Geometry Optimization" ) wB97X_D3_def2_SVPD_SMD_Single_Point = "wB97X-D3/def2-SVPD/SMD Single Point" wB97X_D3_def2_SVPD_SMD_Transition_State_Geometry_Optimization = ( "wB97X-D3/def2-SVPD/SMD Transition State Geometry Optimization" ) wB97X_D3_def2_SVPD_SMD_Unknown = "wB97X-D3/def2-SVPD/SMD Unknown" wB97X_D3_def2_SVPD_VACUUM_Force = "wB97X-D3/def2-SVPD/VACUUM Force" wB97X_D3_def2_SVPD_VACUUM_Frequency_Analysis = ( "wB97X-D3/def2-SVPD/VACUUM Frequency Analysis" ) wB97X_D3_def2_SVPD_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "wB97X-D3/def2-SVPD/VACUUM Frequency Flattening Geometry Optimization" ) wB97X_D3_def2_SVPD_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-D3/def2-SVPD/VACUUM Frequency Flattening Transition State Geometry Optimization" wB97X_D3_def2_SVPD_VACUUM_Geometry_Optimization = ( "wB97X-D3/def2-SVPD/VACUUM Geometry Optimization" ) wB97X_D3_def2_SVPD_VACUUM_Single_Point = "wB97X-D3/def2-SVPD/VACUUM Single Point" wB97X_D3_def2_SVPD_VACUUM_Transition_State_Geometry_Optimization = ( "wB97X-D3/def2-SVPD/VACUUM Transition State Geometry Optimization" ) wB97X_D3_def2_SVPD_VACUUM_Unknown = "wB97X-D3/def2-SVPD/VACUUM Unknown" wB97X_D3_def2_TZVPD_PCM_Force = "wB97X-D3/def2-TZVPD/PCM Force" wB97X_D3_def2_TZVPD_PCM_Frequency_Analysis = ( "wB97X-D3/def2-TZVPD/PCM Frequency Analysis" ) wB97X_D3_def2_TZVPD_PCM_Frequency_Flattening_Geometry_Optimization = ( "wB97X-D3/def2-TZVPD/PCM Frequency Flattening Geometry Optimization" ) wB97X_D3_def2_TZVPD_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-D3/def2-TZVPD/PCM Frequency Flattening Transition State Geometry Optimization" wB97X_D3_def2_TZVPD_PCM_Geometry_Optimization = ( "wB97X-D3/def2-TZVPD/PCM Geometry Optimization" ) wB97X_D3_def2_TZVPD_PCM_Single_Point = "wB97X-D3/def2-TZVPD/PCM Single Point" wB97X_D3_def2_TZVPD_PCM_Transition_State_Geometry_Optimization = ( "wB97X-D3/def2-TZVPD/PCM Transition State Geometry Optimization" ) wB97X_D3_def2_TZVPD_PCM_Unknown = "wB97X-D3/def2-TZVPD/PCM Unknown" wB97X_D3_def2_TZVPD_SMD_Force = "wB97X-D3/def2-TZVPD/SMD Force" wB97X_D3_def2_TZVPD_SMD_Frequency_Analysis = ( "wB97X-D3/def2-TZVPD/SMD Frequency Analysis" ) wB97X_D3_def2_TZVPD_SMD_Frequency_Flattening_Geometry_Optimization = ( "wB97X-D3/def2-TZVPD/SMD Frequency Flattening Geometry Optimization" ) wB97X_D3_def2_TZVPD_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-D3/def2-TZVPD/SMD Frequency Flattening Transition State Geometry Optimization" wB97X_D3_def2_TZVPD_SMD_Geometry_Optimization = ( "wB97X-D3/def2-TZVPD/SMD Geometry Optimization" ) wB97X_D3_def2_TZVPD_SMD_Single_Point = "wB97X-D3/def2-TZVPD/SMD Single Point" wB97X_D3_def2_TZVPD_SMD_Transition_State_Geometry_Optimization = ( "wB97X-D3/def2-TZVPD/SMD Transition State Geometry Optimization" ) wB97X_D3_def2_TZVPD_SMD_Unknown = "wB97X-D3/def2-TZVPD/SMD Unknown" wB97X_D3_def2_TZVPD_VACUUM_Force = "wB97X-D3/def2-TZVPD/VACUUM Force" wB97X_D3_def2_TZVPD_VACUUM_Frequency_Analysis = ( "wB97X-D3/def2-TZVPD/VACUUM Frequency Analysis" ) wB97X_D3_def2_TZVPD_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "wB97X-D3/def2-TZVPD/VACUUM Frequency Flattening Geometry Optimization" ) wB97X_D3_def2_TZVPD_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-D3/def2-TZVPD/VACUUM Frequency Flattening Transition State Geometry Optimization" wB97X_D3_def2_TZVPD_VACUUM_Geometry_Optimization = ( "wB97X-D3/def2-TZVPD/VACUUM Geometry Optimization" ) wB97X_D3_def2_TZVPD_VACUUM_Single_Point = "wB97X-D3/def2-TZVPD/VACUUM Single Point" wB97X_D3_def2_TZVPD_VACUUM_Transition_State_Geometry_Optimization = ( "wB97X-D3/def2-TZVPD/VACUUM Transition State Geometry Optimization" ) wB97X_D3_def2_TZVPD_VACUUM_Unknown = "wB97X-D3/def2-TZVPD/VACUUM Unknown" wB97X_D3_def2_TZVPPD_PCM_Force = "wB97X-D3/def2-TZVPPD/PCM Force" wB97X_D3_def2_TZVPPD_PCM_Frequency_Analysis = ( "wB97X-D3/def2-TZVPPD/PCM Frequency Analysis" ) wB97X_D3_def2_TZVPPD_PCM_Frequency_Flattening_Geometry_Optimization = ( "wB97X-D3/def2-TZVPPD/PCM Frequency Flattening Geometry Optimization" ) wB97X_D3_def2_TZVPPD_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-D3/def2-TZVPPD/PCM Frequency Flattening Transition State Geometry Optimization" wB97X_D3_def2_TZVPPD_PCM_Geometry_Optimization = ( "wB97X-D3/def2-TZVPPD/PCM Geometry Optimization" ) wB97X_D3_def2_TZVPPD_PCM_Single_Point = "wB97X-D3/def2-TZVPPD/PCM Single Point" wB97X_D3_def2_TZVPPD_PCM_Transition_State_Geometry_Optimization = ( "wB97X-D3/def2-TZVPPD/PCM Transition State Geometry Optimization" ) wB97X_D3_def2_TZVPPD_PCM_Unknown = "wB97X-D3/def2-TZVPPD/PCM Unknown" wB97X_D3_def2_TZVPPD_SMD_Force = "wB97X-D3/def2-TZVPPD/SMD Force" wB97X_D3_def2_TZVPPD_SMD_Frequency_Analysis = ( "wB97X-D3/def2-TZVPPD/SMD Frequency Analysis" ) wB97X_D3_def2_TZVPPD_SMD_Frequency_Flattening_Geometry_Optimization = ( "wB97X-D3/def2-TZVPPD/SMD Frequency Flattening Geometry Optimization" ) wB97X_D3_def2_TZVPPD_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-D3/def2-TZVPPD/SMD Frequency Flattening Transition State Geometry Optimization" wB97X_D3_def2_TZVPPD_SMD_Geometry_Optimization = ( "wB97X-D3/def2-TZVPPD/SMD Geometry Optimization" ) wB97X_D3_def2_TZVPPD_SMD_Single_Point = "wB97X-D3/def2-TZVPPD/SMD Single Point" wB97X_D3_def2_TZVPPD_SMD_Transition_State_Geometry_Optimization = ( "wB97X-D3/def2-TZVPPD/SMD Transition State Geometry Optimization" ) wB97X_D3_def2_TZVPPD_SMD_Unknown = "wB97X-D3/def2-TZVPPD/SMD Unknown" wB97X_D3_def2_TZVPPD_VACUUM_Force = "wB97X-D3/def2-TZVPPD/VACUUM Force" wB97X_D3_def2_TZVPPD_VACUUM_Frequency_Analysis = ( "wB97X-D3/def2-TZVPPD/VACUUM Frequency Analysis" ) wB97X_D3_def2_TZVPPD_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "wB97X-D3/def2-TZVPPD/VACUUM Frequency Flattening Geometry Optimization" ) wB97X_D3_def2_TZVPPD_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-D3/def2-TZVPPD/VACUUM Frequency Flattening Transition State Geometry Optimization" wB97X_D3_def2_TZVPPD_VACUUM_Geometry_Optimization = ( "wB97X-D3/def2-TZVPPD/VACUUM Geometry Optimization" ) wB97X_D3_def2_TZVPPD_VACUUM_Single_Point = ( "wB97X-D3/def2-TZVPPD/VACUUM Single Point" ) wB97X_D3_def2_TZVPPD_VACUUM_Transition_State_Geometry_Optimization = ( "wB97X-D3/def2-TZVPPD/VACUUM Transition State Geometry Optimization" ) wB97X_D3_def2_TZVPPD_VACUUM_Unknown = "wB97X-D3/def2-TZVPPD/VACUUM Unknown" wB97X_D3_def2_TZVPP_PCM_Force = "wB97X-D3/def2-TZVPP/PCM Force" wB97X_D3_def2_TZVPP_PCM_Frequency_Analysis = ( "wB97X-D3/def2-TZVPP/PCM Frequency Analysis" ) wB97X_D3_def2_TZVPP_PCM_Frequency_Flattening_Geometry_Optimization = ( "wB97X-D3/def2-TZVPP/PCM Frequency Flattening Geometry Optimization" ) wB97X_D3_def2_TZVPP_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-D3/def2-TZVPP/PCM Frequency Flattening Transition State Geometry Optimization" wB97X_D3_def2_TZVPP_PCM_Geometry_Optimization = ( "wB97X-D3/def2-TZVPP/PCM Geometry Optimization" ) wB97X_D3_def2_TZVPP_PCM_Single_Point = "wB97X-D3/def2-TZVPP/PCM Single Point" wB97X_D3_def2_TZVPP_PCM_Transition_State_Geometry_Optimization = ( "wB97X-D3/def2-TZVPP/PCM Transition State Geometry Optimization" ) wB97X_D3_def2_TZVPP_PCM_Unknown = "wB97X-D3/def2-TZVPP/PCM Unknown" wB97X_D3_def2_TZVPP_SMD_Force = "wB97X-D3/def2-TZVPP/SMD Force" wB97X_D3_def2_TZVPP_SMD_Frequency_Analysis = ( "wB97X-D3/def2-TZVPP/SMD Frequency Analysis" ) wB97X_D3_def2_TZVPP_SMD_Frequency_Flattening_Geometry_Optimization = ( "wB97X-D3/def2-TZVPP/SMD Frequency Flattening Geometry Optimization" ) wB97X_D3_def2_TZVPP_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-D3/def2-TZVPP/SMD Frequency Flattening Transition State Geometry Optimization" wB97X_D3_def2_TZVPP_SMD_Geometry_Optimization = ( "wB97X-D3/def2-TZVPP/SMD Geometry Optimization" ) wB97X_D3_def2_TZVPP_SMD_Single_Point = "wB97X-D3/def2-TZVPP/SMD Single Point" wB97X_D3_def2_TZVPP_SMD_Transition_State_Geometry_Optimization = ( "wB97X-D3/def2-TZVPP/SMD Transition State Geometry Optimization" ) wB97X_D3_def2_TZVPP_SMD_Unknown = "wB97X-D3/def2-TZVPP/SMD Unknown" wB97X_D3_def2_TZVPP_VACUUM_Force = "wB97X-D3/def2-TZVPP/VACUUM Force" wB97X_D3_def2_TZVPP_VACUUM_Frequency_Analysis = ( "wB97X-D3/def2-TZVPP/VACUUM Frequency Analysis" ) wB97X_D3_def2_TZVPP_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "wB97X-D3/def2-TZVPP/VACUUM Frequency Flattening Geometry Optimization" ) wB97X_D3_def2_TZVPP_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-D3/def2-TZVPP/VACUUM Frequency Flattening Transition State Geometry Optimization" wB97X_D3_def2_TZVPP_VACUUM_Geometry_Optimization = ( "wB97X-D3/def2-TZVPP/VACUUM Geometry Optimization" ) wB97X_D3_def2_TZVPP_VACUUM_Single_Point = "wB97X-D3/def2-TZVPP/VACUUM Single Point" wB97X_D3_def2_TZVPP_VACUUM_Transition_State_Geometry_Optimization = ( "wB97X-D3/def2-TZVPP/VACUUM Transition State Geometry Optimization" ) wB97X_D3_def2_TZVPP_VACUUM_Unknown = "wB97X-D3/def2-TZVPP/VACUUM Unknown" wB97X_D3_def2_TZVP_PCM_Force = "wB97X-D3/def2-TZVP/PCM Force" wB97X_D3_def2_TZVP_PCM_Frequency_Analysis = ( "wB97X-D3/def2-TZVP/PCM Frequency Analysis" ) wB97X_D3_def2_TZVP_PCM_Frequency_Flattening_Geometry_Optimization = ( "wB97X-D3/def2-TZVP/PCM Frequency Flattening Geometry Optimization" ) wB97X_D3_def2_TZVP_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-D3/def2-TZVP/PCM Frequency Flattening Transition State Geometry Optimization" wB97X_D3_def2_TZVP_PCM_Geometry_Optimization = ( "wB97X-D3/def2-TZVP/PCM Geometry Optimization" ) wB97X_D3_def2_TZVP_PCM_Single_Point = "wB97X-D3/def2-TZVP/PCM Single Point" wB97X_D3_def2_TZVP_PCM_Transition_State_Geometry_Optimization = ( "wB97X-D3/def2-TZVP/PCM Transition State Geometry Optimization" ) wB97X_D3_def2_TZVP_PCM_Unknown = "wB97X-D3/def2-TZVP/PCM Unknown" wB97X_D3_def2_TZVP_SMD_Force = "wB97X-D3/def2-TZVP/SMD Force" wB97X_D3_def2_TZVP_SMD_Frequency_Analysis = ( "wB97X-D3/def2-TZVP/SMD Frequency Analysis" ) wB97X_D3_def2_TZVP_SMD_Frequency_Flattening_Geometry_Optimization = ( "wB97X-D3/def2-TZVP/SMD Frequency Flattening Geometry Optimization" ) wB97X_D3_def2_TZVP_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-D3/def2-TZVP/SMD Frequency Flattening Transition State Geometry Optimization" wB97X_D3_def2_TZVP_SMD_Geometry_Optimization = ( "wB97X-D3/def2-TZVP/SMD Geometry Optimization" ) wB97X_D3_def2_TZVP_SMD_Single_Point = "wB97X-D3/def2-TZVP/SMD Single Point" wB97X_D3_def2_TZVP_SMD_Transition_State_Geometry_Optimization = ( "wB97X-D3/def2-TZVP/SMD Transition State Geometry Optimization" ) wB97X_D3_def2_TZVP_SMD_Unknown = "wB97X-D3/def2-TZVP/SMD Unknown" wB97X_D3_def2_TZVP_VACUUM_Force = "wB97X-D3/def2-TZVP/VACUUM Force" wB97X_D3_def2_TZVP_VACUUM_Frequency_Analysis = ( "wB97X-D3/def2-TZVP/VACUUM Frequency Analysis" ) wB97X_D3_def2_TZVP_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "wB97X-D3/def2-TZVP/VACUUM Frequency Flattening Geometry Optimization" ) wB97X_D3_def2_TZVP_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-D3/def2-TZVP/VACUUM Frequency Flattening Transition State Geometry Optimization" wB97X_D3_def2_TZVP_VACUUM_Geometry_Optimization = ( "wB97X-D3/def2-TZVP/VACUUM Geometry Optimization" ) wB97X_D3_def2_TZVP_VACUUM_Single_Point = "wB97X-D3/def2-TZVP/VACUUM Single Point" wB97X_D3_def2_TZVP_VACUUM_Transition_State_Geometry_Optimization = ( "wB97X-D3/def2-TZVP/VACUUM Transition State Geometry Optimization" ) wB97X_D3_def2_TZVP_VACUUM_Unknown = "wB97X-D3/def2-TZVP/VACUUM Unknown" wB97X_D_6_31g_d_PCM_Force = "wB97X-D/6-31g*/PCM Force" wB97X_D_6_31g_d_PCM_Frequency_Analysis = "wB97X-D/6-31g*/PCM Frequency Analysis" wB97X_D_6_31g_d_PCM_Frequency_Flattening_Geometry_Optimization = ( "wB97X-D/6-31g*/PCM Frequency Flattening Geometry Optimization" ) wB97X_D_6_31g_d_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = ( "wB97X-D/6-31g*/PCM Frequency Flattening Transition State Geometry Optimization" ) wB97X_D_6_31g_d_PCM_Geometry_Optimization = ( "wB97X-D/6-31g*/PCM Geometry Optimization" ) wB97X_D_6_31g_d_PCM_Single_Point = "wB97X-D/6-31g*/PCM Single Point" wB97X_D_6_31g_d_PCM_Transition_State_Geometry_Optimization = ( "wB97X-D/6-31g*/PCM Transition State Geometry Optimization" ) wB97X_D_6_31g_d_PCM_Unknown = "wB97X-D/6-31g*/PCM Unknown" wB97X_D_6_31g_d_SMD_Force = "wB97X-D/6-31g*/SMD Force" wB97X_D_6_31g_d_SMD_Frequency_Analysis = "wB97X-D/6-31g*/SMD Frequency Analysis" wB97X_D_6_31g_d_SMD_Frequency_Flattening_Geometry_Optimization = ( "wB97X-D/6-31g*/SMD Frequency Flattening Geometry Optimization" ) wB97X_D_6_31g_d_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = ( "wB97X-D/6-31g*/SMD Frequency Flattening Transition State Geometry Optimization" ) wB97X_D_6_31g_d_SMD_Geometry_Optimization = ( "wB97X-D/6-31g*/SMD Geometry Optimization" ) wB97X_D_6_31g_d_SMD_Single_Point = "wB97X-D/6-31g*/SMD Single Point" wB97X_D_6_31g_d_SMD_Transition_State_Geometry_Optimization = ( "wB97X-D/6-31g*/SMD Transition State Geometry Optimization" ) wB97X_D_6_31g_d_SMD_Unknown = "wB97X-D/6-31g*/SMD Unknown" wB97X_D_6_31g_d_VACUUM_Force = "wB97X-D/6-31g*/VACUUM Force" wB97X_D_6_31g_d_VACUUM_Frequency_Analysis = ( "wB97X-D/6-31g*/VACUUM Frequency Analysis" ) wB97X_D_6_31g_d_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "wB97X-D/6-31g*/VACUUM Frequency Flattening Geometry Optimization" ) wB97X_D_6_31g_d_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-D/6-31g*/VACUUM Frequency Flattening Transition State Geometry Optimization" wB97X_D_6_31g_d_VACUUM_Geometry_Optimization = ( "wB97X-D/6-31g*/VACUUM Geometry Optimization" ) wB97X_D_6_31g_d_VACUUM_Single_Point = "wB97X-D/6-31g*/VACUUM Single Point" wB97X_D_6_31g_d_VACUUM_Transition_State_Geometry_Optimization = ( "wB97X-D/6-31g*/VACUUM Transition State Geometry Optimization" ) wB97X_D_6_31g_d_VACUUM_Unknown = "wB97X-D/6-31g*/VACUUM Unknown" wB97X_D_def2_QZVPD_PCM_Force = "wB97X-D/def2-QZVPD/PCM Force" wB97X_D_def2_QZVPD_PCM_Frequency_Analysis = ( "wB97X-D/def2-QZVPD/PCM Frequency Analysis" ) wB97X_D_def2_QZVPD_PCM_Frequency_Flattening_Geometry_Optimization = ( "wB97X-D/def2-QZVPD/PCM Frequency Flattening Geometry Optimization" ) wB97X_D_def2_QZVPD_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-D/def2-QZVPD/PCM Frequency Flattening Transition State Geometry Optimization" wB97X_D_def2_QZVPD_PCM_Geometry_Optimization = ( "wB97X-D/def2-QZVPD/PCM Geometry Optimization" ) wB97X_D_def2_QZVPD_PCM_Single_Point = "wB97X-D/def2-QZVPD/PCM Single Point" wB97X_D_def2_QZVPD_PCM_Transition_State_Geometry_Optimization = ( "wB97X-D/def2-QZVPD/PCM Transition State Geometry Optimization" ) wB97X_D_def2_QZVPD_PCM_Unknown = "wB97X-D/def2-QZVPD/PCM Unknown" wB97X_D_def2_QZVPD_SMD_Force = "wB97X-D/def2-QZVPD/SMD Force" wB97X_D_def2_QZVPD_SMD_Frequency_Analysis = ( "wB97X-D/def2-QZVPD/SMD Frequency Analysis" ) wB97X_D_def2_QZVPD_SMD_Frequency_Flattening_Geometry_Optimization = ( "wB97X-D/def2-QZVPD/SMD Frequency Flattening Geometry Optimization" ) wB97X_D_def2_QZVPD_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-D/def2-QZVPD/SMD Frequency Flattening Transition State Geometry Optimization" wB97X_D_def2_QZVPD_SMD_Geometry_Optimization = ( "wB97X-D/def2-QZVPD/SMD Geometry Optimization" ) wB97X_D_def2_QZVPD_SMD_Single_Point = "wB97X-D/def2-QZVPD/SMD Single Point" wB97X_D_def2_QZVPD_SMD_Transition_State_Geometry_Optimization = ( "wB97X-D/def2-QZVPD/SMD Transition State Geometry Optimization" ) wB97X_D_def2_QZVPD_SMD_Unknown = "wB97X-D/def2-QZVPD/SMD Unknown" wB97X_D_def2_QZVPD_VACUUM_Force = "wB97X-D/def2-QZVPD/VACUUM Force" wB97X_D_def2_QZVPD_VACUUM_Frequency_Analysis = ( "wB97X-D/def2-QZVPD/VACUUM Frequency Analysis" ) wB97X_D_def2_QZVPD_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "wB97X-D/def2-QZVPD/VACUUM Frequency Flattening Geometry Optimization" ) wB97X_D_def2_QZVPD_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-D/def2-QZVPD/VACUUM Frequency Flattening Transition State Geometry Optimization" wB97X_D_def2_QZVPD_VACUUM_Geometry_Optimization = ( "wB97X-D/def2-QZVPD/VACUUM Geometry Optimization" ) wB97X_D_def2_QZVPD_VACUUM_Single_Point = "wB97X-D/def2-QZVPD/VACUUM Single Point" wB97X_D_def2_QZVPD_VACUUM_Transition_State_Geometry_Optimization = ( "wB97X-D/def2-QZVPD/VACUUM Transition State Geometry Optimization" ) wB97X_D_def2_QZVPD_VACUUM_Unknown = "wB97X-D/def2-QZVPD/VACUUM Unknown" wB97X_D_def2_QZVPPD_PCM_Force = "wB97X-D/def2-QZVPPD/PCM Force" wB97X_D_def2_QZVPPD_PCM_Frequency_Analysis = ( "wB97X-D/def2-QZVPPD/PCM Frequency Analysis" ) wB97X_D_def2_QZVPPD_PCM_Frequency_Flattening_Geometry_Optimization = ( "wB97X-D/def2-QZVPPD/PCM Frequency Flattening Geometry Optimization" ) wB97X_D_def2_QZVPPD_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-D/def2-QZVPPD/PCM Frequency Flattening Transition State Geometry Optimization" wB97X_D_def2_QZVPPD_PCM_Geometry_Optimization = ( "wB97X-D/def2-QZVPPD/PCM Geometry Optimization" ) wB97X_D_def2_QZVPPD_PCM_Single_Point = "wB97X-D/def2-QZVPPD/PCM Single Point" wB97X_D_def2_QZVPPD_PCM_Transition_State_Geometry_Optimization = ( "wB97X-D/def2-QZVPPD/PCM Transition State Geometry Optimization" ) wB97X_D_def2_QZVPPD_PCM_Unknown = "wB97X-D/def2-QZVPPD/PCM Unknown" wB97X_D_def2_QZVPPD_SMD_Force = "wB97X-D/def2-QZVPPD/SMD Force" wB97X_D_def2_QZVPPD_SMD_Frequency_Analysis = ( "wB97X-D/def2-QZVPPD/SMD Frequency Analysis" ) wB97X_D_def2_QZVPPD_SMD_Frequency_Flattening_Geometry_Optimization = ( "wB97X-D/def2-QZVPPD/SMD Frequency Flattening Geometry Optimization" ) wB97X_D_def2_QZVPPD_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-D/def2-QZVPPD/SMD Frequency Flattening Transition State Geometry Optimization" wB97X_D_def2_QZVPPD_SMD_Geometry_Optimization = ( "wB97X-D/def2-QZVPPD/SMD Geometry Optimization" ) wB97X_D_def2_QZVPPD_SMD_Single_Point = "wB97X-D/def2-QZVPPD/SMD Single Point" wB97X_D_def2_QZVPPD_SMD_Transition_State_Geometry_Optimization = ( "wB97X-D/def2-QZVPPD/SMD Transition State Geometry Optimization" ) wB97X_D_def2_QZVPPD_SMD_Unknown = "wB97X-D/def2-QZVPPD/SMD Unknown" wB97X_D_def2_QZVPPD_VACUUM_Force = "wB97X-D/def2-QZVPPD/VACUUM Force" wB97X_D_def2_QZVPPD_VACUUM_Frequency_Analysis = ( "wB97X-D/def2-QZVPPD/VACUUM Frequency Analysis" ) wB97X_D_def2_QZVPPD_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "wB97X-D/def2-QZVPPD/VACUUM Frequency Flattening Geometry Optimization" ) wB97X_D_def2_QZVPPD_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-D/def2-QZVPPD/VACUUM Frequency Flattening Transition State Geometry Optimization" wB97X_D_def2_QZVPPD_VACUUM_Geometry_Optimization = ( "wB97X-D/def2-QZVPPD/VACUUM Geometry Optimization" ) wB97X_D_def2_QZVPPD_VACUUM_Single_Point = "wB97X-D/def2-QZVPPD/VACUUM Single Point" wB97X_D_def2_QZVPPD_VACUUM_Transition_State_Geometry_Optimization = ( "wB97X-D/def2-QZVPPD/VACUUM Transition State Geometry Optimization" ) wB97X_D_def2_QZVPPD_VACUUM_Unknown = "wB97X-D/def2-QZVPPD/VACUUM Unknown" wB97X_D_def2_SVPD_PCM_Force = "wB97X-D/def2-SVPD/PCM Force" wB97X_D_def2_SVPD_PCM_Frequency_Analysis = ( "wB97X-D/def2-SVPD/PCM Frequency Analysis" ) wB97X_D_def2_SVPD_PCM_Frequency_Flattening_Geometry_Optimization = ( "wB97X-D/def2-SVPD/PCM Frequency Flattening Geometry Optimization" ) wB97X_D_def2_SVPD_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-D/def2-SVPD/PCM Frequency Flattening Transition State Geometry Optimization" wB97X_D_def2_SVPD_PCM_Geometry_Optimization = ( "wB97X-D/def2-SVPD/PCM Geometry Optimization" ) wB97X_D_def2_SVPD_PCM_Single_Point = "wB97X-D/def2-SVPD/PCM Single Point" wB97X_D_def2_SVPD_PCM_Transition_State_Geometry_Optimization = ( "wB97X-D/def2-SVPD/PCM Transition State Geometry Optimization" ) wB97X_D_def2_SVPD_PCM_Unknown = "wB97X-D/def2-SVPD/PCM Unknown" wB97X_D_def2_SVPD_SMD_Force = "wB97X-D/def2-SVPD/SMD Force" wB97X_D_def2_SVPD_SMD_Frequency_Analysis = ( "wB97X-D/def2-SVPD/SMD Frequency Analysis" ) wB97X_D_def2_SVPD_SMD_Frequency_Flattening_Geometry_Optimization = ( "wB97X-D/def2-SVPD/SMD Frequency Flattening Geometry Optimization" ) wB97X_D_def2_SVPD_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-D/def2-SVPD/SMD Frequency Flattening Transition State Geometry Optimization" wB97X_D_def2_SVPD_SMD_Geometry_Optimization = ( "wB97X-D/def2-SVPD/SMD Geometry Optimization" ) wB97X_D_def2_SVPD_SMD_Single_Point = "wB97X-D/def2-SVPD/SMD Single Point" wB97X_D_def2_SVPD_SMD_Transition_State_Geometry_Optimization = ( "wB97X-D/def2-SVPD/SMD Transition State Geometry Optimization" ) wB97X_D_def2_SVPD_SMD_Unknown = "wB97X-D/def2-SVPD/SMD Unknown" wB97X_D_def2_SVPD_VACUUM_Force = "wB97X-D/def2-SVPD/VACUUM Force" wB97X_D_def2_SVPD_VACUUM_Frequency_Analysis = ( "wB97X-D/def2-SVPD/VACUUM Frequency Analysis" ) wB97X_D_def2_SVPD_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "wB97X-D/def2-SVPD/VACUUM Frequency Flattening Geometry Optimization" ) wB97X_D_def2_SVPD_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-D/def2-SVPD/VACUUM Frequency Flattening Transition State Geometry Optimization" wB97X_D_def2_SVPD_VACUUM_Geometry_Optimization = ( "wB97X-D/def2-SVPD/VACUUM Geometry Optimization" ) wB97X_D_def2_SVPD_VACUUM_Single_Point = "wB97X-D/def2-SVPD/VACUUM Single Point" wB97X_D_def2_SVPD_VACUUM_Transition_State_Geometry_Optimization = ( "wB97X-D/def2-SVPD/VACUUM Transition State Geometry Optimization" ) wB97X_D_def2_SVPD_VACUUM_Unknown = "wB97X-D/def2-SVPD/VACUUM Unknown" wB97X_D_def2_TZVPD_PCM_Force = "wB97X-D/def2-TZVPD/PCM Force" wB97X_D_def2_TZVPD_PCM_Frequency_Analysis = ( "wB97X-D/def2-TZVPD/PCM Frequency Analysis" ) wB97X_D_def2_TZVPD_PCM_Frequency_Flattening_Geometry_Optimization = ( "wB97X-D/def2-TZVPD/PCM Frequency Flattening Geometry Optimization" ) wB97X_D_def2_TZVPD_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-D/def2-TZVPD/PCM Frequency Flattening Transition State Geometry Optimization" wB97X_D_def2_TZVPD_PCM_Geometry_Optimization = ( "wB97X-D/def2-TZVPD/PCM Geometry Optimization" ) wB97X_D_def2_TZVPD_PCM_Single_Point = "wB97X-D/def2-TZVPD/PCM Single Point" wB97X_D_def2_TZVPD_PCM_Transition_State_Geometry_Optimization = ( "wB97X-D/def2-TZVPD/PCM Transition State Geometry Optimization" ) wB97X_D_def2_TZVPD_PCM_Unknown = "wB97X-D/def2-TZVPD/PCM Unknown" wB97X_D_def2_TZVPD_SMD_Force = "wB97X-D/def2-TZVPD/SMD Force" wB97X_D_def2_TZVPD_SMD_Frequency_Analysis = ( "wB97X-D/def2-TZVPD/SMD Frequency Analysis" ) wB97X_D_def2_TZVPD_SMD_Frequency_Flattening_Geometry_Optimization = ( "wB97X-D/def2-TZVPD/SMD Frequency Flattening Geometry Optimization" ) wB97X_D_def2_TZVPD_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-D/def2-TZVPD/SMD Frequency Flattening Transition State Geometry Optimization" wB97X_D_def2_TZVPD_SMD_Geometry_Optimization = ( "wB97X-D/def2-TZVPD/SMD Geometry Optimization" ) wB97X_D_def2_TZVPD_SMD_Single_Point = "wB97X-D/def2-TZVPD/SMD Single Point" wB97X_D_def2_TZVPD_SMD_Transition_State_Geometry_Optimization = ( "wB97X-D/def2-TZVPD/SMD Transition State Geometry Optimization" ) wB97X_D_def2_TZVPD_SMD_Unknown = "wB97X-D/def2-TZVPD/SMD Unknown" wB97X_D_def2_TZVPD_VACUUM_Force = "wB97X-D/def2-TZVPD/VACUUM Force" wB97X_D_def2_TZVPD_VACUUM_Frequency_Analysis = ( "wB97X-D/def2-TZVPD/VACUUM Frequency Analysis" ) wB97X_D_def2_TZVPD_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "wB97X-D/def2-TZVPD/VACUUM Frequency Flattening Geometry Optimization" ) wB97X_D_def2_TZVPD_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-D/def2-TZVPD/VACUUM Frequency Flattening Transition State Geometry Optimization" wB97X_D_def2_TZVPD_VACUUM_Geometry_Optimization = ( "wB97X-D/def2-TZVPD/VACUUM Geometry Optimization" ) wB97X_D_def2_TZVPD_VACUUM_Single_Point = "wB97X-D/def2-TZVPD/VACUUM Single Point" wB97X_D_def2_TZVPD_VACUUM_Transition_State_Geometry_Optimization = ( "wB97X-D/def2-TZVPD/VACUUM Transition State Geometry Optimization" ) wB97X_D_def2_TZVPD_VACUUM_Unknown = "wB97X-D/def2-TZVPD/VACUUM Unknown" wB97X_D_def2_TZVPPD_PCM_Force = "wB97X-D/def2-TZVPPD/PCM Force" wB97X_D_def2_TZVPPD_PCM_Frequency_Analysis = ( "wB97X-D/def2-TZVPPD/PCM Frequency Analysis" ) wB97X_D_def2_TZVPPD_PCM_Frequency_Flattening_Geometry_Optimization = ( "wB97X-D/def2-TZVPPD/PCM Frequency Flattening Geometry Optimization" ) wB97X_D_def2_TZVPPD_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-D/def2-TZVPPD/PCM Frequency Flattening Transition State Geometry Optimization" wB97X_D_def2_TZVPPD_PCM_Geometry_Optimization = ( "wB97X-D/def2-TZVPPD/PCM Geometry Optimization" ) wB97X_D_def2_TZVPPD_PCM_Single_Point = "wB97X-D/def2-TZVPPD/PCM Single Point" wB97X_D_def2_TZVPPD_PCM_Transition_State_Geometry_Optimization = ( "wB97X-D/def2-TZVPPD/PCM Transition State Geometry Optimization" ) wB97X_D_def2_TZVPPD_PCM_Unknown = "wB97X-D/def2-TZVPPD/PCM Unknown" wB97X_D_def2_TZVPPD_SMD_Force = "wB97X-D/def2-TZVPPD/SMD Force" wB97X_D_def2_TZVPPD_SMD_Frequency_Analysis = ( "wB97X-D/def2-TZVPPD/SMD Frequency Analysis" ) wB97X_D_def2_TZVPPD_SMD_Frequency_Flattening_Geometry_Optimization = ( "wB97X-D/def2-TZVPPD/SMD Frequency Flattening Geometry Optimization" ) wB97X_D_def2_TZVPPD_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-D/def2-TZVPPD/SMD Frequency Flattening Transition State Geometry Optimization" wB97X_D_def2_TZVPPD_SMD_Geometry_Optimization = ( "wB97X-D/def2-TZVPPD/SMD Geometry Optimization" ) wB97X_D_def2_TZVPPD_SMD_Single_Point = "wB97X-D/def2-TZVPPD/SMD Single Point" wB97X_D_def2_TZVPPD_SMD_Transition_State_Geometry_Optimization = ( "wB97X-D/def2-TZVPPD/SMD Transition State Geometry Optimization" ) wB97X_D_def2_TZVPPD_SMD_Unknown = "wB97X-D/def2-TZVPPD/SMD Unknown" wB97X_D_def2_TZVPPD_VACUUM_Force = "wB97X-D/def2-TZVPPD/VACUUM Force" wB97X_D_def2_TZVPPD_VACUUM_Frequency_Analysis = ( "wB97X-D/def2-TZVPPD/VACUUM Frequency Analysis" ) wB97X_D_def2_TZVPPD_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "wB97X-D/def2-TZVPPD/VACUUM Frequency Flattening Geometry Optimization" ) wB97X_D_def2_TZVPPD_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-D/def2-TZVPPD/VACUUM Frequency Flattening Transition State Geometry Optimization" wB97X_D_def2_TZVPPD_VACUUM_Geometry_Optimization = ( "wB97X-D/def2-TZVPPD/VACUUM Geometry Optimization" ) wB97X_D_def2_TZVPPD_VACUUM_Single_Point = "wB97X-D/def2-TZVPPD/VACUUM Single Point" wB97X_D_def2_TZVPPD_VACUUM_Transition_State_Geometry_Optimization = ( "wB97X-D/def2-TZVPPD/VACUUM Transition State Geometry Optimization" ) wB97X_D_def2_TZVPPD_VACUUM_Unknown = "wB97X-D/def2-TZVPPD/VACUUM Unknown" wB97X_D_def2_TZVPP_PCM_Force = "wB97X-D/def2-TZVPP/PCM Force" wB97X_D_def2_TZVPP_PCM_Frequency_Analysis = ( "wB97X-D/def2-TZVPP/PCM Frequency Analysis" ) wB97X_D_def2_TZVPP_PCM_Frequency_Flattening_Geometry_Optimization = ( "wB97X-D/def2-TZVPP/PCM Frequency Flattening Geometry Optimization" ) wB97X_D_def2_TZVPP_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-D/def2-TZVPP/PCM Frequency Flattening Transition State Geometry Optimization" wB97X_D_def2_TZVPP_PCM_Geometry_Optimization = ( "wB97X-D/def2-TZVPP/PCM Geometry Optimization" ) wB97X_D_def2_TZVPP_PCM_Single_Point = "wB97X-D/def2-TZVPP/PCM Single Point" wB97X_D_def2_TZVPP_PCM_Transition_State_Geometry_Optimization = ( "wB97X-D/def2-TZVPP/PCM Transition State Geometry Optimization" ) wB97X_D_def2_TZVPP_PCM_Unknown = "wB97X-D/def2-TZVPP/PCM Unknown" wB97X_D_def2_TZVPP_SMD_Force = "wB97X-D/def2-TZVPP/SMD Force" wB97X_D_def2_TZVPP_SMD_Frequency_Analysis = ( "wB97X-D/def2-TZVPP/SMD Frequency Analysis" ) wB97X_D_def2_TZVPP_SMD_Frequency_Flattening_Geometry_Optimization = ( "wB97X-D/def2-TZVPP/SMD Frequency Flattening Geometry Optimization" ) wB97X_D_def2_TZVPP_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-D/def2-TZVPP/SMD Frequency Flattening Transition State Geometry Optimization" wB97X_D_def2_TZVPP_SMD_Geometry_Optimization = ( "wB97X-D/def2-TZVPP/SMD Geometry Optimization" ) wB97X_D_def2_TZVPP_SMD_Single_Point = "wB97X-D/def2-TZVPP/SMD Single Point" wB97X_D_def2_TZVPP_SMD_Transition_State_Geometry_Optimization = ( "wB97X-D/def2-TZVPP/SMD Transition State Geometry Optimization" ) wB97X_D_def2_TZVPP_SMD_Unknown = "wB97X-D/def2-TZVPP/SMD Unknown" wB97X_D_def2_TZVPP_VACUUM_Force = "wB97X-D/def2-TZVPP/VACUUM Force" wB97X_D_def2_TZVPP_VACUUM_Frequency_Analysis = ( "wB97X-D/def2-TZVPP/VACUUM Frequency Analysis" ) wB97X_D_def2_TZVPP_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "wB97X-D/def2-TZVPP/VACUUM Frequency Flattening Geometry Optimization" ) wB97X_D_def2_TZVPP_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-D/def2-TZVPP/VACUUM Frequency Flattening Transition State Geometry Optimization" wB97X_D_def2_TZVPP_VACUUM_Geometry_Optimization = ( "wB97X-D/def2-TZVPP/VACUUM Geometry Optimization" ) wB97X_D_def2_TZVPP_VACUUM_Single_Point = "wB97X-D/def2-TZVPP/VACUUM Single Point" wB97X_D_def2_TZVPP_VACUUM_Transition_State_Geometry_Optimization = ( "wB97X-D/def2-TZVPP/VACUUM Transition State Geometry Optimization" ) wB97X_D_def2_TZVPP_VACUUM_Unknown = "wB97X-D/def2-TZVPP/VACUUM Unknown" wB97X_D_def2_TZVP_PCM_Force = "wB97X-D/def2-TZVP/PCM Force" wB97X_D_def2_TZVP_PCM_Frequency_Analysis = ( "wB97X-D/def2-TZVP/PCM Frequency Analysis" ) wB97X_D_def2_TZVP_PCM_Frequency_Flattening_Geometry_Optimization = ( "wB97X-D/def2-TZVP/PCM Frequency Flattening Geometry Optimization" ) wB97X_D_def2_TZVP_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-D/def2-TZVP/PCM Frequency Flattening Transition State Geometry Optimization" wB97X_D_def2_TZVP_PCM_Geometry_Optimization = ( "wB97X-D/def2-TZVP/PCM Geometry Optimization" ) wB97X_D_def2_TZVP_PCM_Single_Point = "wB97X-D/def2-TZVP/PCM Single Point" wB97X_D_def2_TZVP_PCM_Transition_State_Geometry_Optimization = ( "wB97X-D/def2-TZVP/PCM Transition State Geometry Optimization" ) wB97X_D_def2_TZVP_PCM_Unknown = "wB97X-D/def2-TZVP/PCM Unknown" wB97X_D_def2_TZVP_SMD_Force = "wB97X-D/def2-TZVP/SMD Force" wB97X_D_def2_TZVP_SMD_Frequency_Analysis = ( "wB97X-D/def2-TZVP/SMD Frequency Analysis" ) wB97X_D_def2_TZVP_SMD_Frequency_Flattening_Geometry_Optimization = ( "wB97X-D/def2-TZVP/SMD Frequency Flattening Geometry Optimization" ) wB97X_D_def2_TZVP_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-D/def2-TZVP/SMD Frequency Flattening Transition State Geometry Optimization" wB97X_D_def2_TZVP_SMD_Geometry_Optimization = ( "wB97X-D/def2-TZVP/SMD Geometry Optimization" ) wB97X_D_def2_TZVP_SMD_Single_Point = "wB97X-D/def2-TZVP/SMD Single Point" wB97X_D_def2_TZVP_SMD_Transition_State_Geometry_Optimization = ( "wB97X-D/def2-TZVP/SMD Transition State Geometry Optimization" ) wB97X_D_def2_TZVP_SMD_Unknown = "wB97X-D/def2-TZVP/SMD Unknown" wB97X_D_def2_TZVP_VACUUM_Force = "wB97X-D/def2-TZVP/VACUUM Force" wB97X_D_def2_TZVP_VACUUM_Frequency_Analysis = ( "wB97X-D/def2-TZVP/VACUUM Frequency Analysis" ) wB97X_D_def2_TZVP_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "wB97X-D/def2-TZVP/VACUUM Frequency Flattening Geometry Optimization" ) wB97X_D_def2_TZVP_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-D/def2-TZVP/VACUUM Frequency Flattening Transition State Geometry Optimization" wB97X_D_def2_TZVP_VACUUM_Geometry_Optimization = ( "wB97X-D/def2-TZVP/VACUUM Geometry Optimization" ) wB97X_D_def2_TZVP_VACUUM_Single_Point = "wB97X-D/def2-TZVP/VACUUM Single Point" wB97X_D_def2_TZVP_VACUUM_Transition_State_Geometry_Optimization = ( "wB97X-D/def2-TZVP/VACUUM Transition State Geometry Optimization" ) wB97X_D_def2_TZVP_VACUUM_Unknown = "wB97X-D/def2-TZVP/VACUUM Unknown" wB97X_V_6_31g_d_PCM_Force = "wB97X-V/6-31g*/PCM Force" wB97X_V_6_31g_d_PCM_Frequency_Analysis = "wB97X-V/6-31g*/PCM Frequency Analysis" wB97X_V_6_31g_d_PCM_Frequency_Flattening_Geometry_Optimization = ( "wB97X-V/6-31g*/PCM Frequency Flattening Geometry Optimization" ) wB97X_V_6_31g_d_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = ( "wB97X-V/6-31g*/PCM Frequency Flattening Transition State Geometry Optimization" ) wB97X_V_6_31g_d_PCM_Geometry_Optimization = ( "wB97X-V/6-31g*/PCM Geometry Optimization" ) wB97X_V_6_31g_d_PCM_Single_Point = "wB97X-V/6-31g*/PCM Single Point" wB97X_V_6_31g_d_PCM_Transition_State_Geometry_Optimization = ( "wB97X-V/6-31g*/PCM Transition State Geometry Optimization" ) wB97X_V_6_31g_d_PCM_Unknown = "wB97X-V/6-31g*/PCM Unknown" wB97X_V_6_31g_d_SMD_Force = "wB97X-V/6-31g*/SMD Force" wB97X_V_6_31g_d_SMD_Frequency_Analysis = "wB97X-V/6-31g*/SMD Frequency Analysis" wB97X_V_6_31g_d_SMD_Frequency_Flattening_Geometry_Optimization = ( "wB97X-V/6-31g*/SMD Frequency Flattening Geometry Optimization" ) wB97X_V_6_31g_d_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = ( "wB97X-V/6-31g*/SMD Frequency Flattening Transition State Geometry Optimization" ) wB97X_V_6_31g_d_SMD_Geometry_Optimization = ( "wB97X-V/6-31g*/SMD Geometry Optimization" ) wB97X_V_6_31g_d_SMD_Single_Point = "wB97X-V/6-31g*/SMD Single Point" wB97X_V_6_31g_d_SMD_Transition_State_Geometry_Optimization = ( "wB97X-V/6-31g*/SMD Transition State Geometry Optimization" ) wB97X_V_6_31g_d_SMD_Unknown = "wB97X-V/6-31g*/SMD Unknown" wB97X_V_6_31g_d_VACUUM_Force = "wB97X-V/6-31g*/VACUUM Force" wB97X_V_6_31g_d_VACUUM_Frequency_Analysis = ( "wB97X-V/6-31g*/VACUUM Frequency Analysis" ) wB97X_V_6_31g_d_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "wB97X-V/6-31g*/VACUUM Frequency Flattening Geometry Optimization" ) wB97X_V_6_31g_d_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-V/6-31g*/VACUUM Frequency Flattening Transition State Geometry Optimization" wB97X_V_6_31g_d_VACUUM_Geometry_Optimization = ( "wB97X-V/6-31g*/VACUUM Geometry Optimization" ) wB97X_V_6_31g_d_VACUUM_Single_Point = "wB97X-V/6-31g*/VACUUM Single Point" wB97X_V_6_31g_d_VACUUM_Transition_State_Geometry_Optimization = ( "wB97X-V/6-31g*/VACUUM Transition State Geometry Optimization" ) wB97X_V_6_31g_d_VACUUM_Unknown = "wB97X-V/6-31g*/VACUUM Unknown" wB97X_V_def2_QZVPD_PCM_Force = "wB97X-V/def2-QZVPD/PCM Force" wB97X_V_def2_QZVPD_PCM_Frequency_Analysis = ( "wB97X-V/def2-QZVPD/PCM Frequency Analysis" ) wB97X_V_def2_QZVPD_PCM_Frequency_Flattening_Geometry_Optimization = ( "wB97X-V/def2-QZVPD/PCM Frequency Flattening Geometry Optimization" ) wB97X_V_def2_QZVPD_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-V/def2-QZVPD/PCM Frequency Flattening Transition State Geometry Optimization" wB97X_V_def2_QZVPD_PCM_Geometry_Optimization = ( "wB97X-V/def2-QZVPD/PCM Geometry Optimization" ) wB97X_V_def2_QZVPD_PCM_Single_Point = "wB97X-V/def2-QZVPD/PCM Single Point" wB97X_V_def2_QZVPD_PCM_Transition_State_Geometry_Optimization = ( "wB97X-V/def2-QZVPD/PCM Transition State Geometry Optimization" ) wB97X_V_def2_QZVPD_PCM_Unknown = "wB97X-V/def2-QZVPD/PCM Unknown" wB97X_V_def2_QZVPD_SMD_Force = "wB97X-V/def2-QZVPD/SMD Force" wB97X_V_def2_QZVPD_SMD_Frequency_Analysis = ( "wB97X-V/def2-QZVPD/SMD Frequency Analysis" ) wB97X_V_def2_QZVPD_SMD_Frequency_Flattening_Geometry_Optimization = ( "wB97X-V/def2-QZVPD/SMD Frequency Flattening Geometry Optimization" ) wB97X_V_def2_QZVPD_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-V/def2-QZVPD/SMD Frequency Flattening Transition State Geometry Optimization" wB97X_V_def2_QZVPD_SMD_Geometry_Optimization = ( "wB97X-V/def2-QZVPD/SMD Geometry Optimization" ) wB97X_V_def2_QZVPD_SMD_Single_Point = "wB97X-V/def2-QZVPD/SMD Single Point" wB97X_V_def2_QZVPD_SMD_Transition_State_Geometry_Optimization = ( "wB97X-V/def2-QZVPD/SMD Transition State Geometry Optimization" ) wB97X_V_def2_QZVPD_SMD_Unknown = "wB97X-V/def2-QZVPD/SMD Unknown" wB97X_V_def2_QZVPD_VACUUM_Force = "wB97X-V/def2-QZVPD/VACUUM Force" wB97X_V_def2_QZVPD_VACUUM_Frequency_Analysis = ( "wB97X-V/def2-QZVPD/VACUUM Frequency Analysis" ) wB97X_V_def2_QZVPD_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "wB97X-V/def2-QZVPD/VACUUM Frequency Flattening Geometry Optimization" ) wB97X_V_def2_QZVPD_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-V/def2-QZVPD/VACUUM Frequency Flattening Transition State Geometry Optimization" wB97X_V_def2_QZVPD_VACUUM_Geometry_Optimization = ( "wB97X-V/def2-QZVPD/VACUUM Geometry Optimization" ) wB97X_V_def2_QZVPD_VACUUM_Single_Point = "wB97X-V/def2-QZVPD/VACUUM Single Point" wB97X_V_def2_QZVPD_VACUUM_Transition_State_Geometry_Optimization = ( "wB97X-V/def2-QZVPD/VACUUM Transition State Geometry Optimization" ) wB97X_V_def2_QZVPD_VACUUM_Unknown = "wB97X-V/def2-QZVPD/VACUUM Unknown" wB97X_V_def2_QZVPPD_PCM_Force = "wB97X-V/def2-QZVPPD/PCM Force" wB97X_V_def2_QZVPPD_PCM_Frequency_Analysis = ( "wB97X-V/def2-QZVPPD/PCM Frequency Analysis" ) wB97X_V_def2_QZVPPD_PCM_Frequency_Flattening_Geometry_Optimization = ( "wB97X-V/def2-QZVPPD/PCM Frequency Flattening Geometry Optimization" ) wB97X_V_def2_QZVPPD_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-V/def2-QZVPPD/PCM Frequency Flattening Transition State Geometry Optimization" wB97X_V_def2_QZVPPD_PCM_Geometry_Optimization = ( "wB97X-V/def2-QZVPPD/PCM Geometry Optimization" ) wB97X_V_def2_QZVPPD_PCM_Single_Point = "wB97X-V/def2-QZVPPD/PCM Single Point" wB97X_V_def2_QZVPPD_PCM_Transition_State_Geometry_Optimization = ( "wB97X-V/def2-QZVPPD/PCM Transition State Geometry Optimization" ) wB97X_V_def2_QZVPPD_PCM_Unknown = "wB97X-V/def2-QZVPPD/PCM Unknown" wB97X_V_def2_QZVPPD_SMD_Force = "wB97X-V/def2-QZVPPD/SMD Force" wB97X_V_def2_QZVPPD_SMD_Frequency_Analysis = ( "wB97X-V/def2-QZVPPD/SMD Frequency Analysis" ) wB97X_V_def2_QZVPPD_SMD_Frequency_Flattening_Geometry_Optimization = ( "wB97X-V/def2-QZVPPD/SMD Frequency Flattening Geometry Optimization" ) wB97X_V_def2_QZVPPD_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-V/def2-QZVPPD/SMD Frequency Flattening Transition State Geometry Optimization" wB97X_V_def2_QZVPPD_SMD_Geometry_Optimization = ( "wB97X-V/def2-QZVPPD/SMD Geometry Optimization" ) wB97X_V_def2_QZVPPD_SMD_Single_Point = "wB97X-V/def2-QZVPPD/SMD Single Point" wB97X_V_def2_QZVPPD_SMD_Transition_State_Geometry_Optimization = ( "wB97X-V/def2-QZVPPD/SMD Transition State Geometry Optimization" ) wB97X_V_def2_QZVPPD_SMD_Unknown = "wB97X-V/def2-QZVPPD/SMD Unknown" wB97X_V_def2_QZVPPD_VACUUM_Force = "wB97X-V/def2-QZVPPD/VACUUM Force" wB97X_V_def2_QZVPPD_VACUUM_Frequency_Analysis = ( "wB97X-V/def2-QZVPPD/VACUUM Frequency Analysis" ) wB97X_V_def2_QZVPPD_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "wB97X-V/def2-QZVPPD/VACUUM Frequency Flattening Geometry Optimization" ) wB97X_V_def2_QZVPPD_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-V/def2-QZVPPD/VACUUM Frequency Flattening Transition State Geometry Optimization" wB97X_V_def2_QZVPPD_VACUUM_Geometry_Optimization = ( "wB97X-V/def2-QZVPPD/VACUUM Geometry Optimization" ) wB97X_V_def2_QZVPPD_VACUUM_Single_Point = "wB97X-V/def2-QZVPPD/VACUUM Single Point" wB97X_V_def2_QZVPPD_VACUUM_Transition_State_Geometry_Optimization = ( "wB97X-V/def2-QZVPPD/VACUUM Transition State Geometry Optimization" ) wB97X_V_def2_QZVPPD_VACUUM_Unknown = "wB97X-V/def2-QZVPPD/VACUUM Unknown" wB97X_V_def2_SVPD_PCM_Force = "wB97X-V/def2-SVPD/PCM Force" wB97X_V_def2_SVPD_PCM_Frequency_Analysis = ( "wB97X-V/def2-SVPD/PCM Frequency Analysis" ) wB97X_V_def2_SVPD_PCM_Frequency_Flattening_Geometry_Optimization = ( "wB97X-V/def2-SVPD/PCM Frequency Flattening Geometry Optimization" ) wB97X_V_def2_SVPD_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-V/def2-SVPD/PCM Frequency Flattening Transition State Geometry Optimization" wB97X_V_def2_SVPD_PCM_Geometry_Optimization = ( "wB97X-V/def2-SVPD/PCM Geometry Optimization" ) wB97X_V_def2_SVPD_PCM_Single_Point = "wB97X-V/def2-SVPD/PCM Single Point" wB97X_V_def2_SVPD_PCM_Transition_State_Geometry_Optimization = ( "wB97X-V/def2-SVPD/PCM Transition State Geometry Optimization" ) wB97X_V_def2_SVPD_PCM_Unknown = "wB97X-V/def2-SVPD/PCM Unknown" wB97X_V_def2_SVPD_SMD_Force = "wB97X-V/def2-SVPD/SMD Force" wB97X_V_def2_SVPD_SMD_Frequency_Analysis = ( "wB97X-V/def2-SVPD/SMD Frequency Analysis" ) wB97X_V_def2_SVPD_SMD_Frequency_Flattening_Geometry_Optimization = ( "wB97X-V/def2-SVPD/SMD Frequency Flattening Geometry Optimization" ) wB97X_V_def2_SVPD_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-V/def2-SVPD/SMD Frequency Flattening Transition State Geometry Optimization" wB97X_V_def2_SVPD_SMD_Geometry_Optimization = ( "wB97X-V/def2-SVPD/SMD Geometry Optimization" ) wB97X_V_def2_SVPD_SMD_Single_Point = "wB97X-V/def2-SVPD/SMD Single Point" wB97X_V_def2_SVPD_SMD_Transition_State_Geometry_Optimization = ( "wB97X-V/def2-SVPD/SMD Transition State Geometry Optimization" ) wB97X_V_def2_SVPD_SMD_Unknown = "wB97X-V/def2-SVPD/SMD Unknown" wB97X_V_def2_SVPD_VACUUM_Force = "wB97X-V/def2-SVPD/VACUUM Force" wB97X_V_def2_SVPD_VACUUM_Frequency_Analysis = ( "wB97X-V/def2-SVPD/VACUUM Frequency Analysis" ) wB97X_V_def2_SVPD_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "wB97X-V/def2-SVPD/VACUUM Frequency Flattening Geometry Optimization" ) wB97X_V_def2_SVPD_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-V/def2-SVPD/VACUUM Frequency Flattening Transition State Geometry Optimization" wB97X_V_def2_SVPD_VACUUM_Geometry_Optimization = ( "wB97X-V/def2-SVPD/VACUUM Geometry Optimization" ) wB97X_V_def2_SVPD_VACUUM_Single_Point = "wB97X-V/def2-SVPD/VACUUM Single Point" wB97X_V_def2_SVPD_VACUUM_Transition_State_Geometry_Optimization = ( "wB97X-V/def2-SVPD/VACUUM Transition State Geometry Optimization" ) wB97X_V_def2_SVPD_VACUUM_Unknown = "wB97X-V/def2-SVPD/VACUUM Unknown" wB97X_V_def2_TZVPD_PCM_Force = "wB97X-V/def2-TZVPD/PCM Force" wB97X_V_def2_TZVPD_PCM_Frequency_Analysis = ( "wB97X-V/def2-TZVPD/PCM Frequency Analysis" ) wB97X_V_def2_TZVPD_PCM_Frequency_Flattening_Geometry_Optimization = ( "wB97X-V/def2-TZVPD/PCM Frequency Flattening Geometry Optimization" ) wB97X_V_def2_TZVPD_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-V/def2-TZVPD/PCM Frequency Flattening Transition State Geometry Optimization" wB97X_V_def2_TZVPD_PCM_Geometry_Optimization = ( "wB97X-V/def2-TZVPD/PCM Geometry Optimization" ) wB97X_V_def2_TZVPD_PCM_Single_Point = "wB97X-V/def2-TZVPD/PCM Single Point" wB97X_V_def2_TZVPD_PCM_Transition_State_Geometry_Optimization = ( "wB97X-V/def2-TZVPD/PCM Transition State Geometry Optimization" ) wB97X_V_def2_TZVPD_PCM_Unknown = "wB97X-V/def2-TZVPD/PCM Unknown" wB97X_V_def2_TZVPD_SMD_Force = "wB97X-V/def2-TZVPD/SMD Force" wB97X_V_def2_TZVPD_SMD_Frequency_Analysis = ( "wB97X-V/def2-TZVPD/SMD Frequency Analysis" ) wB97X_V_def2_TZVPD_SMD_Frequency_Flattening_Geometry_Optimization = ( "wB97X-V/def2-TZVPD/SMD Frequency Flattening Geometry Optimization" ) wB97X_V_def2_TZVPD_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-V/def2-TZVPD/SMD Frequency Flattening Transition State Geometry Optimization" wB97X_V_def2_TZVPD_SMD_Geometry_Optimization = ( "wB97X-V/def2-TZVPD/SMD Geometry Optimization" ) wB97X_V_def2_TZVPD_SMD_Single_Point = "wB97X-V/def2-TZVPD/SMD Single Point" wB97X_V_def2_TZVPD_SMD_Transition_State_Geometry_Optimization = ( "wB97X-V/def2-TZVPD/SMD Transition State Geometry Optimization" ) wB97X_V_def2_TZVPD_SMD_Unknown = "wB97X-V/def2-TZVPD/SMD Unknown" wB97X_V_def2_TZVPD_VACUUM_Force = "wB97X-V/def2-TZVPD/VACUUM Force" wB97X_V_def2_TZVPD_VACUUM_Frequency_Analysis = ( "wB97X-V/def2-TZVPD/VACUUM Frequency Analysis" ) wB97X_V_def2_TZVPD_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "wB97X-V/def2-TZVPD/VACUUM Frequency Flattening Geometry Optimization" ) wB97X_V_def2_TZVPD_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-V/def2-TZVPD/VACUUM Frequency Flattening Transition State Geometry Optimization" wB97X_V_def2_TZVPD_VACUUM_Geometry_Optimization = ( "wB97X-V/def2-TZVPD/VACUUM Geometry Optimization" ) wB97X_V_def2_TZVPD_VACUUM_Single_Point = "wB97X-V/def2-TZVPD/VACUUM Single Point" wB97X_V_def2_TZVPD_VACUUM_Transition_State_Geometry_Optimization = ( "wB97X-V/def2-TZVPD/VACUUM Transition State Geometry Optimization" ) wB97X_V_def2_TZVPD_VACUUM_Unknown = "wB97X-V/def2-TZVPD/VACUUM Unknown" wB97X_V_def2_TZVPPD_PCM_Force = "wB97X-V/def2-TZVPPD/PCM Force" wB97X_V_def2_TZVPPD_PCM_Frequency_Analysis = ( "wB97X-V/def2-TZVPPD/PCM Frequency Analysis" ) wB97X_V_def2_TZVPPD_PCM_Frequency_Flattening_Geometry_Optimization = ( "wB97X-V/def2-TZVPPD/PCM Frequency Flattening Geometry Optimization" ) wB97X_V_def2_TZVPPD_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-V/def2-TZVPPD/PCM Frequency Flattening Transition State Geometry Optimization" wB97X_V_def2_TZVPPD_PCM_Geometry_Optimization = ( "wB97X-V/def2-TZVPPD/PCM Geometry Optimization" ) wB97X_V_def2_TZVPPD_PCM_Single_Point = "wB97X-V/def2-TZVPPD/PCM Single Point" wB97X_V_def2_TZVPPD_PCM_Transition_State_Geometry_Optimization = ( "wB97X-V/def2-TZVPPD/PCM Transition State Geometry Optimization" ) wB97X_V_def2_TZVPPD_PCM_Unknown = "wB97X-V/def2-TZVPPD/PCM Unknown" wB97X_V_def2_TZVPPD_SMD_Force = "wB97X-V/def2-TZVPPD/SMD Force" wB97X_V_def2_TZVPPD_SMD_Frequency_Analysis = ( "wB97X-V/def2-TZVPPD/SMD Frequency Analysis" ) wB97X_V_def2_TZVPPD_SMD_Frequency_Flattening_Geometry_Optimization = ( "wB97X-V/def2-TZVPPD/SMD Frequency Flattening Geometry Optimization" ) wB97X_V_def2_TZVPPD_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-V/def2-TZVPPD/SMD Frequency Flattening Transition State Geometry Optimization" wB97X_V_def2_TZVPPD_SMD_Geometry_Optimization = ( "wB97X-V/def2-TZVPPD/SMD Geometry Optimization" ) wB97X_V_def2_TZVPPD_SMD_Single_Point = "wB97X-V/def2-TZVPPD/SMD Single Point" wB97X_V_def2_TZVPPD_SMD_Transition_State_Geometry_Optimization = ( "wB97X-V/def2-TZVPPD/SMD Transition State Geometry Optimization" ) wB97X_V_def2_TZVPPD_SMD_Unknown = "wB97X-V/def2-TZVPPD/SMD Unknown" wB97X_V_def2_TZVPPD_VACUUM_Force = "wB97X-V/def2-TZVPPD/VACUUM Force" wB97X_V_def2_TZVPPD_VACUUM_Frequency_Analysis = ( "wB97X-V/def2-TZVPPD/VACUUM Frequency Analysis" ) wB97X_V_def2_TZVPPD_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "wB97X-V/def2-TZVPPD/VACUUM Frequency Flattening Geometry Optimization" ) wB97X_V_def2_TZVPPD_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-V/def2-TZVPPD/VACUUM Frequency Flattening Transition State Geometry Optimization" wB97X_V_def2_TZVPPD_VACUUM_Geometry_Optimization = ( "wB97X-V/def2-TZVPPD/VACUUM Geometry Optimization" ) wB97X_V_def2_TZVPPD_VACUUM_Single_Point = "wB97X-V/def2-TZVPPD/VACUUM Single Point" wB97X_V_def2_TZVPPD_VACUUM_Transition_State_Geometry_Optimization = ( "wB97X-V/def2-TZVPPD/VACUUM Transition State Geometry Optimization" ) wB97X_V_def2_TZVPPD_VACUUM_Unknown = "wB97X-V/def2-TZVPPD/VACUUM Unknown" wB97X_V_def2_TZVPP_PCM_Force = "wB97X-V/def2-TZVPP/PCM Force" wB97X_V_def2_TZVPP_PCM_Frequency_Analysis = ( "wB97X-V/def2-TZVPP/PCM Frequency Analysis" ) wB97X_V_def2_TZVPP_PCM_Frequency_Flattening_Geometry_Optimization = ( "wB97X-V/def2-TZVPP/PCM Frequency Flattening Geometry Optimization" ) wB97X_V_def2_TZVPP_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-V/def2-TZVPP/PCM Frequency Flattening Transition State Geometry Optimization" wB97X_V_def2_TZVPP_PCM_Geometry_Optimization = ( "wB97X-V/def2-TZVPP/PCM Geometry Optimization" ) wB97X_V_def2_TZVPP_PCM_Single_Point = "wB97X-V/def2-TZVPP/PCM Single Point" wB97X_V_def2_TZVPP_PCM_Transition_State_Geometry_Optimization = ( "wB97X-V/def2-TZVPP/PCM Transition State Geometry Optimization" ) wB97X_V_def2_TZVPP_PCM_Unknown = "wB97X-V/def2-TZVPP/PCM Unknown" wB97X_V_def2_TZVPP_SMD_Force = "wB97X-V/def2-TZVPP/SMD Force" wB97X_V_def2_TZVPP_SMD_Frequency_Analysis = ( "wB97X-V/def2-TZVPP/SMD Frequency Analysis" ) wB97X_V_def2_TZVPP_SMD_Frequency_Flattening_Geometry_Optimization = ( "wB97X-V/def2-TZVPP/SMD Frequency Flattening Geometry Optimization" ) wB97X_V_def2_TZVPP_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-V/def2-TZVPP/SMD Frequency Flattening Transition State Geometry Optimization" wB97X_V_def2_TZVPP_SMD_Geometry_Optimization = ( "wB97X-V/def2-TZVPP/SMD Geometry Optimization" ) wB97X_V_def2_TZVPP_SMD_Single_Point = "wB97X-V/def2-TZVPP/SMD Single Point" wB97X_V_def2_TZVPP_SMD_Transition_State_Geometry_Optimization = ( "wB97X-V/def2-TZVPP/SMD Transition State Geometry Optimization" ) wB97X_V_def2_TZVPP_SMD_Unknown = "wB97X-V/def2-TZVPP/SMD Unknown" wB97X_V_def2_TZVPP_VACUUM_Force = "wB97X-V/def2-TZVPP/VACUUM Force" wB97X_V_def2_TZVPP_VACUUM_Frequency_Analysis = ( "wB97X-V/def2-TZVPP/VACUUM Frequency Analysis" ) wB97X_V_def2_TZVPP_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "wB97X-V/def2-TZVPP/VACUUM Frequency Flattening Geometry Optimization" ) wB97X_V_def2_TZVPP_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-V/def2-TZVPP/VACUUM Frequency Flattening Transition State Geometry Optimization" wB97X_V_def2_TZVPP_VACUUM_Geometry_Optimization = ( "wB97X-V/def2-TZVPP/VACUUM Geometry Optimization" ) wB97X_V_def2_TZVPP_VACUUM_Single_Point = "wB97X-V/def2-TZVPP/VACUUM Single Point" wB97X_V_def2_TZVPP_VACUUM_Transition_State_Geometry_Optimization = ( "wB97X-V/def2-TZVPP/VACUUM Transition State Geometry Optimization" ) wB97X_V_def2_TZVPP_VACUUM_Unknown = "wB97X-V/def2-TZVPP/VACUUM Unknown" wB97X_V_def2_TZVP_PCM_Force = "wB97X-V/def2-TZVP/PCM Force" wB97X_V_def2_TZVP_PCM_Frequency_Analysis = ( "wB97X-V/def2-TZVP/PCM Frequency Analysis" ) wB97X_V_def2_TZVP_PCM_Frequency_Flattening_Geometry_Optimization = ( "wB97X-V/def2-TZVP/PCM Frequency Flattening Geometry Optimization" ) wB97X_V_def2_TZVP_PCM_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-V/def2-TZVP/PCM Frequency Flattening Transition State Geometry Optimization" wB97X_V_def2_TZVP_PCM_Geometry_Optimization = ( "wB97X-V/def2-TZVP/PCM Geometry Optimization" ) wB97X_V_def2_TZVP_PCM_Single_Point = "wB97X-V/def2-TZVP/PCM Single Point" wB97X_V_def2_TZVP_PCM_Transition_State_Geometry_Optimization = ( "wB97X-V/def2-TZVP/PCM Transition State Geometry Optimization" ) wB97X_V_def2_TZVP_PCM_Unknown = "wB97X-V/def2-TZVP/PCM Unknown" wB97X_V_def2_TZVP_SMD_Force = "wB97X-V/def2-TZVP/SMD Force" wB97X_V_def2_TZVP_SMD_Frequency_Analysis = ( "wB97X-V/def2-TZVP/SMD Frequency Analysis" ) wB97X_V_def2_TZVP_SMD_Frequency_Flattening_Geometry_Optimization = ( "wB97X-V/def2-TZVP/SMD Frequency Flattening Geometry Optimization" ) wB97X_V_def2_TZVP_SMD_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-V/def2-TZVP/SMD Frequency Flattening Transition State Geometry Optimization" wB97X_V_def2_TZVP_SMD_Geometry_Optimization = ( "wB97X-V/def2-TZVP/SMD Geometry Optimization" ) wB97X_V_def2_TZVP_SMD_Single_Point = "wB97X-V/def2-TZVP/SMD Single Point" wB97X_V_def2_TZVP_SMD_Transition_State_Geometry_Optimization = ( "wB97X-V/def2-TZVP/SMD Transition State Geometry Optimization" ) wB97X_V_def2_TZVP_SMD_Unknown = "wB97X-V/def2-TZVP/SMD Unknown" wB97X_V_def2_TZVP_VACUUM_Force = "wB97X-V/def2-TZVP/VACUUM Force" wB97X_V_def2_TZVP_VACUUM_Frequency_Analysis = ( "wB97X-V/def2-TZVP/VACUUM Frequency Analysis" ) wB97X_V_def2_TZVP_VACUUM_Frequency_Flattening_Geometry_Optimization = ( "wB97X-V/def2-TZVP/VACUUM Frequency Flattening Geometry Optimization" ) wB97X_V_def2_TZVP_VACUUM_Frequency_Flattening_Transition_State_Geometry_Optimization = "wB97X-V/def2-TZVP/VACUUM Frequency Flattening Transition State Geometry Optimization" wB97X_V_def2_TZVP_VACUUM_Geometry_Optimization = ( "wB97X-V/def2-TZVP/VACUUM Geometry Optimization" ) wB97X_V_def2_TZVP_VACUUM_Single_Point = "wB97X-V/def2-TZVP/VACUUM Single Point" wB97X_V_def2_TZVP_VACUUM_Transition_State_Geometry_Optimization = ( "wB97X-V/def2-TZVP/VACUUM Transition State Geometry Optimization" ) wB97X_V_def2_TZVP_VACUUM_Unknown = "wB97X-V/def2-TZVP/VACUUM Unknown" ././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1787011023.825055 emmet_core-0.87.2/emmet/core/qchem/calc_types/utils.py0000644000175100017510000001625715240717720022411 0ustar00runnerrunner"""Utilities to determine level of theory, task type, and calculation type for Q-Chem calculations""" from __future__ import annotations from typing import TYPE_CHECKING from emmet.core.qchem.calc_types import CalcType, LevelOfTheory, TaskType from emmet.core.qchem.calc_types.calc_types import BASIS_SETS, FUNCTIONALS if TYPE_CHECKING: from typing import Any __author__ = "Evan Spotte-Smith " # TODO : better string translation functional_synonyms = { "b97mv": "b97m-v", "b97mrv": "b97m-rv", "wb97xd": "wb97x-d", "wb97xd3": "wb97x-d3", "wb97xv": "wb97x-v", "wb97mv": "wb97m-v", } smd_synonyms = { "DIELECTRIC=7,230;N=1,410;ALPHA=0,000;BETA=0,859;GAMMA=36,830;PHI=0,000;PSI=0,000": "diglyme", "DIELECTRIC=18,500;N=1,415;ALPHA=0,000;BETA=0,735;GAMMA=20,200;PHI=0,000;PSI=0,000": "3:7 EC:EMC", } def level_of_theory(parameters: dict[str, Any]) -> LevelOfTheory: """ Returns the level of theory for a calculation, based on the input parameters given to Q-Chem Args: parameters: dict of Q-Chem input parameters """ funct_raw = parameters.get("rem", dict()).get("method") basis_raw = parameters.get("rem", dict()).get("basis") if funct_raw is None or basis_raw is None: raise ValueError( 'Method and basis must be included in "rem" section ' "of parameters!" ) disp_corr = parameters.get("rem", dict()).get("dft_d") if disp_corr is None: funct_lower = funct_raw.lower() funct_lower = functional_synonyms.get(funct_lower, funct_lower) else: # Replace Q-Chem terms for D3 tails with more common expressions disp_corr = disp_corr.replace("_bj", "(bj)").replace("_zero", "(0)") funct_lower = f"{funct_raw}-{disp_corr}" basis_lower = basis_raw.lower() functional = [f for f in FUNCTIONALS if f.lower() == funct_lower] if not functional: raise ValueError(f"Unexpected functional {funct_lower}!") functional = functional[0] basis = [b for b in BASIS_SETS if b.lower() == basis_lower] if not basis: raise ValueError(f"Unexpected basis set {basis_lower}!") basis = basis[0] solvent_method = parameters["rem"].get("solvent_method", "").lower() if solvent_method == "": solvation = "VACUUM" elif solvent_method in ["pcm", "cosmo"]: solvation = "PCM" # TODO: Add this once added into pymatgen and atomate # elif solvent_method == "isosvp": # if parameters.get("svp", {}).get("idefesr", 0): # solvation = "CMIRS" # else: # solvation = "ISOSVP" elif solvent_method == "smd": solvation = "SMD" else: raise ValueError(f"Unexpected implicit solvent method {solvent_method}!") lot = f"{functional}/{basis}/{solvation}" return LevelOfTheory(lot) def solvent(parameters: dict[str, Any], custom_smd: str | None = None) -> str: """ Returns the solvent used for this calculation. Args: parameters: dict of Q-Chem input parameters custom_smd: (Optional) string representing SMD parameters for a non-standard solvent """ lot = level_of_theory(parameters) solvation = lot.value.split("/")[-1] if solvation == "PCM": dielectric = float(parameters.get("solvent", {}).get("dielectric", 78.39)) dielectric_string = f"{dielectric:.2f}".replace(".", ",") return f"DIELECTRIC={dielectric_string}" # TODO: Add this once added into pymatgen and atomate # elif solvation == "ISOSVP": # dielectric = float(parameters.get("svp", {}).get("dielst", 78.39)) # rho = float(parameters.get("svp", {}).get("rhoiso", 0.001)) # return f"DIELECTRIC={round(dielectric, 2)},RHO={round(rho, 4)}" # elif solvation == "CMIRS": # dielectric = float(parameters.get("svp", {}).get("dielst", 78.39)) # rho = float(parameters.get("svp", {}).get("rhoiso", 0.001)) # a = parameters.get("pcm_nonels", {}).get("a") # b = parameters.get("pcm_nonels", {}).get("b") # c = parameters.get("pcm_nonels", {}).get("c") # d = parameters.get("pcm_nonels", {}).get("d") # solvrho = parameters.get("pcm_nonels", {}).get("solvrho") # gamma = parameters.get("pcm_nonels", {}).get("gamma") # # string = f"DIELECTRIC={round(dielectric, 2)},RHO={round(rho, 4)}" # for name, (piece, digits) in {"A": (a, 6), "B": (b, 6), "C": (c, 1), "D": (d, 3), # "SOLVRHO": (solvrho, 2), "GAMMA": (gamma, 1)}.items(): # if piece is None: # piecestring = "NONE" # else: # piecestring = f"{name}={round(float(piece), digits)}" # string += "," + piecestring # return string elif solvation == "SMD": solvent = parameters.get("smx", {}).get("solvent", "water") if solvent == "other": if custom_smd is None: raise ValueError( "SMD calculation with solvent=other requires custom_smd!" ) names = ["DIELECTRIC", "N", "ALPHA", "BETA", "GAMMA", "PHI", "PSI"] numbers = [float(x) for x in custom_smd.split(",")] string = "" for name, number in zip(names, numbers): string += f"{name}={number:.3f};" return string.rstrip(",").rstrip(";").replace(".", ",") else: return f"SOLVENT={solvent.upper()}" else: return "NONE" def lot_solvent_string( parameters: dict[str, Any], custom_smd: str | None = None ) -> str: """ Returns a string representation of the level of theory and solvent used for this calculation. Args: parameters: dict of Q-Chem input parameters custom_smd: (Optional) string representing SMD parameters for a non-standard solvent """ lot = level_of_theory(parameters).value solv = solvent(parameters, custom_smd=custom_smd) return f"{lot}({solv})" def task_type(orig: dict[str, Any], special_run_type: str | None = None) -> TaskType: if special_run_type == "frequency_flattener": return TaskType("Frequency Flattening Geometry Optimization") elif special_run_type == "ts_frequency_flattener": return TaskType("Frequency Flattening Transition State Geometry Optimization") if orig["rem"].get("job_type") == "sp": return TaskType("Single Point") elif orig["rem"].get("job_type") == "force": return TaskType("Force") elif orig["rem"].get("job_type") == "opt": return TaskType("Geometry Optimization") elif orig["rem"].get("job_type") == "ts": return TaskType("Transition State Geometry Optimization") elif orig["rem"].get("job_type") == "freq": return TaskType("Frequency Analysis") return TaskType("Unknown") def calc_type(special_run_type: str, orig: dict[str, Any]) -> CalcType: """ Determines the calc type Args: inputs: inputs dict with an incar, kpoints, potcar, and poscar dictionaries parameters: Dictionary of VASP parameters from Vasprun.xml """ rt = level_of_theory(orig).value tt = task_type(orig, special_run_type=special_run_type).value return CalcType(f"{rt} {tt}") ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8252716 emmet_core-0.87.2/emmet/core/qchem/calculation.py0000644000175100017510000006203415240717720021413 0ustar00runnerrunner"""Core definitions of a QChem calculation document.""" from __future__ import annotations import logging import re import warnings from collections import OrderedDict from datetime import datetime from pathlib import Path from typing import Any import numpy as np from pydantic import BaseModel, ConfigDict, Field, field_validator from emmet.core.io.pymatgen import Molecule, QCInput, QCOutput from emmet.core.qchem.calc_types import CalcType, LevelOfTheory, TaskType from emmet.core.qchem.calc_types.calc_types import BASIS_SETS, FUNCTIONALS from emmet.core.qchem.task import QChemStatus from emmet.core.utils import arrow_incompatible # from emmet.core.qchem.calc_types.em_utils import ( # level_of_theory, # task_type, # calc_type, # ) functional_synonyms = { "b97mv": "b97m-v", "b97mrv": "b97m-rv", "wb97xd": "wb97x-d", "wb97xd3": "wb97x-d3", "wb97xv": "wb97x-v", "wb97mv": "wb97m-v", } smd_synonyms = { "DIELECTRIC=7,230;N=1,410;ALPHA=0,000;BETA=0,859;GAMMA=36,830;PHI=0,000;PSI=0,000": "diglyme", "DIELECTRIC=18,500;N=1,415;ALPHA=0,000;BETA=0,735;GAMMA=20,200;PHI=0,000;PSI=0,000": "3:7 EC:EMC", } __author__ = "Rishabh D. Guha " logger = logging.getLogger(__name__) # class QChemObject(ValueEnum): # Not sure but can we have something like GRAD and HESS # as QChem data objects @arrow_incompatible class CalculationInput(BaseModel): """ Document defining QChem calculation inputs. """ initial_molecule: Molecule | None = Field( None, description="input molecule geometry before the QChem calculation" ) # parameters: dict[str, Any] = Field( # None, description = "Parameters from a previous QChem calculation." # ) charge: int | None = Field(None, description="The charge of the input molecule") rem: dict[str, Any] | None = Field( None, description="The rem dict of the input file which has all the input parameters", ) job_type: str | None = Field( None, description="The type of QChem calculation being performed" ) opt: dict[str, Any] | None = Field( None, description="A dictionary of opt section. For instance atom constraints and fixed atoms. Go to QCInput definition for more details.", ) pcm: dict[str, Any] | None = Field( None, description="A dictionary for the PCM solvent details if used" ) solvent: dict[str, Any] | None = Field( None, description="The solvent parameters used if the PCM solvent model has been employed", ) smx: dict[str, Any] | None = Field( None, description="A dictionary for the solvent parameters if the SMD solvent method has been employed", ) vdw_mode: str | None = Field( None, description="Either atomic or sequential. Used when custon van der Waals radii are used to construct pcm cavity", ) van_der_waals: dict[str, Any] | None = Field( None, description="The dictionary of the custom van der Waals radii if used" ) scan_variables: dict[str, Any] | None = Field( None, description="The dictionary of scan variables for torsions or bond stretches", ) tags: dict[str, Any] | str | None = Field( None, description="Any tags associated with the QChem calculation." ) @classmethod def from_qcinput(cls, qcinput: QCInput) -> "CalculationInput": """ Create a QChem input document from a QCInout object. Parameters ---------- qcinput A QCInput object. Returns -------- CalculationInput The input document. """ # QCInput.molecule is either a Molecule, list of Molecule, or a str-literal # Catch latter two cases here and error out if not isinstance(qcinput.molecule, Molecule): raise ValueError("QCInput contains multiple molecules.") return cls( # type: ignore[call-arg] initial_molecule=qcinput.molecule, charge=( int(qcinput.molecule.as_dict()["charge"]) if qcinput.molecule.as_dict() else None ), rem=qcinput.rem, job_type=qcinput.rem.get("job_type", None), opt=qcinput.opt, pcm=qcinput.pcm, solvent=qcinput.solvent, smx=qcinput.smx, vdw_mode=qcinput.vdw_mode, scan_variables=qcinput.scan, van_der_waals=qcinput.van_der_waals, ) @arrow_incompatible class CalculationOutput(BaseModel): """Document defining QChem calculation outputs.""" optimized_molecule: Molecule | None = Field( None, description="optimized geometry of the molecule after calculation in case of opt, optimization or ts", ) mulliken: list | dict[str, Any] | None = Field( None, description="Calculate Mulliken charges on the atoms" ) esp: list | dict[str, Any] | None = Field( None, description="Calculated charges on the atoms if esp calculation has been performed", ) resp: list | dict[str, Any] | None = Field( None, description="Calculated charges on the atoms if resp calculation has been performed", ) nbo_data: dict[str, Any] | None = Field( None, description="NBO data if analysis has been performed." ) frequencies: dict[str, Any] | list | None = Field( None, description="Calculated frequency modes if the job type is freq or frequency", ) frequency_modes: list | str | None = Field( None, description="The list of calculated frequency mode vectors" ) final_energy: str | float | None = Field( None, description="The final energy of the molecule after the calculation is complete", ) enthalpy: str | float | None = Field( None, description="The total enthalpy correction if a frequency calculation has been performed", ) entropy: str | float | None = Field( None, description="The total entropy of the system if a frequency calculation has been performed", ) scan_energies: dict[str, Any] | None = Field( None, description="A dictionary of the scan energies with their respective parameters", ) scan_geometries: list | dict[str, Any] | None = Field( None, description="optimized geometry of the molecules after the geometric scan" ) scan_molecules: list | dict[str, Any] | Molecule | None = Field( None, description="The constructed pymatgen molecules from the optimized scan geometries", ) pcm_gradients: dict[str, Any] | np.ndarray | list | None = Field( None, description="The parsed total gradients after adding the PCM contributions.", ) @field_validator("pcm_gradients", mode="before") @classmethod def validate_pcm_gradients(cls, v): if v is not None and not isinstance(v, (np.ndarray, dict, list)): raise ValueError( "pcm_gradients must be a numpy array, a dict or a list or None." ) return v cds_gradients: dict[str, Any] | np.ndarray | list | None = Field( None, description="The parsed CDS gradients." ) @field_validator("cds_gradients", mode="before") @classmethod def validate_cds_gradients(cls, v): if v is not None and not isinstance(v, (np.ndarray, dict, list)): raise ValueError( "cds_gradients must be a numpy array, a dict or a list or None." ) return v dipoles: dict[str, Any] | None = Field( None, description="The associated dipoles for Mulliken/RESP/ESP charges" ) gap_info: dict[str, Any] | None = Field( None, description="The Kohn-Sham HOMO-LUMO gaps and associated Eigenvalues" ) @classmethod def from_qcoutput(cls, qcoutput: QCOutput) -> "CalculationOutput": """ Create a QChem output document from a QCOutput object. Parameters ---------- qcoutput A QCOutput object. Returns -------- CalculationOutput The output document. """ optimized_molecule = qcoutput.data.get("molecule_from_optimized_geometry", {}) optimized_molecule = optimized_molecule if optimized_molecule else None return cls( optimized_molecule=optimized_molecule, mulliken=qcoutput.data.get(["Mulliken"][-1], []), esp=qcoutput.data.get(["ESP"][-1], []), resp=qcoutput.data.get(["RESP"][-1], []), nbo_data=qcoutput.data.get("nbo_data", {}), frequencies=qcoutput.data.get("frequencies", {}), frequency_modes=qcoutput.data.get("frequency_mode_vectors", []), final_energy=qcoutput.data.get("final_energy", None), enthalpy=qcoutput.data.get("total_enthalpy", None), entropy=qcoutput.data.get("total_entropy", None), scan_energies=qcoutput.data.get("scan_energies", {}), scan_geometries=qcoutput.data.get("optimized_geometries", {}), scan_molecules=qcoutput.data.get("molecules_from_optimized_geometries", {}), pcm_gradients=qcoutput.data.get(["pcm_gradients"][0], None), cds_gradients=qcoutput.data.get(["CDS_gradients"][0], None), dipoles=qcoutput.data.get("dipoles", None), gap_info=qcoutput.data.get("gap_info", None), ) model_config = ConfigDict(arbitrary_types_allowed=True) # TODO What can be done for the trajectories, also how will walltime and cputime be reconciled @arrow_incompatible class Calculation(BaseModel): """Full QChem calculation inputs and outputs.""" dir_name: str = Field(description="The directory for this QChem calculation") qchem_version: str | None = Field( None, description="QChem version used to perform the current calculation" ) has_qchem_completed: QChemStatus | bool | None = Field( None, description="Whether QChem calculated the calculation successfully" ) input: CalculationInput | None = Field( None, description="QChem input settings for the calculation" ) output: CalculationOutput | None = Field( None, description="The QChem calculation output document" ) completed_at: str | None = Field( None, description="Timestamp for when the calculation was completed" ) task_name: str | None = Field( None, description="Name of task given by custodian (e.g. opt1, opt2, freq1, freq2)", ) output_file_paths: dict[str, str | Path | dict[str, Path]] | None = Field( None, description="Paths (relative to dir_name) of the QChem output files associated with this calculation", ) level_of_theory: LevelOfTheory | str = Field( None, description="Levels of theory used for the QChem calculation: For instance, B97-D/6-31g*", ) solvation_lot_info: str | None = Field( None, description="A condensed string representation of the comboned LOT and Solvent info", ) task_type: TaskType | None = Field( None, description="Calculation task type like Single Point, Geometry Optimization. Frequency...", ) calc_type: CalcType | str | None = Field( None, description="Combination dict of LOT + TaskType: B97-D/6-31g*/VACUUM Geometry Optimization", ) @classmethod def from_qchem_files( cls, dir_name: Path | str, task_name: str, qcinput_file: Path | str, qcoutput_file: Path | str, validate_lot: bool = True, store_energy_trajectory: bool = False, qcinput_kwargs: dict | None = None, qcoutput_kwargs: dict | None = None, ) -> "Calculation": """ Create a QChem calculation document from a directory and file paths. Parameters ---------- dir_name The directory containing the QChem calculation outputs. task_name The task name. qcinput_file Path to the .in/qin file, relative to dir_name. qcoutput_file Path to the .out/.qout file, relative to dir_name. store_energy_trajectory Whether to store the energy trajectory during a QChem calculation #TODO: Revisit this- False for now. qcinput_kwargs Additional keyword arguments that will be passed to the qcinput file qcoutput_kwargs Additional keyword arguments that will be passed to the qcoutput file Returns ------- Calculation A QChem calculation document. """ dir_name = Path(dir_name) qcinput_file = dir_name / qcinput_file qcoutput_file = dir_name / qcoutput_file output_file_paths = _find_qchem_files(dir_name) qcinput_kwargs = qcinput_kwargs if qcinput_kwargs else {} qcinput = QCInput.from_file(qcinput_file, **qcinput_kwargs) qcoutput_kwargs = qcoutput_kwargs if qcoutput_kwargs else {} qcoutput = QCOutput(str(qcoutput_file), **qcoutput_kwargs) completed_at = str(datetime.fromtimestamp(qcoutput_file.stat().st_mtime)) input_doc = CalculationInput.from_qcinput(qcinput) output_doc = CalculationOutput.from_qcoutput(qcoutput) has_qchem_completed = ( QChemStatus.SUCCESS if qcoutput.data.get("completion", []) else QChemStatus.FAILED ) if store_energy_trajectory: warnings.warn("Still have to figure the energy trajectory") return cls( dir_name=str(dir_name), task_name=task_name, qchem_version=qcoutput.data["version"], has_qchem_completed=has_qchem_completed, completed_at=completed_at, input=input_doc, output=output_doc, output_file_paths={ k.lower(): ( Path(v) if isinstance(v, str) else {k2: Path(v2) for k2, v2 in v.items()} ) for k, v in output_file_paths.items() }, level_of_theory=level_of_theory(input_doc, validate_lot=validate_lot), solvation_lot_info=lot_solvent_string(input_doc, validate_lot=validate_lot), task_type=task_type(input_doc), calc_type=calc_type(input_doc, validate_lot=validate_lot), ) def _find_qchem_files( path: str | Path, ) -> dict[str, Any]: """ Find QChem files in a directory. Only the mol.qout file (or alternatively files with the task name as an extension, e.g., mol.qout.opt_0.gz, mol.qout.freq_1.gz, or something like this...) will be returned. Parameters ---------- path Path to a directory to search. Returns ------- dict[str, Any] The filenames of the calculation outputs for each QChem task, given as a ordered dictionary of:: { task_name:{ "qchem_out_file": qcrun_filename, }, ... } If there is only 1 qout file task_name will be "standard" otherwise it will be the extension name like "opt_0" """ path = Path(path) task_files = OrderedDict() in_file_pattern = re.compile(r"^(?Pmol\.(qin|in)(?:\..+)?)(\.gz)?$") for file in path.iterdir(): if file.is_file(): in_match = in_file_pattern.match(file.name) # This block is for generalizing outputs coming from both atomate and manual qchem calculations if in_match: in_task_name = re.sub( r"(\.gz|gz)$", "", in_match.group("in_task_name").replace("mol.qin.", ""), ) in_task_name = in_task_name or "mol.qin" if in_task_name == "orig": task_files[in_task_name] = {"orig_input_file": file.name} elif in_task_name == "last": continue elif in_task_name == "mol.qin" or in_task_name == "mol.in": if in_task_name == "mol.qin": out_file = ( path / "mol.qout.gz" if (path / "mol.qout.gz").exists() else path / "mol.qout" ) else: out_file = ( path / "mol.out.gz" if (path / "mol.out.gz").exists() else path / "mol.out" ) task_files["standard"] = { "qcinput_file": file.name, "qcoutput_file": out_file.name, } # This block will exist only if calcs were run through atomate else: try: task_files[in_task_name] = { "qcinput_file": file.name, "qcoutput_file": Path( "mol.qout." + in_task_name + ".gz" ).name, } except FileNotFoundError: task_files[in_task_name] = { "qcinput_file": file.name, "qcoutput_file": "No qout files exist for this in file", } return task_files def level_of_theory( parameters: CalculationInput, validate_lot: bool = True ) -> LevelOfTheory: """ Returns the level of theory for a calculation, based on the input parameters given to Q-Chem Args: parameters: dict of Q-Chem input parameters """ rem_pars = parameters.rem or {} funct_raw = rem_pars.get("method") basis_raw = rem_pars.get("basis") if funct_raw is None or basis_raw is None: raise ValueError( 'Method and basis must be included in "rem" section ' "of parameters!" ) disp_corr = rem_pars.get("dft_d") if disp_corr is None: funct_lower = funct_raw.lower() funct_lower = functional_synonyms.get(funct_lower, funct_lower) else: # Replace Q-Chem terms for D3 tails with more common expressions disp_corr = disp_corr.replace("_bj", "(bj)").replace("_zero", "(0)") funct_lower = f"{funct_raw}-{disp_corr}" basis_lower = basis_raw.lower() solvent_method = rem_pars.get("solvent_method", "").lower() if solvent_method == "": solvation = "VACUUM" elif solvent_method in ["pcm", "cosmo"]: solvation = "PCM" # TODO: Add this once added into pymatgen and atomate # elif solvent_method == "isosvp": # if parameters.get("svp", {}).get("idefesr", 0): # solvation = "CMIRS" # else: # solvation = "ISOSVP" elif solvent_method == "smd": solvation = "SMD" else: raise ValueError(f"Unexpected implicit solvent method {solvent_method}!") if validate_lot: # --> TODO: replace with enums functional = [f for f in FUNCTIONALS if f.lower() == funct_lower] if not functional: raise ValueError(f"Unexpected functional {funct_lower}!") functional = functional[0] basis = [b for b in BASIS_SETS if b.lower() == basis_lower] if not basis: raise ValueError(f"Unexpected basis set {basis_lower}!") basis = basis[0] # <-- lot = f"{functional}/{basis}/{solvation}" return LevelOfTheory(lot) else: warnings.warn( "User has turned the validate flag off." "This can have downstream effects if the chosen functional and basis " "is not in the available sets of MP employed functionals and the user" "wants to include the TaskDoc in the MP infrastructure." "Users should ignore this warning if their objective is just to create TaskDocs", UserWarning, stacklevel=2, ) functional = funct_lower basis = basis_lower lot = f"{functional}/{basis}/{solvation}" return lot def solvent( parameters: CalculationInput, validate_lot: bool = True, custom_smd: str | None = None, ) -> str: """ Returns the solvent used for this calculation. Args: parameters: dict of Q-Chem input parameters custom_smd: (Optional) string representing SMD parameters for a non-standard solvent """ lot = level_of_theory(parameters, validate_lot=validate_lot) if validate_lot: solvation = lot.value.split("/")[-1] else: solvation = lot.split("/")[-1] if solvation == "PCM": # dielectric = float(parameters.get("solvent", {}).get("dielectric", 78.39)) # dielectric = float(parameters.get("solvent", {})) # dielectric = getattr(parameters, "solvent", None) # dielectric_string = f"{dielectric.get('dielectric', '0.0'):.2f}".replace(".", ",") dielectric_string = getattr(parameters, "solvent", None) return f"DIELECTRIC= {dielectric_string}" # TODO: Add this once added into pymatgen and atomate # elif solvation == "ISOSVP": # dielectric = float(parameters.get("svp", {}).get("dielst", 78.39)) # rho = float(parameters.get("svp", {}).get("rhoiso", 0.001)) # return f"DIELECTRIC={round(dielectric, 2)},RHO={round(rho, 4)}" # elif solvation == "CMIRS": # dielectric = float(parameters.get("svp", {}).get("dielst", 78.39)) # rho = float(parameters.get("svp", {}).get("rhoiso", 0.001)) # a = parameters.get("pcm_nonels", {}).get("a") # b = parameters.get("pcm_nonels", {}).get("b") # c = parameters.get("pcm_nonels", {}).get("c") # d = parameters.get("pcm_nonels", {}).get("d") # solvrho = parameters.get("pcm_nonels", {}).get("solvrho") # gamma = parameters.get("pcm_nonels", {}).get("gamma") # # string = f"DIELECTRIC={round(dielectric, 2)},RHO={round(rho, 4)}" # for name, (piece, digits) in {"A": (a, 6), "B": (b, 6), "C": (c, 1), "D": (d, 3), # "SOLVRHO": (solvrho, 2), "GAMMA": (gamma, 1)}.items(): # if piece is None: # piecestring = "NONE" # else: # piecestring = f"{name}={round(float(piece), digits)}" # string += "," + piecestring # return string elif solvation == "SMD": solvent = (parameters.smx or {}).get("solvent", "water") if solvent == "other": if custom_smd is None: raise ValueError( "SMD calculation with solvent=other requires custom_smd!" ) names = ["DIELECTRIC", "N", "ALPHA", "BETA", "GAMMA", "PHI", "PSI"] numbers = [float(x) for x in custom_smd.split(",")] string = "" for name, number in zip(names, numbers): string += f"{name}={number:.3f};" return string.rstrip(",").rstrip(";").replace(".", ",") else: return f"SOLVENT={solvent.upper()}" else: return "NONE" def lot_solvent_string( parameters: CalculationInput, validate_lot: bool = True, custom_smd: str | None = None, ) -> str: """ Returns a string representation of the level of theory and solvent used for this calculation. Args: parameters: dict of Q-Chem input parameters custom_smd: (Optional) string representing SMD parameters for a non-standard solvent """ if validate_lot: lot = level_of_theory(parameters, validate_lot=validate_lot).value else: lot = level_of_theory(parameters, validate_lot=validate_lot) solv = solvent(parameters, custom_smd=custom_smd, validate_lot=validate_lot) return f"{lot}({solv})" def task_type( parameters: CalculationInput, special_run_type: str | None = None ) -> TaskType: if special_run_type == "frequency_flattener": return TaskType("Frequency Flattening Geometry Optimization") elif special_run_type == "ts_frequency_flattener": return TaskType("Frequency Flattening Transition State Geometry Optimization") if parameters.job_type == "sp": return TaskType("Single Point") elif parameters.job_type == "force": return TaskType("Force") elif parameters.job_type == "opt": return TaskType("Geometry Optimization") elif parameters.job_type == "ts": return TaskType("Transition State Geometry Optimization") elif parameters.job_type == "freq": return TaskType("Frequency Analysis") return TaskType("Unknown") def calc_type( parameters: CalculationInput, validate_lot: bool = True, special_run_type: str | None = None, ) -> CalcType: """ Determines the calc type Args: parameters: CalculationInput parameters """ tt = task_type(parameters, special_run_type=special_run_type).value if validate_lot: rt = level_of_theory(parameters, validate_lot=validate_lot).value return CalcType(f"{rt} {tt}") else: rt = level_of_theory(parameters, validate_lot=validate_lot) return str(f"{rt} {tt}") ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8252716 emmet_core-0.87.2/emmet/core/qchem/molecule.py0000644000175100017510000004402715240717720020724 0ustar00runnerrunner"""Core definition of a Molecule Document""" from typing import Any from pydantic import Field from emmet.core.io.pymatgen import MoleculeMatcher, Molecule, BabelMolAdaptor from emmet.core.material import CoreMoleculeDoc from emmet.core.molecules import MolPropertyOrigin from emmet.core.mpid import MPculeID from emmet.core.qchem.calc_types import CalcType, LevelOfTheory, TaskType from emmet.core.qchem.task import TaskDocument from emmet.core.settings import EmmetSettings from emmet.core.utils import arrow_incompatible, get_molecule_id try: import openbabel except ImportError: openbabel = None __author__ = "Evan Spotte-Smith " SETTINGS = EmmetSettings() def evaluate_lot( lot: LevelOfTheory | str, funct_scores: dict[str, int] = SETTINGS.QCHEM_FUNCTIONAL_QUALITY_SCORES, basis_scores: dict[str, int] = SETTINGS.QCHEM_BASIS_QUALITY_SCORES, solvent_scores: dict[str, int] = SETTINGS.QCHEM_SOLVENT_MODEL_QUALITY_SCORES, ): """ Score the various components of a level of theory (functional, basis set, and solvent model), where a lower score is better than a higher score. :param lot: Level of theory to be evaluated :param funct_scores: Scores for various density functionals :param basis_scores: Scores for various basis sets :param solvent_scores: Scores for various implicit solvent models :return: """ if isinstance(lot, LevelOfTheory): lot_comp = lot.value.split("/") else: lot_comp = lot.split("/") return ( -1 * funct_scores.get(lot_comp[0], 0), -1 * basis_scores.get(lot_comp[1], 0), -1 * solvent_scores.get(lot_comp[2], 0), ) def evaluate_task( task: TaskDocument, funct_scores: dict[str, int] = SETTINGS.QCHEM_FUNCTIONAL_QUALITY_SCORES, basis_scores: dict[str, int] = SETTINGS.QCHEM_BASIS_QUALITY_SCORES, solvent_scores: dict[str, int] = SETTINGS.QCHEM_SOLVENT_MODEL_QUALITY_SCORES, task_quality_scores: dict[str, int] = SETTINGS.QCHEM_TASK_QUALITY_SCORES, ): """ Helper function to order optimization calcs by - Level of theory - Electronic energy Note that lower scores indicate a higher quality. :param task: Task to be evaluated :param funct_scores: Scores for various density functionals :param basis_scores: Scores for various basis sets :param solvent_scores: Scores for various implicit solvent models :param task_quality_scores: Scores for various task types :return: tuple representing different levels of evaluation: - Task validity - Level of theory score - Task score - Electronic energy """ lot = task.level_of_theory lot_eval = evaluate_lot( lot, funct_scores=funct_scores, basis_scores=basis_scores, solvent_scores=solvent_scores, ) return ( -1 * int(task.is_valid), sum(lot_eval), -1 * task_quality_scores.get(task.task_type.value, 0), task.output.final_energy, ) def evaluate_task_entry( entry: dict[str, Any], funct_scores: dict[str, int] = SETTINGS.QCHEM_FUNCTIONAL_QUALITY_SCORES, basis_scores: dict[str, int] = SETTINGS.QCHEM_BASIS_QUALITY_SCORES, solvent_scores: dict[str, int] = SETTINGS.QCHEM_SOLVENT_MODEL_QUALITY_SCORES, task_quality_scores: dict[str, int] = SETTINGS.QCHEM_TASK_QUALITY_SCORES, ): """ Helper function to order optimization calcs by - Level of theory - Electronic energy Note that lower scores indicate a higher quality. :param task: Task to be evaluated :param funct_scores: Scores for various density functionals :param basis_scores: Scores for various basis sets :param solvent_scores: Scores for various implicit solvent models :param task_quality_scores: Scores for various task types :return: tuple representing different levels of evaluation: - Level of theory score - Task score - Electronic energy """ lot = entry["level_of_theory"] lot_eval = evaluate_lot( lot, funct_scores=funct_scores, basis_scores=basis_scores, solvent_scores=solvent_scores, ) return ( sum(lot_eval), -1 * task_quality_scores.get(entry["task_type"], 0), entry["energy"], ) @arrow_incompatible class MoleculeDoc(CoreMoleculeDoc): species: list[str] | None = Field( None, description="Ordered list of elements/species in this Molecule." ) molecules: dict[str, Molecule] | None = Field( None, description="The lowest energy optimized structures for this molecule for each solvent.", ) molecule_levels_of_theory: dict[str, str] | None = Field( None, description="Level of theory used to optimize the best molecular structure for each solvent.", ) inchi: str | None = Field( None, description="International Chemical Identifier (InChI) for this molecule" ) inchi_key: str | None = Field( None, description="Standardized hash of the InChI for this molecule" ) calc_types: dict[str, CalcType] | None = Field( # type: ignore None, description="Calculation types for all the calculations that make up this molecule", ) task_types: dict[str, TaskType] | None = Field( None, description="Task types for all the calculations that make up this molecule", ) levels_of_theory: dict[str, LevelOfTheory] | None = Field( None, description="Levels of theory types for all the calculations that make up this molecule", ) solvents: dict[str, str] | None = Field( None, description="Solvents (solvent parameters) for all the calculations that make up this molecule", ) lot_solvents: dict[str, str] | None = Field( None, description="Combinations of level of theory and solvent for all calculations that make up this molecule", ) unique_calc_types: list[CalcType] | None = Field( None, description="Collection of all unique calculation types used for this molecule", ) unique_task_types: list[TaskType] | None = Field( None, description="Collection of all unique task types used for this molecule", ) unique_levels_of_theory: list[LevelOfTheory] | None = Field( None, description="Collection of all unique levels of theory used for this molecule", ) unique_solvents: list[str] | None = Field( None, description="Collection of all unique solvents (solvent parameters) used for this molecule", ) unique_lot_solvents: list[str] | None = Field( None, description="Collection of all unique combinations of level of theory and solvent used for this molecule", ) origins: list[MolPropertyOrigin] | None = Field( None, description="List of property origins for tracking the provenance of properties", ) entries: list[dict[str, Any]] | None = Field( None, description="Dictionary representations of all task documents for this molecule", ) best_entries: dict[str, dict[str, Any]] | None = Field( None, description="Mapping for tracking the best entries at each level of theory (+ solvent) for Q-Chem calculations", ) constituent_molecules: list[MPculeID] | None = Field( None, description="For cases where data from multiple MoleculeDocs have been compiled, a list of " "MPculeIDs of documents used to construct this document", ) similar_molecules: list[MPculeID] | None = Field( None, description="List of MPculeIDs with of molecules similar (by e.g. structure) to this one", ) @classmethod def from_tasks( cls, task_group: list[TaskDocument], ) -> "MoleculeDoc": """ Converts a group of tasks into one molecule document Args: task_group: List of task document """ if openbabel is None: raise ModuleNotFoundError( "openbabel must be installed to instantiate a MoleculeDoc from tasks" ) if len(task_group) == 0: raise Exception("Must have more than one task in the group.") entries = [t.entry for t in task_group] # Metadata last_updated = max(task.last_updated for task in task_group) created_at = min(task.last_updated for task in task_group) task_ids = list({task.task_id for task in task_group}) deprecated_tasks = {task.task_id for task in task_group if not task.is_valid} levels_of_theory = {task.task_id: task.level_of_theory for task in task_group} solvents = {task.task_id: task.solvent for task in task_group} lot_solvents = {task.task_id: task.lot_solvent for task in task_group} task_types = {task.task_id: task.task_type for task in task_group} calc_types = {task.task_id: task.calc_type for task in task_group} unique_lots = list(set(levels_of_theory.values())) unique_solvents = list(set(solvents.values())) unique_lot_solvents = list(set(lot_solvents.values())) unique_task_types = list(set(task_types.values())) unique_calc_types = list(set(calc_types.values())) mols = [task.output.initial_molecule for task in task_group] # If we're dealing with single-atoms, process is much different if all([len(m) == 1 for m in mols]): sorted_tasks = sorted(task_group, key=evaluate_task) molecule = sorted_tasks[0].output.initial_molecule species = [e.symbol for e in molecule.species] molecule_id = get_molecule_id(molecule, node_attr="coords") # Initial molecules. No geometry should change for a single atom initial_molecules = [molecule] # Deprecated deprecated = all(task.task_id in deprecated_tasks for task in task_group) # Origins origins = [ MolPropertyOrigin( name="molecule", task_id=sorted_tasks[0].task_id, last_updated=sorted_tasks[0].last_updated, ) ] # entries best_entries = dict() for lot_solv in unique_lot_solvents: relevant_calcs = sorted( [ doc for doc in task_group if doc.lot_solvent == lot_solv and doc.is_valid ], key=evaluate_task, ) if len(relevant_calcs) > 0: best_task_doc = relevant_calcs[0] entry = best_task_doc.entry best_entries[lot_solv] = entry else: geometry_optimizations = [ task for task in task_group if task.task_type in [ TaskType.Geometry_Optimization, TaskType.Frequency_Flattening_Geometry_Optimization, "Geometry Optimization", "Frequency Flattening Geometry Optimization", ] # noqa: E501 ] try: best_molecule_calc = sorted(geometry_optimizations, key=evaluate_task)[ 0 ] except IndexError: raise Exception("No geometry optimization calculations available!") molecule = best_molecule_calc.output.optimized_molecule species = [e.symbol for e in molecule.species] molecule_id = get_molecule_id(molecule, node_attr="coords") # Initial molecules initial_molecules = list() for task in task_group: if isinstance(task.orig["molecule"], Molecule): initial_molecules.append(task.orig["molecule"]) else: initial_molecules.append(Molecule.from_dict(task.orig["molecule"])) mm = MoleculeMatcher() initial_molecules = [ group[0] for group in mm.group_molecules(initial_molecules) ] # Deprecated deprecated = all( task.task_id in deprecated_tasks for task in geometry_optimizations ) deprecated = deprecated or best_molecule_calc.task_id in deprecated_tasks # Origins origins = [ MolPropertyOrigin( name="molecule", task_id=best_molecule_calc.task_id, last_updated=best_molecule_calc.last_updated, ) ] # entries best_entries = dict() all_lot_solvs = set(lot_solvents.values()) for lot_solv in all_lot_solvs: relevant_calcs = sorted( [ doc for doc in geometry_optimizations if doc.lot_solvent == lot_solv and doc.is_valid ], key=evaluate_task, ) if len(relevant_calcs) > 0: best_task_doc = relevant_calcs[0] entry = best_task_doc.entry best_entries[lot_solv] = entry for entry in entries: entry["entry_id"] = molecule_id ad = BabelMolAdaptor(molecule) openbabel.StereoFrom3D(ad.openbabel_mol) inchi = ad.pybel_mol.write("inchi").strip() # type: ignore[attr-defined] inchikey = ad.pybel_mol.write("inchikey").strip() # type: ignore[attr-defined] return cls.from_molecule( molecule=molecule, molecule_id=molecule_id, species=species, inchi=inchi, inchi_key=inchikey, initial_molecules=initial_molecules, last_updated=last_updated, created_at=created_at, task_ids=task_ids, calc_types=calc_types, levels_of_theory=levels_of_theory, solvents=solvents, lot_solvents=lot_solvents, task_types=task_types, unique_levels_of_theory=unique_lots, unique_solvents=unique_solvents, unique_lot_solvents=unique_lot_solvents, unique_task_types=unique_task_types, unique_calc_types=unique_calc_types, deprecated=deprecated, deprecated_tasks=deprecated_tasks, origins=origins, entries=entries, best_entries=best_entries, ) @classmethod def construct_deprecated_molecule( cls, task_group: list[TaskDocument], ) -> "MoleculeDoc": """ Converts a group of tasks into a deprecated molecule document Args: task_group: List of task document """ if len(task_group) == 0: raise Exception("Must have more than one task in the group.") # Metadata last_updated = max(task.last_updated for task in task_group) created_at = min(task.last_updated for task in task_group) task_ids = list({task.task_id for task in task_group}) deprecated_tasks = {task.task_id for task in task_group} levels_of_theory = {task.task_id: task.level_of_theory for task in task_group} solvents = {task.task_id: task.solvent for task in task_group} lot_solvents = {task.task_id: task.lot_solvent for task in task_group} task_types = {task.task_id: task.task_type for task in task_group} calc_types = {task.task_id: task.calc_type for task in task_group} unique_lots = list(set(levels_of_theory.values())) unique_solvents = list(set(solvents.values())) unique_lot_solvents = list(set(lot_solvents.values())) unique_task_types = list(set(task_types.values())) unique_calc_types = list(set(calc_types.values())) # Arbitrarily choose task with lowest ID molecule = sorted(task_group, key=lambda x: x.task_id)[ 0 ].output.initial_molecule species = [e.symbol for e in molecule.species] # Molecule ID molecule_id = get_molecule_id(molecule, "coords") ad = BabelMolAdaptor(molecule) openbabel.StereoFrom3D(ad.openbabel_mol) inchi = ad.pybel_mol.write("inchi").strip() # type: ignore[attr-defined] inchikey = ad.pybel_mol.write("inchikey").strip() # type: ignore[attr-defined] return cls.from_molecule( molecule=molecule, molecule_id=molecule_id, species=species, inchi=inchi, inchi_key=inchikey, last_updated=last_updated, created_at=created_at, task_ids=task_ids, calc_types=calc_types, levels_of_theory=levels_of_theory, solvents=solvents, lot_solvents=lot_solvents, task_types=task_types, unique_levels_of_theory=unique_lots, unique_solvents=unique_solvents, unique_lot_solvents=unique_lot_solvents, unique_task_types=unique_task_types, unique_calc_types=unique_calc_types, deprecated=True, deprecated_tasks=deprecated_tasks, ) def best_lot( mol_doc: MoleculeDoc, funct_scores: dict[str, int] = SETTINGS.QCHEM_FUNCTIONAL_QUALITY_SCORES, basis_scores: dict[str, int] = SETTINGS.QCHEM_BASIS_QUALITY_SCORES, solvent_scores: dict[str, int] = SETTINGS.QCHEM_SOLVENT_MODEL_QUALITY_SCORES, ) -> str: """ Return the best level of theory used within a MoleculeDoc :param mol_doc: MoleculeDoc :param funct_scores: Scores for various density functionals :param basis_scores: Scores for various basis sets :param solvent_scores: Scores for various implicit solvent models :return: string representation of LevelOfTheory """ sorted_lots = sorted( mol_doc.best_entries.keys(), # type: ignore key=lambda x: evaluate_lot(x, funct_scores, basis_scores, solvent_scores), ) best = sorted_lots[0] if isinstance(best, LevelOfTheory): return best.value else: return best ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8252716 emmet_core-0.87.2/emmet/core/qchem/task.py0000644000175100017510000002052615240717720020057 0ustar00runnerrunner"""Core definition of a Q-Chem Task Document""" from __future__ import annotations from typing import TYPE_CHECKING, Any from pydantic import BaseModel, Field from emmet.core.io.pymatgen import Molecule from emmet.core.mpid import MPID, MPculeID from emmet.core.qchem.calc_types import ( CalcType, LevelOfTheory, TaskType, calc_type, level_of_theory, lot_solvent_string, solvent, task_type, ) from emmet.core.structure import MoleculeMetadata from emmet.core.task import BaseTaskDocument from emmet.core.types.enums import ValueEnum from emmet.core.utils import arrow_incompatible if TYPE_CHECKING: from collections.abc import Callable __author__ = "Evan Spotte-Smith " class QChemStatus(ValueEnum): """ Q-Chem Calculation State """ SUCCESS = "successful" FAILED = "unsuccessful" @arrow_incompatible class OutputSummary(BaseModel): """ Summary of an output for a Q-Chem calculation """ initial_molecule: Molecule | None = Field(None, description="Input Molecule object") optimized_molecule: Molecule | None = Field( None, description="Optimized Molecule object" ) final_energy: float | None = Field( None, description="Final electronic energy for the calculation (units: Hartree)" ) enthalpy: float | None = Field( None, description="Total enthalpy of the molecule (units: kcal/mol)" ) entropy: float | None = Field( None, description="Total entropy of the molecule (units: cal/mol-K" ) mulliken: list[Any] | None = Field( None, description="Mulliken atomic partial charges and partial spins" ) resp: list[float] | None = Field( None, description="Restrained Electrostatic Potential (RESP) atomic partial charges", ) nbo: dict[str, Any] | None = Field( None, description="Natural Bonding Orbital (NBO) output" ) frequencies: list[float] | None = Field( None, description="Vibrational frequencies of the molecule (units: cm^-1)" ) dipoles: dict[str, Any] | None = Field( None, description="Electric dipole information for the molecule" ) gradients: list[list[float]] | None = Field( None, description="Atomic forces, in atomic units (Ha/Bohr)" ) precise_gradients: list[list[float]] | None = Field( None, description="High-precision atomic forces, in atomic units (Ha/Bohr)" ) pcm_gradients: list[list[float]] | None = Field( None, description="Electrostatic atomic forces from polarizable continuum model (PCM) implicit solvation," "in atomic units (Ha/Bohr).", ) CDS_gradients: list[list[float]] | None = Field( None, description="Atomic force contributions from cavitation, dispersion, and structural rearrangement in the SMx" "family of implicit solvent models, in atomic units (Ha/Bohr)", ) def as_dict(self) -> dict[str, Any]: return { "@module": self.__class__.__module__, "@class": self.__class__.__name__, "initial_molecule": self.initial_molecule, "optimized_molecule": self.optimized_molecule, "final_energy": self.final_energy, "enthalpy": self.enthalpy, "entropy": self.entropy, "mulliken": self.mulliken, "resp": self.resp, "nbo": self.nbo, "frequencies": self.frequencies, "dipoles": self.dipoles, "gradients": self.gradients, "precise_gradients": self.precise_gradients, } @arrow_incompatible class TaskDocument(BaseTaskDocument, MoleculeMetadata): """ Definition of a Q-Chem task document """ task_id: MPID | MPculeID | None = Field( None, description="the Task ID For this document" ) calc_code: str = "Q-Chem" completed: bool = True is_valid: bool = Field( True, description="Whether this task document passed validation or not" ) state: QChemStatus | None = Field(None, description="State of this calculation") cputime: float | None = Field(None, description="The system CPU time in seconds") walltime: float | None = Field(None, description="The real elapsed time in seconds") calcs_reversed: list[dict] = Field( [], description="The 'raw' calculation docs used to assembled this task" ) orig: dict[str, Any] = Field( {}, description="Summary of the original Q-Chem inputs" ) output: OutputSummary = Field(OutputSummary()) # type: ignore[call-arg] critic2: dict[str, Any] | None = Field( None, description="Output from Critic2 critical point analysis code" ) custom_smd: str | None = Field( None, description="Parameter string for SMD implicit solvent model" ) special_run_type: str | None = Field( None, description="Special workflow name (if applicable)" ) smiles: str | None = Field( None, description="Simplified molecular-input line-entry system (SMILES) string for the molecule involved " "in this calculation.", ) species_hash: str | None = Field( None, description="Weisfeiler Lehman (WL) graph hash using the atom species as the graph " "node attribute.", ) coord_hash: str | None = Field( None, description="Weisfeiler Lehman (WL) graph hash using the atom coordinates as the graph " "node attribute.", ) # TODO - type of `tags` field seems to differ among task databases # sometimes List, sometimes Dict # left as Any here to ensure tags don't cause validation to fail. tags: Any | None = Field(None, description="Metadata tags") warnings: dict[str, bool] | None = Field( None, description="Any warnings related to this task document" ) @property def level_of_theory(self) -> LevelOfTheory: return level_of_theory(self.orig) @property def solvent(self) -> str: return solvent(self.orig, custom_smd=self.custom_smd) @property def lot_solvent(self) -> str: return lot_solvent_string(self.orig, custom_smd=self.custom_smd) @property def task_type(self) -> TaskType: return task_type(self.orig, special_run_type=self.special_run_type) @property def calc_type(self) -> CalcType: return calc_type(self.special_run_type, self.orig) @property def entry(self) -> dict[str, Any]: mol = None for mol_field in ("optimized_molecule", "initial_molecule"): if mol := getattr(self.output, mol_field, None): break else: raise ValueError("No molecule could be associated with the calculation.") charge = int(mol.charge) if self.charge is None else int(self.charge) spin = ( mol.spin_multiplicity if self.spin_multiplicity is None else self.spin_multiplicity ) entry_dict = { "entry_id": self.task_id, "task_id": self.task_id, "charge": charge, "spin_multiplicity": spin, "level_of_theory": self.level_of_theory, "solvent": self.solvent, "lot_solvent": self.lot_solvent, "custom_smd": self.custom_smd, "task_type": self.task_type, "calc_type": self.calc_type, "molecule": mol, "composition": mol.composition, "formula": mol.composition.alphabetical_formula, "energy": self.output.final_energy, "output": self.output.as_dict(), "critic2": self.critic2, "orig": self.orig, "tags": self.tags, "last_updated": self.last_updated, "species_hash": self.species_hash, "coord_hash": self.coord_hash, } return entry_dict def filter_task_type( entries: list[dict[str, Any]], task_type: TaskType, sort_by: Callable | None = None, ) -> list[dict[str, Any]]: """ Filter (and sort) TaskDocument entries based on task type :param entries: List of TaskDocument entry dicts :param TaskType: TaskType to accept :param sort_by: Function used to sort (default None) :return: Filtered (sorted) list of entries """ filtered = [f for f in entries if f["task_type"] == task_type] return sorted(filtered, key=sort_by) if sort_by is not None else filtered ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8252716 emmet_core-0.87.2/emmet/core/robocrys.py0000644000175100017510000000525315240717720017662 0ustar00runnerrunnerfrom __future__ import annotations from typing import TYPE_CHECKING from pydantic import BaseModel, Field from emmet.core.io.pymatgen import Structure from emmet.core import __version__ from emmet.core.material_property import PropertyDoc from emmet.core.utils import generate_robocrys_condensed_struct_and_description if TYPE_CHECKING: from emmet.core.types.typing import IdentifierType class MineralData(BaseModel): """ Model for mineral data in the condensed structure robocrystallographer field """ type: str | None = Field( None, description="Mineral type.", ) name: str | None = Field(None, description="The mineral name if found.") class CondensedStructureData(BaseModel): """ Model for data in the condensed structure robocrystallographer field More details: https://hackingmaterials.lbl.gov/robocrystallographer/format.html """ mineral: MineralData = Field( description="Matched mineral data for the material.", ) dimensionality: int = Field( description="Dimensionality of the material.", ) formula: str | None = Field( None, description="Formula for the material.", ) spg_symbol: str | None = Field( None, description="Space group symbol of the material.", ) crystal_system: str | None = Field( None, description="Crystal system of the material.", ) class RobocrystallogapherDoc(PropertyDoc): """ This document contains the descriptive data from robocrystallographer for a material: Structural features, mineral prototypes, dimensionality, ... """ property_name: str = "robocrys" description: str = Field( description="Description text from robocrytallographer.", ) condensed_structure: CondensedStructureData = Field( description="Condensed structure data from robocrytallographer.", ) robocrys_version: str = Field( __version__, description="The version of Robocrystallographer used to generate this document.", ) @classmethod def from_structure( cls, structure: Structure, material_id: IdentifierType | None = None, mineral_matcher=None, **kwargs, ): ( condensed_structure, description, ) = generate_robocrys_condensed_struct_and_description( structure=structure, mineral_matcher=mineral_matcher ) return super().from_structure( meta_structure=structure, material_id=material_id, condensed_structure=condensed_structure, description=description, **kwargs, ) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8252716 emmet_core-0.87.2/emmet/core/settings.py0000644000175100017510000002070415240717720017656 0ustar00runnerrunner"""Settings for defaults in the core definitions of Materials Project Documents.""" from __future__ import annotations import gzip import orjson from pathlib import Path from typing import TYPE_CHECKING import requests # type: ignore[import-untyped] from monty.io import zopen from monty.json import MontyDecoder from pydantic import Field, ImportString, field_validator, model_validator from pydantic_settings import BaseSettings, SettingsConfigDict if TYPE_CHECKING: from typing import Any from typing_extensions import Self DEFAULT_CONFIG_FILE_PATH = str(Path("~/.emmet.json").expanduser()) class EmmetSettings(BaseSettings): """ Settings for the emmet- packages Non-core packages should subclass this to get settings specific to their needs The default way to modify these is to modify ~/.emmet.json or set the environment variable EMMET_CONFIG_FILE to point to the json with emmet settings """ config_file: str = Field( DEFAULT_CONFIG_FILE_PATH, description="File to load alternative defaults from" ) LTOL: float = Field( 0.2, description="Fractional length tolerance for structure matching" ) STOL: float = Field( 0.3, description="Site tolerance for structure matching. Defined as the fraction of the" " average free length per atom = ( V / Nsites ) ** (1/3)", ) SYMPREC: float = Field( 0.1, description="Symmetry precision for spglib symmetry finding" ) ANGLE_TOL: float = Field( 5, description="Angle tolerance for structure matching in degrees." ) PGATOL: float = Field( 0.3, description="Distance tolerance to consider sites as symmetrically equivalent.", ) PGAEIGENTOL: float = Field( 0.01, description="Tolerance to compare eigen values of the inertia tensor." ) PGAMATRIXTOL: float = Field( 0.1, description="Tolerance used to generate the full set of symmetry operations of the point group.", ) MAX_PIEZO_MILLER: int = Field( 10, description="Maximum miller allowed for computing strain direction for maximal piezo response", ) QCHEM_FUNCTIONAL_QUALITY_SCORES: dict[str, int] = Field( { "wB97M-V": 7, "wB97X-V": 6, "wB97X-D3": 5, "wB97X-D": 5, "B3LYP": 4, "B97M-V": 3, "B97M-rV": 3, "B97-D3": 2, "B97-D": 2, "PBE": 1, }, description="Dictionary mapping Q-Chem density functionals to a quality score.", ) QCHEM_BASIS_QUALITY_SCORES: dict[str, int] = Field( { "6-31g*": 1, "def2-SVPD": 2, "def2-TZVP": 3, "def2-TZVPD": 4, "def2-TZVPP": 5, "def2-TZVPPD": 6, "def2-QZVPPD": 7, }, description="Dictionary mapping Q-Chem basis sets to a quality score.", ) QCHEM_SOLVENT_MODEL_QUALITY_SCORES: dict[str, int] = Field( {"CMIRS": 7, "SMD": 5, "ISOSVP": 4, "PCM": 3, "VACUUM": 1}, description="Dictionary mapping Q-Chem solvent models to a quality score.", ) QCHEM_TASK_QUALITY_SCORES: dict[str, int] = Field( { "single_point": 1, "geometry optimization": 2, "frequency-flattening geometry optimization": 3, }, description="Dictionary mapping Q-Chem task type to a quality score", ) VASP_STRUCTURE_QUALITY_SCORES: dict[str, int] = Field( {"r2SCAN": 5, "SCAN": 4, "GGA+U": 3, "GGA": 2, "PBEsol": 1}, description="Dictionary Mapping VASP calculation run types to rung level for VASP materials builder structure data", # noqa: E501 ) VASP_KPTS_TOLERANCE: float = Field( 0.9, description="Relative tolerance for kpt density to still be a valid task document", ) VASP_KSPACING_TOLERANCE: float = Field( 0.05, description="Relative tolerance for kspacing to still be a valid task document", ) VASP_MAX_MAGMOM: dict[str, float] = Field( {"Cr": 5}, description="Maximum permitted magnetic moments by element type." ) VASP_DEFAULT_INPUT_SETS: dict[str, ImportString] = Field( { "GGA Structure Optimization": "pymatgen.io.vasp.sets.MPRelaxSet", "GGA+U Structure Optimization": "pymatgen.io.vasp.sets.MPRelaxSet", "r2SCAN Structure Optimization": "pymatgen.io.vasp.sets.MPScanRelaxSet", "SCAN Structure Optimization": "pymatgen.io.vasp.sets.MPScanRelaxSet", "PBEsol Structure Optimization": "pymatgen.io.vasp.sets.MPScanRelaxSet", "GGA Static": "pymatgen.io.vasp.sets.MPStaticSet", "GGA+U Static": "pymatgen.io.vasp.sets.MPStaticSet", "r2SCAN Static": "pymatgen.io.vasp.sets.MPScanStaticSet", "SCAN Static": "pymatgen.io.vasp.sets.MPScanStaticSet", "PBEsol Static": "pymatgen.io.vasp.sets.MPScanStaticSet", "HSE06 Static": "pymatgen.io.vasp.sets.MPScanStaticSet", "GGA NSCF Uniform": "pymatgen.io.vasp.sets.MPNonSCFSet", "GGA+U NSCF Uniform": "pymatgen.io.vasp.sets.MPNonSCFSet", "GGA NSCF Line": "pymatgen.io.vasp.sets.MPNonSCFSet", "GGA+U NSCF Line": "pymatgen.io.vasp.sets.MPNonSCFSet", "GGA NMR Electric Field Gradient": "pymatgen.io.vasp.sets.MPNMRSet", "GGA NMR Nuclear Shielding": "pymatgen.io.vasp.sets.MPNMRSet", "GGA+U NMR Electric Field Gradient": "pymatgen.io.vasp.sets.MPNMRSet", "GGA+U NMR Nuclear Shielding": "pymatgen.io.vasp.sets.MPNMRSet", "GGA Deformation": "pymatgen.io.vasp.sets.MPStaticSet", "GGA+U Deformation": "pymatgen.io.vasp.sets.MPStaticSet", "GGA DFPT Dielectric": "pymatgen.io.vasp.sets.MPStaticSet", "GGA+U DFPT Dielectric": "pymatgen.io.vasp.sets.MPStaticSet", }, description="Default input sets for task validation", ) VASP_VALIDATE_POTCAR_STATS: bool = Field( True, description="Whether to validate POTCAR stat values." ) VASP_CHECKED_LDAU_FIELDS: list[str] = Field( ["LDAUU", "LDAUJ", "LDAUL"], description="LDAU fields to validate for tasks" ) VASP_MAX_SCF_GRADIENT: float = Field( 100, description="Maximum upward gradient in the last SCF for any VASP calculation", ) VASP_USE_STATICS: bool = Field( True, description="Use static calculations for structure and energy along with structure optimizations", ) USE_EMMET_MODELS: bool = Field( False, description=( "Whether to use emmet (True) or pymatgen (False, default) models " "for materials simulation outputs in certain document models " "which are used only in workflows and not in build pipelines." ), ) model_config = SettingsConfigDict(env_prefix="emmet_", extra="ignore") @model_validator(mode="before") @classmethod def load_default_settings(cls, values: Any) -> Any: """ Loads settings from a root file if available and uses that as defaults in place of built in defaults """ config_file_path: str = values.get("config_file", DEFAULT_CONFIG_FILE_PATH) new_values = {} # TODO: do we want to support gzipped config files? if config_file_path.startswith("http"): response = requests.get(config_file_path) if response.content.startswith(b"\x1f\x8b"): new_values = orjson.loads(gzip.decompress(response.content)) else: new_values = orjson.loads(response.content) elif Path(config_file_path).exists(): with zopen(config_file_path, "rb") as f: new_values = orjson.loads(f.read()) new_values.update(values) return new_values @classmethod def autoload(cls, settings: None | dict | Self) -> Self: if settings is None: return cls(**{}) elif isinstance(settings, dict): return cls(**settings) return settings @field_validator("VASP_DEFAULT_INPUT_SETS", mode="before") @classmethod def convert_input_sets(cls, value): if isinstance(value, dict): return {k: MontyDecoder().process_decoded(v) for k, v in value.items()} return value def as_dict(self): """ HotPatch to enable serializing EmmetSettings via Monty """ return self.model_dump(exclude_unset=True, exclude_defaults=True) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8252716 emmet_core-0.87.2/emmet/core/similarity.py0000644000175100017510000004711415240717720020210 0ustar00runnerrunner"""Define metrics of similarity between structures.""" from __future__ import annotations from functools import partial import multiprocessing from pathlib import Path from typing import TYPE_CHECKING import zlib import numpy as np from pydantic import BaseModel, Field, TypeAdapter from emmet.core.featurization import Featurizer, SiteStatsFingerprint from emmet.core.material_property import PropertyDoc from emmet.core.types.enums import ValueEnum from emmet.core.types.typing import MaterialIdentifierType try: import matgl except ImportError: matgl = None # type: ignore[assignment] if TYPE_CHECKING: from emmet.core.io.pymatgen import Structure class SimilarityMethod(ValueEnum): """Indicate which method was used to score similarity.""" CRYSTALNN = "CrystalNN" M3GNET = "M3GNet" def _vector_to_hex_and_norm(vector: list[float]) -> tuple[str, float]: """Convert a list of floats to a hex string. Used internally to transfer/interpret vectors over a GET. Parameters ----------- vector : Sequence of float Returns ----------- str : the hex representation of the unit vector float : the norm of the vector """ v = np.array(vector) vnorm = np.linalg.norm(v) return zlib.compress((v / vnorm).tobytes()).hex(), vnorm # type: ignore[return-value] def _vector_from_hex_and_norm(hexstr: str, vnorm: float) -> list[float]: """Convert a hex string to a list of floats. Used internally to transfer/interpret vectors over a GET. Parameters ----------- str : the hex representation of the unit vector float : the norm of the vector Returns ----------- list of float : the reconstructed vector """ return (vnorm * np.frombuffer(zlib.decompress(bytes.fromhex(hexstr)))).tolist() def _vector_difference_matrix_row( idxs: tuple[int, int], v: np.ndarray, norms: np.ndarray, dtype: np.dtype = np.dtype("float64"), ) -> tuple[tuple[int, int], np.ndarray]: """Compute the distance between a single vector and a list of other vectors. Parameters ----------- idxs : the integer index of the vector in v to use as a fixed point v : the list of all possible vectors norms : the norms of the vectors in v dtype : the numpy dtype of working and returned arrays. Returns ----------- int (copy of idxs) and np.ndarray of the squared vector distances. """ inner = np.zeros((idxs[1] - idxs[0], v.shape[0]), dtype=dtype) _ = np.einsum("ik,jk->ij", v[idxs[0] : idxs[1]], v, out=inner) return idxs, np.array( [ norms[idxs[0] : idxs[1]] + norms[i] - 2 * inner[:, i] for i in range(norms.shape[0]) ], dtype=dtype, ) def vector_difference_matrix( v: np.ndarray, noise_floor: float = 1e-14, spread_rows: int = 0, dtype: np.dtype = np.dtype("float64"), ) -> np.ndarray: """Construct a symmetric matrix of vector differences. Given a list of vectors v, a symmetric matrix D such that: D_ij = | v_i - v_j | D_ji = D_ij if spread_rows is a positive int, then this will return only the upper triangle of D_ij, j >= i, and parallelize construction of the rows of D_ij. Parameters ----------- v : numpy ndarray List of vectors. Axis = 0 should indicate distinct vectors, and axis = 1 their components. noise_floor : float = 1e-14 Any values less than noise_floor will be zeroed out. Helps with loss of precision. spread_rows : int = 0 The number of parallel processes to use in constructing the rows of D_ij dtype : the numpy dtype of the arrays used, defaults to float64 """ x = v.astype(dtype) if dtype != v.dtype else v.copy() vlen = x.shape[0] v_diff = np.zeros(2 * (vlen,), dtype=dtype) norms = np.zeros(vlen, dtype=dtype) _ = np.einsum("ik,ik->i", x, x, out=norms) if not spread_rows: inner = np.zeros(2 * (vlen,), dtype=dtype) _ = np.einsum("ik,jk->ij", x, x, out=inner) for i in range(vlen): v_diff[:, i] = norms + norms[i] - 2 * inner[:, i] else: func = partial(_vector_difference_matrix_row, v=x, norms=norms) with multiprocessing.Pool(spread_rows) as pool: vdiff_blocks = pool.map( func, [ (min(x), 1 + max(x)) for x in np.array_split(range(vlen), spread_rows) ], ) for idxs, row in vdiff_blocks: v_diff[:, idxs[0] : idxs[1]] = row v_diff[v_diff < noise_floor] = 0.0 return v_diff ** (0.5) class SimilarityScorer: """Mixin for ranking the similarity between structures. Parameters ----------- fingerprinter: Featurizer or None (default) A structural featurizer. If None, defaults to the featurizer used in the above reference. """ def _featurize_structure(self, structure: Structure) -> np.ndarray: """Featurize a single structure using a user implemented method Parameters ----------- structure : Structure Returns ----------- np.ndarray """ raise NotImplementedError @staticmethod def _post_process_distance( distances: np.ndarray, ) -> np.ndarray: """Postprocess vector distances to yield consistent similarity scores. This method should return a percentage where 0% indicates no similarity, and 100% indicates identical structures. Defaults to normalizing vector distances by tanh. See CrystalNNSimilarity for another example. Parameters ----------- distances : a matrix of structure feature vector distances. Returns ----------- np.ndarray """ return 100 * (1.0 - np.tanh(distances)) def featurize_structures( self, structures: list[Structure], num_procs: int = 1, ): """Featurize structures using the user-defined _featurize_structure. This method may be preferred when dealing with huge sets of structures. In those cases, getting distances between structures may lead to memory errors. Parameters ----------- structures : list of Structure objects num_procs : int = 1 Number of parallel processes to run in featurizing structures. Returns ----------- np.ndarray : the feature vectors of the input structures. """ if num_procs > 1: with multiprocessing.Pool(num_procs) as pool: _feature_vectors = pool.map(self._featurize_structure, structures) else: _feature_vectors = [ self._featurize_structure(structure) for structure in structures ] return np.array(_feature_vectors) @classmethod def _get_closest_vectors( cls, idx: int, v: np.ndarray, num: int ) -> tuple[np.ndarray, np.ndarray]: """Return only a subset of vectors most similar to a specified vector. Parameters ----------- idx : the specific index of the vector list to isolate v : numpy ndarray, the list of vectors num : the number of closest vectors to return """ dist = np.linalg.norm(v[idx] - v, axis=1) idxs = np.array([j for j in np.argpartition(dist, num) if j != idx])[:num] subset_dist = dist[idxs] sorted_subset_idx = np.argsort(subset_dist) return idxs[sorted_subset_idx], cls._post_process_distance( subset_dist[sorted_subset_idx] ) def get_all_similarity_scores( self, structures: list[Structure], num_procs: int = 1, **kwargs, ) -> tuple[np.ndarray, np.ndarray]: """Rank the similarity between structures using CrystalNN. Parameters ----------- structures : list of Structure objects num_procs : int = 1 Number of parallel processes to run in featurizing structures. **kwargs Kwargs to pass to `vector_difference_matrix` Returns ----------- tuple of np.ndarray, np.ndarray The first array contains the structure feature vectors, and the second their similarity scores. """ feature_vectors = self.featurize_structures(structures, num_procs=num_procs) distances = vector_difference_matrix(feature_vectors, **kwargs) return feature_vectors, self._post_process_distance(distances) def get_most_similar( self, feature_vectors: np.ndarray, num_procs: int = 1, num_top: int = 100, labels: list[str] | None = None, ) -> dict[str, dict[str, list[str] | np.ndarray]]: """Rank the similarity between structures using CrystalNN. Parameters ----------- feature_vectors : list of feature vectors num_procs : int = 1 Number of parallel processes to run in featurizing structures. num_top : int or None If an int, returns that number of most similar structures indicated by their indices in the original list. If None, returns all distances. labels : list of str or None If a list of str, the labels corresponding to the feature vectors, e.g., MPIDs. If None, defaults to the list indices. Returns ----------- dict[int,dict[str,np.ndarray]]], containing the index of the structure in `structures`, with a dict containing the `indices` of the top `num_top` most similar structures and their corresponding `distances`. """ wrapped = partial(self._get_closest_vectors, v=feature_vectors, num=num_top) nfv = feature_vectors.shape[0] with multiprocessing.Pool(num_procs) as pool: meta = pool.map(wrapped, range(nfv)) labels = labels or TypeAdapter(list[MaterialIdentifierType]).validate_python( range(nfv) ) return { labels[idx]: { "indices": [labels[idx] for idx in field[0]], "distances": field[1], } for idx, field in enumerate(meta) } def build_similarity_collection_from_structures( self, structures: dict[str, Structure], num_procs: int = 1, num_top: int = 100, ) -> list[SimilarityDoc]: """Build a collection of similarity documents. This defines the build pipeline for the MP similarity collection. Parameters ----------- structures : dict of str (e.g., MPID) to a corresponding structure. num_procs : int = 1 Number of parallel processes to run in featurizing structures. num_top : int or None If an int, returns that number of most similar structures indicated by their indices in the original list. If None, returns all distances. Returns ----------- A list of SimilarityDoc. """ identifiers = list(structures) ordered_structures = [structures[idx] for idx in identifiers] feature_vectors = self.featurize_structures( ordered_structures, num_procs=num_procs ) sim_meta = self.get_most_similar( feature_vectors, num_procs=num_procs, num_top=num_top, labels=identifiers ) return [ SimilarityDoc.from_structure( meta_structure=structures[idx], material_id=idx, feature_vector=feature_vectors[i], sim=[ SimilarityEntry( task_id=jdx, nelements=len(structures[jdx].composition.elements), dissimilarity=100.0 - meta["distances"][j], # type: ignore[operator] formula=structures[jdx].formula, ) for j, jdx in enumerate(meta["indices"]) ], ) for i, (idx, meta) in enumerate(sim_meta.items()) ] @staticmethod def get_vendi_score(feature_vectors: np.ndarray) -> float: """Get the Vendi score of a set of feature vectors. Uses the conventions described in arXiv:2210.02410 Describes the diveristy of a set of structures. Parameters ----------- feature_vectors : np.ndarray The feature vectors, such as those from SimilarityScorer._featurize_structure Each row should be a distinct feature vector. Returns ----------- float, the Vendi score. A Vendi score close to feature_vectors.shape[0] indicates high sample diversity, and a Vendi score close to 1 indicates low sample diversity. """ # Remove NaN rows non_nan_fv = feature_vectors[~np.any(np.isnan(feature_vectors), axis=1)] norms = np.einsum("ik,ik->i", non_nan_fv, non_nan_fv) ** (-0.5) norm_fv = np.einsum("ij,i->ij", non_nan_fv, norms) k_mat = np.einsum("ik,jk->ij", norm_fv, norm_fv) num_samp = non_nan_fv.shape[0] eigs = np.linalg.eigvalsh(k_mat) / num_samp log_eigs = np.zeros(num_samp) mask = eigs > 0.0 log_eigs[mask] = np.log(eigs[mask]) return np.exp(-np.einsum("i,i", eigs, log_eigs)) class CrystalNNSimilarity(SimilarityScorer): """Rank the similarity between structures using CrystalNN. Reference: 10.1039/C9RA07755C Parameters ----------- fingerprinter: Featurizer or None (default) A structural featurizer. If None, defaults to the featurizer used in the above reference. """ def __init__(self, fingerprinter: Featurizer | None = None) -> None: self.fingerprinter = fingerprinter or SiteStatsFingerprint() self.num_feature = len(self.fingerprinter.feature_labels) def _featurize_structure(self, structure: Structure) -> np.ndarray: """Featurize a single structure using CrystalNN. Parameters ----------- structure : Structure Returns ----------- np.ndarray """ try: return np.array(self.fingerprinter.featurize(structure)) except Exception: return np.nan * np.ones(self.num_feature) @staticmethod def _post_process_distance(distances: np.ndarray) -> np.ndarray: """Use exponential weighting of feature vector distances. Parameters ----------- distances : a matrix of structure feature vector distances. Returns ----------- np.ndarray """ return 100 * np.exp(-distances) class M3GNetSimilarity(SimilarityScorer): """Obtain structural similarity using an M3GNet formation energy model. Parameters ----------- model : str or Path Model for computing feature vectors of a structure. Defaults to "M3GNet-MP-2018.6.1-Eform" """ def __init__(self, model: str | Path = "M3GNet-MP-2018.6.1-Eform"): if matgl is None: raise ValueError("`pip install matgl` to use these features.") self._model_path = Path(model) self._model = None @staticmethod def _load_model(model_path: str | Path): return matgl.load_model(Path(model_path)).model @property def model(self): """Return the matgl model.""" if self._model_path and self._model is None: self._model = self._load_model(self._model_path) return self._model @staticmethod def _model_readout_from_structure( structure: Structure, model, ) -> np.ndarray: """Featurize a structure using a given matgl model.""" try: return ( model.predict_structure(structure, return_features=True)["readout"] .detach() .cpu() .numpy()[0] ) except Exception: return np.nan * np.ones(128) def _featurize_structure(self, structure: Structure) -> np.ndarray: """Featurize a single structure using M3GNet-Eform. Parameters ----------- structure : Structure Returns ----------- np.ndarray """ return self._model_readout_from_structure(structure, self.model) def _featurize_structures( self, structures: dict[int, Structure], model_path: Path, fvd: dict[int, np.ndarray], ): model = self._load_model(model_path) new_fvs = { idx: self._model_readout_from_structure(structure, model) for idx, structure in structures.items() } fvd.update(new_fvs) def featurize_structures( self, structures: list[Structure], num_procs: int = 1, ): """Featurize structures using the user-defined _featurize_structure. Rewritten because of pickling issues with torch objects. Parameters ----------- structures : list of Structure objects num_procs : int = 1 Number of parallel processes to run in featurizing structures. Returns ----------- np.ndarray : the feature vectors of the input structures. """ if num_procs > 1: manager = multiprocessing.Manager() fvd = manager.dict() procs = [] num_batches = int(np.ceil(len(structures) / num_procs)) for i in range(num_procs): idxs = (i * num_batches, min((i + 1) * num_batches, len(structures))) proc = multiprocessing.Process( target=self._featurize_structures, args=( {idx: structures[idx] for idx in range(*idxs)}, self._model_path, fvd, ), ) proc.start() procs.append(proc) for proc in procs: proc.join() _feature_vectors = [fvd[idx] for idx in range(len(structures))] else: _feature_vectors = [ self._featurize_structure(structure) for structure in structures ] return np.array(_feature_vectors) class SimilarityEntry(BaseModel): """ Find similar materials to a specified material based on crystal geometry. """ task_id: MaterialIdentifierType | None = Field( None, description="The Materials Project ID for the matched material. This comes in the form: mp-******.", ) nelements: int | None = Field( None, description="Number of elements in the matched material.", ) dissimilarity: float | None = Field( None, description="Dissimilarity measure for the matched material in %.", ) formula: str | None = Field( None, description="Formula of the matched material.", ) class SimilarityDoc(PropertyDoc): """ Model for a document containing structure similarity data """ property_name: str = "similarity" sim: list[SimilarityEntry] | None = Field( None, description="List containing similar structure data for a given material.", ) material_id: MaterialIdentifierType | None = Field( None, description="The Materials Project ID for the material. This comes in the form: mp-******", ) feature_vector: list[float] | None = Field( None, description="The feature / embedding vector of the structure." ) method: SimilarityMethod | None = Field( None, description="The method used to score similarity." ) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8252716 emmet_core-0.87.2/emmet/core/spectrum.py0000644000175100017510000000163115240717720017656 0ustar00runnerrunner"""Core definition of Spectrum document""" from pydantic import Field from emmet.core.base import EmmetBaseModel from emmet.core.structure import StructureMetadata from emmet.core.types.typing import DateTimeType, IdentifierType class SpectrumDoc(StructureMetadata, EmmetBaseModel): """ Base model definition for any spectra document. This should contain metadata on the structure the spectra pertains to """ spectrum_name: str task_id: IdentifierType | None = Field( None, description="The ID of the material, used as a universal reference across proeprty documents. " "This comes in an alphanumeric form.", ) last_updated: DateTimeType = Field( description="Timestamp for the most recent calculation update for this property.", ) warnings: list[str] | None = Field( None, description="Any warnings related to this property." ) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8252716 emmet_core-0.87.2/emmet/core/structure.py0000644000175100017510000002445615240717720020066 0ustar00runnerrunner"""Core definition of Structure and Molecule metadata.""" from __future__ import annotations from typing import TYPE_CHECKING, TypeVar from pydantic import BaseModel, Field from emmet.core.io.pymatgen import Composition, Molecule, Structure from emmet.core.symmetry import PointGroupData, SymmetryData from emmet.core.types.pymatgen_types.composition_adapter import CompositionType from emmet.core.types.pymatgen_types.element_adapter import ElementType from emmet.core.utils import get_graph_hash if TYPE_CHECKING: from typing_extensions import Self T = TypeVar("T", bound="StructureMetadata") S = TypeVar("S", bound="MoleculeMetadata") try: from openbabel import openbabel except Exception: openbabel = None class StructureMetadata(BaseModel): """Mix-in class for structure metadata.""" # Structure metadata nsites: int | None = Field( None, description="Total number of sites in the structure." ) elements: list[ElementType] | None = Field( None, description="List of elements in the material." ) nelements: int | None = Field(None, description="Number of elements.") composition: CompositionType | None = Field( None, description="Full composition for the material." ) composition_reduced: CompositionType | None = Field( None, title="Reduced Composition", description="Simplified representation of the composition.", ) formula_pretty: str | None = Field( None, title="Pretty Formula", description="Cleaned representation of the formula.", ) formula_anonymous: str | None = Field( None, title="Anonymous Formula", description="Anonymized representation of the formula.", ) chemsys: str | None = Field( None, title="Chemical System", description="Dash-separated string of elements in the material.", ) volume: float | None = Field( None, title="Volume", description="Total volume for this structure in ų.", ) density: float | None = Field( None, title="Density", description="Density in g/cm³." ) density_atomic: float | None = Field( None, title="Packing Density", description="The atomic packing density in ų/atom.", ) symmetry: SymmetryData | None = Field( None, description="Symmetry data for this material." ) @classmethod def from_composition( cls, composition: Composition, fields: list[str] | None = None, **kwargs, ) -> Self: fields = ( [ "elements", "nelements", "composition", "composition_reduced", "formula_pretty", "formula_anonymous", "chemsys", ] if fields is None else fields ) composition = composition.remove_charges() elsyms = sorted({e.symbol for e in composition.elements}) data = { "elements": elsyms, "nelements": len(elsyms), "composition": composition, "composition_reduced": composition.reduced_composition.remove_charges(), "formula_pretty": composition.reduced_formula, "formula_anonymous": composition.anonymized_formula, "chemsys": "-".join(elsyms), } return cls(**{k: v for k, v in data.items() if k in fields}, **kwargs) # type: ignore[arg-type] @classmethod def from_structure( cls, meta_structure: Structure, fields: list[str] | None = None, **kwargs, ) -> Self: fields = ( [ "nsites", "elements", "nelements", "composition", "composition_reduced", "formula_pretty", "formula_anonymous", "chemsys", "volume", "density", "density_atomic", "symmetry", ] if fields is None else fields ) comp = meta_structure.composition.remove_charges() elsyms = sorted({e.symbol for e in comp.elements}) symmetry = SymmetryData.from_structure(meta_structure) data = { "nsites": meta_structure.num_sites, "elements": elsyms, "nelements": len(elsyms), "composition": comp, "composition_reduced": comp.reduced_composition, "formula_pretty": comp.reduced_formula, "formula_anonymous": comp.anonymized_formula, "chemsys": "-".join(elsyms), "volume": meta_structure.volume, "density": meta_structure.density, "density_atomic": meta_structure.volume / meta_structure.num_sites, "symmetry": symmetry, } kwargs.update({k: v for k, v in data.items() if k in fields}) return cls(**kwargs) class MoleculeMetadata(BaseModel): """Mix-in class for molecule metadata.""" charge: int | None = Field(None, description="Charge of the molecule") spin_multiplicity: int | None = Field( None, description="Spin multiplicity of the molecule" ) natoms: int | None = Field( None, description="Total number of atoms in the molecule" ) elements: list[ElementType] | None = Field( None, description="List of elements in the molecule" ) nelements: int | None = Field(None, title="Number of Elements") nelectrons: int | None = Field( None, title="Number of electrons", description="The total number of electrons for the molecule", ) composition: CompositionType | None = Field( None, description="Full composition for the molecule" ) composition_reduced: CompositionType | None = Field( None, title="Reduced Composition", description="Simplified representation of the composition", ) formula_alphabetical: str | None = Field( None, title="Alphabetical Formula", description="Alphabetical molecular formula", ) formula_pretty: str | None = Field( None, title="Pretty Formula", description="Cleaned representation of the formula.", ) formula_anonymous: str | None = Field( None, title="Anonymous Formula", description="Anonymized representation of the formula", ) chemsys: str | None = Field( None, title="Chemical System", description="dash-delimited string of elements in the molecule", ) symmetry: PointGroupData | None = Field( None, description="Symmetry data for this molecule" ) species_hash: str | None = Field( None, description="Weisfeiler Lehman (WL) graph hash using the atom species as the graph " "node attribute.", ) coord_hash: str | None = Field( None, description="Weisfeiler Lehman (WL) graph hash using the atom coordinates as the graph " "node attribute.", ) @classmethod def from_composition( cls, comp: Composition, fields: list[str] | None = None, **kwargs, ) -> Self: """ Create a MoleculeMetadata model from a composition. Parameters ---------- comp : .Composition A pymatgen composition. fields : list of str or None Composition fields to include. **kwargs Keyword arguments that are passed to the model constructor. Returns ------- T A molecule metadata model. """ fields = ( [ "elements", "nelements", "composition", "composition_reduced", "formula_alphabetical", "formula_pretty", "formula_anonymous", "chemsys", ] if fields is None else fields ) elsyms = sorted({e.symbol for e in comp.elements}) data = { "elements": elsyms, "nelements": len(elsyms), "composition": comp, "composition_reduced": comp.reduced_composition, "formula_alphabetical": comp.alphabetical_formula, "formula_pretty": comp.reduced_formula, "formula_anonymous": comp.anonymized_formula, "chemsys": "-".join(elsyms), } return cls(**{k: v for k, v in data.items() if k in fields}, **kwargs) # type: ignore[arg-type] @classmethod def from_molecule( cls, meta_molecule: Molecule, fields: list[str] | None = None, **kwargs, ) -> Self: fields = ( [ "charge", "spin_multiplicity", "natoms", "elements", "nelements", "nelectrons", "composition", "composition_reduced", "formula_alphabetical", "formula_pretty", "formula_anonymous", "chemsys", "symmetry", "species_hash", "coord_hash", ] if fields is None else fields ) comp = meta_molecule.composition elsyms = sorted({e.symbol for e in comp.elements}) symmetry = PointGroupData.from_molecule(meta_molecule) data = { "charge": int(meta_molecule.charge), "spin_multiplicity": meta_molecule.spin_multiplicity, "natoms": len(meta_molecule), "elements": elsyms, "nelements": len(elsyms), "nelectrons": int(meta_molecule.nelectrons), "composition": comp, "composition_reduced": comp.reduced_composition, "formula_alphabetical": comp.alphabetical_formula, "formula_pretty": comp.reduced_formula, "formula_anonymous": comp.anonymized_formula, "chemsys": "-".join(elsyms), "symmetry": symmetry, } if openbabel: data["species_hash"] = get_graph_hash(meta_molecule, "specie") data["coord_hash"] = get_graph_hash(meta_molecule, "coords") return cls(**{k: v for k, v in data.items() if k in fields}, **kwargs) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8252716 emmet_core-0.87.2/emmet/core/structure_group.py0000644000175100017510000002513715240717720021277 0ustar00runnerrunnerfrom __future__ import annotations import logging import operator from itertools import groupby from typing import TYPE_CHECKING from pydantic import BaseModel, Field from emmet.core.io.pymatgen import ( ElementComparator, StructureMatcher, Composition, ComputedEntry, ComputedStructureEntry, SpacegroupAnalyzer, ) from emmet.core.mpid import AlphaID from emmet.core.types.typing import DateTimeType, IdentifierType if TYPE_CHECKING: from collections.abc import Iterable logger = logging.getLogger(__name__) def generic_groupby(list_in, comp=operator.eq) -> list[int]: """ Group a list of unsortable objects Args: list_in: A list of generic objects comp: (Default value = operator.eq) The comparator Returns: [int] list of labels for the input list """ list_out: list[int | None] = [None] * len(list_in) label_num = 0 for i1, ls1 in enumerate(list_out): if ls1 is not None: continue list_out[i1] = label_num for i2, ls2 in list(enumerate(list_out))[i1 + 1 :]: if comp(list_in[i1], list_in[i2]): if list_out[i2] is None: list_out[i2] = list_out[i1] else: list_out[i1] = list_out[i2] label_num -= 1 label_num += 1 return list_out # type: ignore def s_hash(el): return el.data["comp_delith"] class StructureGroupDoc(BaseModel): """ Group of structure """ group_id: str | None = Field( None, description="The combined material_id of the grouped document is given by the numerically smallest " "material_id, you can also append the followed by the ignored species at the end.", ) has_distinct_compositions: bool | None = Field( None, description="True if multiple compositions are present in the group." ) material_ids: list[str] | None = Field( None, description="A list of materials ids for all of the materials that were grouped together.", ) host_material_ids: list[str] | None = Field( None, description="Material id(s) that correspond(s) to the host structure(s), which has/have the lowest" "concentration of ignored specie.", ) insertion_material_ids: list[str] | None = Field( None, description="Material ids that correspond to the non-host structures identified in a given structure group.", ) framework_formula: str | None = Field( None, description="The chemical formula for the framework (the materials system without the ignored species).", ) ignored_specie: str | None = Field( None, description="Ignored atomic specie which is usually the working ion (ex: Li, Mg, or Ca).", ) chemsys: str | None = Field( None, description="The chemical system this group belongs to, if the atoms for the ignored species is " "present the chemsys will also include the ignored species.", ) last_updated: DateTimeType = Field( description="Timestamp when this document was built." ) @classmethod def from_grouped_entries( cls, entries: list[ComputedEntry | ComputedStructureEntry], ignored_specie: str, ) -> "StructureGroupDoc": """ " Assuming a list of entries are already grouped together, create a StructureGroupDoc Args: entries: A list of entries that is already grouped together. ignored_specie: The specie that is ignored during structure matching """ all_atoms = set() all_comps = set() for ient in entries: all_atoms |= set(ient.composition.as_dict().keys()) all_comps.add(ient.composition.reduced_formula) common_atoms = all_atoms - set([ignored_specie]) if len(common_atoms) == 0: framework_str = "ignored" else: comp_d = {k: entries[0].composition.as_dict()[k] for k in common_atoms} framework_comp = Composition.from_dict(comp_d) framework_str = framework_comp.reduced_formula ids = [ient.data["material_id"] for ient in entries] lowest_id = min(ids, key=_get_id_lexi) sub_script = "_".join([ignored_specie]) host_and_insertion_ids = StructureGroupDoc.get_host_and_insertion_ids( entries=entries, ignored_specie=ignored_specie ) fields = { "group_id": f"{lowest_id}_{sub_script}", "material_ids": ids, "host_material_ids": host_and_insertion_ids["host_ids"], "insertion_material_ids": host_and_insertion_ids["insertion_ids"], "framework_formula": framework_str, "ignored_specie": ignored_specie, "chemsys": "-".join(sorted(all_atoms | set([ignored_specie]))), "has_distinct_compositions": len(all_comps) > 1, } return cls(**fields) @classmethod def from_ungrouped_structure_entries( cls, entries: list[ComputedEntry | ComputedStructureEntry], ignored_specie: str, ltol: float = 0.2, stol: float = 0.3, angle_tol: float = 5.0, ) -> list["StructureGroupDoc"]: """ Create a list of StructureGroupDocs from a list of ungrouped entries. Args: entries: the list of ComputedStructureEntries to process. ignored_specie: the ignored specie for the structure matcher ltol: length tolerance for the structure matcher stol: site position tolerance for the structure matcher angle_tol: angel tolerance for the structure matcher """ results = [] sm = StructureMatcher( comparator=ElementComparator(), primitive_cell=True, ignored_species=[ignored_specie], ltol=ltol, stol=stol, angle_tol=angle_tol, ) # Add a framework field to each entry's data attribute for ient in entries: ient.data["framework"] = _get_framework( ient.composition.reduced_formula, ignored_specie ) # split into groups for each framework, must sort before grouping entries.sort(key=lambda x: x.data["framework"]) framework_groups = groupby(entries, key=lambda x: x.data["framework"]) cnt_ = 0 for framework, f_group in framework_groups: # if you only have ignored atoms put them into one "ignored" group f_group_l = list(f_group) if framework == "ignored": struct_group = cls.from_grouped_entries( f_group_l, ignored_specie=ignored_specie ) cnt_ += len(struct_group.material_ids) # type: ignore continue logger.debug( f"Performing structure matching for {framework} with {len(f_group_l)} documents." ) for g in group_entries_with_structure_matcher(f_group_l, sm): struct_group = cls.from_grouped_entries( g, ignored_specie=ignored_specie ) cnt_ += len(struct_group.material_ids) # type: ignore results.append(struct_group) if cnt_ != len(entries): raise RuntimeError( "The number of entries in all groups the end does not match the number of supplied entries documents." "Something is seriously wrong, please rebuild the entire database and see if the problem persists." ) return results @staticmethod def get_host_and_insertion_ids( entries: list[ComputedEntry | ComputedStructureEntry], ignored_specie: str, ) -> dict: host_and_insertion_ids = { "host_id": None, "host_ids": [], "host_entries": [], "insertion_ids": [], } # type:dict ignored_specie_min_fraction = min( [e.composition.get_atomic_fraction(ignored_specie) for e in entries] ) for e in entries: if ( e.composition.get_atomic_fraction(ignored_specie) == ignored_specie_min_fraction ): host_and_insertion_ids["host_entries"].append(e) host_and_insertion_ids["host_ids"].append(e.data["material_id"]) else: host_and_insertion_ids["insertion_ids"].append(e.data["material_id"]) host_and_insertion_ids["host_id"] = min( host_and_insertion_ids["host_entries"], key=lambda x: x.energy_per_atom ).data["material_id"] return host_and_insertion_ids def group_entries_with_structure_matcher( g, struct_matcher: StructureMatcher, working_ion: str | None = None, ) -> Iterable[list[ComputedStructureEntry | ComputedEntry]]: """ Group the entries together based on similarity of the primitive cells Args: g: a list of entries struct_matcher: the StructureMatcher object used to aggregate structures working_ion: the name of the working ion, if none use the first ignored species from the structure_matcher Returns: subgroups: subgroups that are grouped together based on structure similarity """ if working_ion is None: wion: str = struct_matcher.as_dict()["ignored_species"][0] # Sort the entries by symmetry and by working ion fraction def get_num_sym_ops(ent): sga = SpacegroupAnalyzer(ent.structure) return len(sga.get_space_group_operations()) g.sort(key=get_num_sym_ops, reverse=True) g.sort(key=lambda x: x.composition.get_atomic_fraction(wion)) labs = generic_groupby( g, comp=lambda x, y: struct_matcher.fit(x.structure, y.structure, symmetric=True), ) for ilab in set(labs): sub_g = [g[itr] for itr, jlab in enumerate(labs) if jlab == ilab] yield [el for el in sub_g] def _get_id_lexi(task_id: str | IdentifierType) -> tuple[str, int]: """Get a lexicographic representation for a task ID""" # matches "mp-1234" or "1234" followed by and optional "-(Alphanumeric)" try: return AlphaID(task_id).parts except ValueError: return (str(task_id), 0) def _get_framework(formula, ignored_specie) -> str: """ Return the reduced formula of the entry without any of the ignored species Return 'ignored' if the all the atoms are ignored """ dd_ = Composition(formula).as_dict() if dd_.keys() == set([ignored_specie]): return "ignored" if ignored_specie in dd_: dd_.pop(ignored_specie) return Composition.from_dict(dd_).reduced_formula ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8252716 emmet_core-0.87.2/emmet/core/substrates.py0000644000175100017510000000226115240717720020213 0ustar00runnerrunnerfrom pydantic import BaseModel, Field class SubstratesDoc(BaseModel): """ Possible growth substrates for a given material. """ sub_form: str | None = Field( None, description="Reduced formula of the substrate.", ) sub_id: str | None = Field( None, description="Materials Project ID of the substrate material. This comes in the form: mp-******.", ) film_orient: str | None = Field( None, description="Surface orientation of the film material.", ) area: float | None = Field( None, description="Minimum coincident interface area in Ų.", ) energy: float | None = Field( None, description="Elastic energy in meV.", ) film_id: str | None = Field( None, description="The Materials Project ID of the film material. This comes in the form: mp-******.", ) norients: int | None = Field( None, description="Number of possible surface orientations for the substrate.", alias="_norients", ) orient: str | None = Field( None, description="Surface orientation of the substrate material.", ) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8252716 emmet_core-0.87.2/emmet/core/summary.py0000644000175100017510000003336115240717720017516 0ustar00runnerrunnerfrom __future__ import annotations from collections import ChainMap from itertools import chain from typing import TYPE_CHECKING from pydantic import BaseModel, Field, PrivateAttr from pydantic.json_schema import SkipJsonSchema from emmet.core.electronic_structure import BandstructureData, DosData from emmet.core.material import PropertyOrigin from emmet.core.material_property import PropertyDoc from emmet.core.thermo import DecompositionProduct from emmet.core.types.enums import ValueEnum, XasEdge, XasType from emmet.core.types.pymatgen_types.element_adapter import ElementType from emmet.core.types.pymatgen_types.structure_adapter import StructureType from emmet.core.types.typing import IdentifierType if TYPE_CHECKING: from typing_extensions import Self class HasProps(ValueEnum): """ Enum of possible has_props values. """ materials = "materials" thermo = "thermo" xas = "xas" grain_boundaries = "grain_boundaries" electronic_structure = "electronic_structure" bandstructure = "bandstructure" dos = "dos" magnetism = "magnetism" elasticity = "elasticity" dielectric = "dielectric" piezoelectric = "piezoelectric" surface_properties = "surface_properties" oxi_states = "oxi_states" provenance = "provenance" charge_density = "charge_density" eos = "eos" phonon = "phonon" absorption = "absorption" insertion_electrodes = "insertion_electrodes" substrates = "substrates" chemenv = "chemenv" class PropModel(BaseModel): """Check for model initialization outside of defaults.""" _prop: str = PrivateAttr("materials") prop_origins: SkipJsonSchema[list[PropertyOrigin | None]] = Field( [], description="Optionally include property origins to fold into summary doc origins", exclude=True, ) @property def _has_props(self) -> bool: _defaults = {field: anno.default for field, anno in self.model_fields.items()} return any( [getattr(self, field) != default for field, default in _defaults.items()] ) @property def name(self) -> HasProps: return HasProps(self._prop) class MaterialsSummary(PropertyDoc, PropModel): property_name: str = "summary" task_ids: list[IdentifierType] = Field( [], title="Calculation IDs", description="List of Calculations IDs associated with this material.", ) structure: StructureType = Field( ..., description="The lowest energy structure for this material." ) class ThermoSummary(PropModel): _prop: str = PrivateAttr("thermo") uncorrected_energy_per_atom: float | None = Field( None, description="The total DFT energy of this material per atom in eV/atom.", ) energy_per_atom: float | None = Field( None, description="The total corrected DFT energy of this material per atom in eV/atom.", ) formation_energy_per_atom: float | None = Field( None, description="The formation energy per atom in eV/atom.", ) energy_above_hull: float | None = Field( None, description="The energy above the hull in eV/Atom.", ) is_stable: bool = Field( False, description="Flag for whether this material is on the hull and therefore stable.", ) equilibrium_reaction_energy_per_atom: float | None = Field( None, description="The reaction energy of a stable entry from the neighboring equilibrium stable materials in eV." " Also known as the inverse distance to hull.", ) decomposes_to: list[DecompositionProduct] | None = Field( None, description="List of decomposition data for this material. Only valid for metastable or unstable material.", ) class XASSearchData(PropModel): """ Fields in XAS sub docs in summary """ edge: XasEdge | None = Field( None, title="Absorption Edge", description="The interaction edge for XAS" ) absorbing_element: ElementType | None = Field( None, description="Absorbing element." ) spectrum_type: XasType | None = Field(None, description="Type of XAS spectrum.") class XASSummary(PropModel): _prop: str = PrivateAttr("xas") xas: list[XASSearchData] | None = Field( None, description="List of xas documents.", ) class GBSearchData(BaseModel): """ Fields in grain boundary sub docs in summary """ sigma: int | None = Field(None, description="Sigma value of the boundary.") type: str | None = Field(None, description="Grain boundary type.") gb_energy: float | None = Field(None, description="Grain boundary energy in J/m^2.") rotation_angle: float | None = Field(None, description="Rotation angle in degrees.") class GBSummary(PropModel): _prop: str = PrivateAttr("grain_boundaries") grain_boundaries: list[GBSearchData] | None = Field( None, description="List of grain boundary documents." ) class ElectronicStructureSummary(PropModel): _prop: str = PrivateAttr("electronic_structure") band_gap: float | None = Field(None, description="Band gap energy in eV.") cbm: float | None = Field(None, description="Conduction band minimum data.") vbm: float | None = Field(None, description="Valence band maximum data.") efermi: float | None = Field(None, description="Fermi energy in eV.") is_gap_direct: bool | None = Field( None, description="Whether the band gap is direct." ) is_metal: bool | None = Field(None, description="Whether the material is a metal.") class BandstructureSummary(PropModel): _prop: str = PrivateAttr("bandstructure") bandstructure: BandstructureData | None = Field( None, description="Band structure data for the material." ) class DosSummary(PropModel): _prop: str = PrivateAttr("dos") dos: DosData | None = Field( None, description="Density of states data for the material." ) dos_energy_up: float | None = Field(None, description="Spin-up DOS band gap in eV.") dos_energy_down: float | None = Field( None, description="Spin-down DOS band gap in eV." ) class MagnetismSummary(PropModel): _prop: str = PrivateAttr("magnetism") is_magnetic: bool | None = Field( None, description="Whether the material is magnetic." ) ordering: str | None = Field(None, description="Type of magnetic ordering.") total_magnetization: float | None = Field( None, description="Total magnetization in μB." ) total_magnetization_normalized_vol: float | None = Field( None, description="Total magnetization normalized by volume in μB/ų." ) total_magnetization_normalized_formula_units: float | None = Field( None, description="Total magnetization normalized by formula unit in μB/f.u. ." ) num_magnetic_sites: int | None = Field( None, description="The number of magnetic sites." ) num_unique_magnetic_sites: int | None = Field( None, description="The number of unique magnetic sites." ) types_of_magnetic_species: list[ElementType] | None = Field( None, description="Magnetic species elements." ) class ElasticitySummary(PropModel): _prop: str = PrivateAttr("elasticity") # k_voigt: float | None = Field(None, description="Voigt average of the bulk modulus.") # k_reuss: float | None = Field(None, description="Reuss average of the bulk modulus in GPa.") # k_vrh: float | None = Field(None, description="Voigt-Reuss-Hill average of the bulk modulus in GPa.") # g_voigt: float | None = Field(None, description="Voigt average of the shear modulus in GPa.") # g_reuss: float | None = Field(None, description="Reuss average of the shear modulus in GPa.") # g_vrh: float | None = Field(None, description="Voigt-Reuss-Hill average of the shear modulus in GPa.") bulk_modulus: dict[str, float] | None = Field( None, description="Voigt, Reuss, and Voigt-Reuss-Hill averages of the bulk modulus in GPa.", ) shear_modulus: dict[str, float] | None = Field( None, description="Voigt, Reuss, and Voigt-Reuss-Hill averages of the shear modulus in GPa.", ) universal_anisotropy: float | None = Field(None, description="Elastic anisotropy.") homogeneous_poisson: float | None = Field(None, description="Poisson's ratio.") class DielectricSummary(PropModel): _prop: str = PrivateAttr("dielectric") e_total: float | None = Field(None, description="Total dielectric constant.") e_ionic: float | None = Field( None, description="Ionic contribution to dielectric constant." ) e_electronic: float | None = Field( None, description="Electronic contribution to dielectric constant." ) n: float | None = Field(None, description="Refractive index.") class PiezoelectricSummary(PropModel): _prop: str = PrivateAttr("piezoelectric") e_ij_max: float | None = Field(None, description="Piezoelectric modulus.") class SurfacesSummary(PropModel): _prop: str = PrivateAttr("surface_properties") weighted_surface_energy_EV_PER_ANG2: float | None = Field( None, description="Weighted surface energy in eV/Ų." ) weighted_surface_energy: float | None = Field( None, description="Weighted surface energy in J/m²." ) weighted_work_function: float | None = Field( None, description="Weighted work function in eV." ) surface_anisotropy: float | None = Field( None, description="Surface energy anisotropy." ) shape_factor: float | None = Field(None, description="Shape factor.") has_reconstructed: bool | None = Field( None, description="Whether the material has any reconstructed surfaces." ) class OxiStatesSummary(PropModel): _prop: str = PrivateAttr("oxi_states") possible_species: list[str] | None = Field( None, description="Possible charged species in this material." ) class ProvenanceSummary(PropModel): _prop: str = PrivateAttr("provenance") theoretical: bool = Field(True, description="Whether the material is theoretical.") database_IDs: dict[str, list[str]] | None = Field( None, description="External database IDs corresponding to this material." ) class PhononSummary(PropModel): _prop: str = PrivateAttr("phonon") phonon_IDs: dict[str, list[IdentifierType]] | None = Field( None, description="Identfiers for phonon documents associated with this material.", ) # ----------------------------------------------------------------------------- # Shims for populating has_props for properties that do not add # values to SummaryDoc # ----------------------------------------------------------------------------- class ChargeDensityData(PropModel): _prop: str = PrivateAttr("charge_density") exists: bool = False class EosData(PropModel): _prop: str = PrivateAttr("eos") exists: bool = False class AbsorptionData(PropModel): _prop: str = PrivateAttr("absorption") exists: bool = False class ElectrodesData(PropModel): _prop: str = PrivateAttr("insertion_electrodes") exists: bool = False class SubstratesData(PropModel): _prop: str = PrivateAttr("substrates") exists: bool = False class ChemenvData(PropModel): _prop: str = PrivateAttr("chemenv") exists: bool = False class SummaryDoc( MaterialsSummary, ThermoSummary, XASSummary, GBSummary, ElectronicStructureSummary, BandstructureSummary, DosSummary, MagnetismSummary, ElasticitySummary, DielectricSummary, PiezoelectricSummary, SurfacesSummary, OxiStatesSummary, ProvenanceSummary, PhononSummary, ): """ Summary information about materials and their properties, useful for materials screening studies and searching. """ has_props: dict[HasProps, bool] | None = Field( None, description="List of properties that are available for a given material.", ) @classmethod def from_docs( cls, property_summary_docs: list[ MaterialsSummary | ThermoSummary | XASSummary | GBSummary | ElectronicStructureSummary | BandstructureSummary | DosSummary | MagnetismSummary | ElasticitySummary | DielectricSummary | PiezoelectricSummary | SurfacesSummary | OxiStatesSummary | ProvenanceSummary | PhononSummary ], property_shim_docs: list[ ChargeDensityData | EosData | AbsorptionData | ElectrodesData | SubstratesData | ChemenvData ], **kwargs, ) -> Self: """ Args: property_summary_docs: List of propery documents with data to be added to SummaryDoc. property_shim_docs: List of property shim documents strictly used to populate has_props. """ # initialize all has_props variants to False, overwrite according to what caller provides has_props = {prop: False for prop in HasProps.__members__.values()} for property_doc in chain(property_summary_docs, property_shim_docs): has_props[property_doc.name] = property_doc._has_props # fold property origins prop_origins = chain.from_iterable( ( doc.prop_origins for doc in chain(property_summary_docs, property_shim_docs) ) ) aggregate_struct = { **ChainMap(*[doc.model_dump() for doc in property_summary_docs]) } aggregate_struct["origins"] = list( chain(aggregate_struct["origins"], prop_origins) ) return SummaryDoc( has_props=has_props, **{**aggregate_struct, **kwargs}, ) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8252716 emmet_core-0.87.2/emmet/core/surface_properties.py0000644000175100017510000000617715240717720021732 0ustar00runnerrunner"""Define schemas for surfaces and heterostructures.""" from __future__ import annotations from typing import TYPE_CHECKING from pydantic import BaseModel, Field, field_validator from emmet.core.io.pymatgen import Structure from emmet.core.types.pymatgen_types.structure_adapter import StructureType from emmet.core.types.typing import MaterialIdentifierType if TYPE_CHECKING: from typing import Any class SurfaceEntry(BaseModel): """ Surface energies, miller indicies, ... """ miller_index: list[int] | None = Field( None, description="Miller index of surface.", ) surface_energy_EV_PER_ANG2: float | None = Field( None, description="Surface energy in eV/Ų.", ) surface_energy: float | None = Field( None, description="Surface energy in J/m².", ) is_reconstructed: bool | None = Field( None, description="Whether it is a reconstructed surface.", ) structure: StructureType | None = Field( None, description="Slab structure.", ) work_function: float | None = Field( None, description="Work function in eV.", ) efermi: float | None = Field( None, description="Fermi energy in eV.", ) area_fraction: float | None = Field( None, description="Area fraction.", ) has_wulff: bool | None = Field( None, description="Whether the surface has wulff entry.", ) @field_validator("structure", mode="before") def get_structure_from_cif(cls, v: Any) -> Structure | None: """Transform legacy CIF data to pymatgen Structure.""" if isinstance(v, str): return Structure.from_str(v, fmt="cif") return v class SurfacePropDoc(BaseModel): """ Model for a document containing surface properties data """ surfaces: list[SurfaceEntry] | None = Field( None, description="List of individual surface data.", ) weighted_surface_energy_EV_PER_ANG2: float | None = Field( None, description="Weighted surface energy in eV/Ų", ) weighted_surface_energy: float | None = Field( None, description="Weighted surface energy in J/m²", ) surface_anisotropy: float | None = Field( None, description="Surface energy anisotropy.", ) pretty_formula: str | None = Field( None, description="Reduced Formula of the material.", ) shape_factor: float | None = Field( None, description="Shape factor.", ) weighted_work_function: float | None = Field( None, description="Weighted work function in eV.", ) has_reconstructed: bool | None = Field( None, description="Whether the entry has any reconstructed surfaces.", ) material_id: MaterialIdentifierType | None = Field( None, description="The Materials Project ID of the material. This comes in the form: mp-******.", ) structure: StructureType | None = Field( None, description="The conventional crystal structure of the material.", ) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8252716 emmet_core-0.87.2/emmet/core/symmetry.py0000644000175100017510000001720315240717720017707 0ustar00runnerrunnerfrom __future__ import annotations from math import isinf from typing import TYPE_CHECKING from pydantic import BaseModel, Field, field_validator from spglib import __version__ as spglib_version from emmet.core.io.pymatgen import ( Structure, Molecule, PointGroupAnalyzer, SpacegroupAnalyzer, SymmetryUndeterminedError, SYMM_DATA, ) from emmet.core.settings import EmmetSettings from emmet.core.types.enums import IgnoreCaseEnum if TYPE_CHECKING: from typing import Any SETTINGS = EmmetSettings() class CrystalSystem(IgnoreCaseEnum): """ The crystal system of the lattice """ tri = "Triclinic" mono = "Monoclinic" ortho = "Orthorhombic" tet = "Tetragonal" trig = "Trigonal" hex_ = "Hexagonal" cubic = "Cubic" def get_crystal_system_from_international_number(sgn: int) -> CrystalSystem: """Identify the crystal system from the international space group number. Parameters ----------- sgn (int) : The international space group number Returns ----------- CrystalSystem """ try: return next( CrystalSystem[family] # type: ignore[misc] for family, sgn_range in { "tri": (1, 3), "mono": (3, 16), "ortho": (16, 75), "tet": (75, 143), "trig": (143, 168), "hex_": (168, 195), "cubic": (195, 231), }.items() if sgn_range[0] <= sgn < sgn_range[1] ) except StopIteration: raise ValueError(f"Invalid space group number {sgn}.") def _get_space_group_symbol_to_number_mapping() -> dict[str, int]: """Return a dict mapping space group symbol to its international number.""" sg_map = { sgs: entry["int_number"] for sgs, entry in SYMM_DATA["space_group_encoding"].items() } # Add in duplicate abbreviated space group symbols # These abbreviated symbols are used by spglib for abbr, full in SYMM_DATA["abbreviated_spacegroup_symbols"].items(): sg_map[abbr] = sg_map[full] return sg_map class PointGroupData(BaseModel): """ Defines symmetry for a molecule document """ point_group: str | None = Field( None, title="Point Group Symbol", description="The point group for the lattice" ) rotation_number: int | None = Field( None, title="Rotational Symmetry Number", description="Rotational symmetry number for the molecule", ) linear: bool | None = Field( None, title="Molecule Linearity", description="Is the molecule linear?" ) tolerance: float | None = Field( None, title="Point Group Analyzer Tolerance", description="Distance tolerance to consider sites as symmetrically equivalent.", ) eigen_tolerance: float | None = Field( None, title="Interia Tensor Eigenvalue Tolerance", description="Tolerance to compare eigen values of the inertia tensor.", ) matrix_tolerance: float | None = Field( None, title="Symmetry Operation Matrix Element Tolerance", description="Tolerance used to generate the full set of symmetry operations of the point group.", ) @field_validator("rotation_number", mode="before") def remove_inf(cls, v: Any) -> int | None: if v is not None: v = None if isinf(float(v)) else int(v) return v @classmethod def from_molecule(cls, molecule: Molecule) -> "PointGroupData": tol = SETTINGS.PGATOL eigentol = SETTINGS.PGAEIGENTOL matrixtol = SETTINGS.PGAMATRIXTOL pga = PointGroupAnalyzer( molecule, tolerance=tol, eigen_tolerance=eigentol, matrix_tolerance=matrixtol, ) symmetry: dict[str, Any] = { "tolerance": tol, "eigen_tolerance": eigentol, "matrix_tolerance": matrixtol, "point_group": pga.sch_symbol, } rotational_symmetry_numbers = { 1: ["C1", "Cs", "Ci", "C*v", "S2"], 2: ["C2", "C2h", "C2v", "S4", "D*h"], 3: ["C3", "C3h", "C3v", "S6"], 4: ["C4v", "D4h", "D4d", "D2", "D2h", "D2d"], 5: ["C5v", "Ih"], 6: ["D3", "D3h", "D3d"], 10: ["D5h", "D5d"], 12: ["T", "Td", "Th", "D6h"], 14: ["D7h"], 16: ["D8h"], 24: ["Oh"], float("inf"): ["Kh"], } try: symmetry["rotation_number"] = next( rot_num for rot_num, point_groups in rotational_symmetry_numbers.items() if symmetry["point_group"] in point_groups ) if isinf(symmetry["rotation_number"]): symmetry["rotation_number"] = None except Exception: symmetry["rotation_number"] = None symmetry["linear"] = symmetry["point_group"] in ["C*v", "D*h"] return PointGroupData(**symmetry) class SymmetryData(BaseModel): """ Defines a symmetry data set for materials documents """ crystal_system: CrystalSystem | None = Field( None, title="Crystal System", description="The crystal system for this lattice." ) symbol: str | None = Field( None, title="Space Group Symbol", description="The spacegroup symbol for the lattice.", ) hall: str | None = Field( None, title="Hall Symbol", description="Hall symbol for the lattice", ) number: int | None = Field( None, title="Space Group Number", description="The spacegroup number for the lattice.", ) point_group: str | None = Field( None, title="Point Group Symbol", description="The point group for the lattice." ) symprec: float | None = Field( None, title="Symmetry Finding Precision", description="The precision provided to spglib to determine the symmetry of this structure.", ) angle_tolerance: float | None = Field( None, title="Angle Tolerance", description="Angle tolerance provided to spglib to determine the symmetry of this structure.", ) version: str | None = Field(None, title="spglib version") @classmethod def from_structure(cls, structure: Structure) -> "SymmetryData": symmetry: dict[str, Any] = { "symbol": None, "number": None, "point_group": None, "crystal_system": None, "hall": None, "version": spglib_version, "symprec": SETTINGS.SYMPREC, "angle_tolerance": SETTINGS.ANGLE_TOL, } try: sg = SpacegroupAnalyzer( structure, symprec=symmetry["symprec"], angle_tolerance=symmetry["angle_tolerance"], ) except SymmetryUndeterminedError: try: symmetry["symprec"] = 1e-3 symmetry["angle_tolerance"] = 1 sg = SpacegroupAnalyzer( structure, symprec=symmetry["symprec"], angle_tolerance=symmetry["angle_tolerance"], ) except SymmetryUndeterminedError: return SymmetryData(**symmetry) symmetry.update( { "symbol": sg.get_space_group_symbol(), "number": sg.get_space_group_number(), "point_group": sg.get_point_group_symbol(), "crystal_system": CrystalSystem(sg.get_crystal_system().title()), "hall": sg.get_hall(), } ) return SymmetryData(**symmetry) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011028.9767923 emmet_core-0.87.2/emmet/core/synthesis/0000755000175100017510000000000015240717725017477 5ustar00runnerrunner././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8252716 emmet_core-0.87.2/emmet/core/synthesis/__init__.py0000644000175100017510000000062015240717720021601 0ustar00runnerrunnerfrom emmet.core.synthesis.core import ( SynthesisRecipe, SynthesisTypeEnum, SynthesisSearchResultModel, ) from emmet.core.synthesis.materials import ( Component, ExtractedMaterial, ) from emmet.core.synthesis.operations import ( Value, Conditions, Operation, OperationTypeEnum, ) from emmet.core.synthesis.reaction import ( FormulaPart, ReactionFormula, ) ././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1787011023.826609 emmet_core-0.87.2/emmet/core/synthesis/core.py0000644000175100017510000000524115240717720020776 0ustar00runnerrunnerfrom pydantic import BaseModel, Field from emmet.core.synthesis.materials import ExtractedMaterial from emmet.core.synthesis.operations import Operation from emmet.core.synthesis.reaction import ReactionFormula from emmet.core.types.enums import ValueEnum class SynthesisTypeEnum(str, ValueEnum): solid_state = "solid-state" sol_gel = "sol-gel" class SynthesisRecipe(BaseModel): """ Model for a document containing synthesis description data """ # Basic facts about this recipe: doi: str | None = Field(None, description="DOI of the journal article.") paragraph_string: str | None = Field( "", description="The paragraph from which this recipe is extracted." ) synthesis_type: SynthesisTypeEnum | None = Field( None, description="Type of the synthesis recipe." ) # Reaction related information: reaction_string: str | None = Field( None, description="String representation of this recipe." ) reaction: ReactionFormula | None = Field( None, description="The balanced reaction formula." ) target: ExtractedMaterial | None = Field(None, description="The target material.") targets_formula: list[str] | None = Field( None, description="List of synthesized target material compositions." ) precursors_formula: list[str] | None = Field( None, description="List of precursor material compositions." ) targets_formula_s: list[str] | None = Field( None, description="List of synthesized target material compositions, as strings.", ) precursors_formula_s: list[str] | None = Field( None, description="List of precursor material compositions, as strings." ) precursors: list[ExtractedMaterial] | None = Field( None, description="List of precursor materials." ) operations: list[Operation] | None = Field( None, description="List of operations used to synthesize this recipe." ) class AtlasSearchText(BaseModel): """Schematize MongoDB Atlas search text match.""" value: str | None = None type: str | None = None class AtlasSearchHighlight(BaseModel): """Schematize MongoDB Atlas search highlight match.""" score: float | None = None path: str | None = None texts: list[AtlasSearchText] | None = None class SynthesisSearchResultModel(SynthesisRecipe): """ Model for a document containing synthesis recipes data and additional keyword search results """ search_score: float | None = Field( None, description="Search score.", ) highlights: list[AtlasSearchHighlight] | None = Field( None, description="Search highlights.", ) ././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1787011023.826609 emmet_core-0.87.2/emmet/core/synthesis/materials.py0000644000175100017510000000346715240717720022037 0ustar00runnerrunnerfrom pydantic import BaseModel, Field __all__ = ["Component", "ExtractedMaterial"] class Component(BaseModel): formula: str = Field(..., description="Formula of this component.") amount: str = Field(..., description="Amount of this component.") elements: dict[str, str] = Field( ..., description="Amount of each chemical elements in this component." ) class Values(BaseModel): values: list[float] | None = Field(None, description="list of values.") min_value: float | None = Field(None, description="Minimal value.") max_value: float | None = Field(None, description="Maximal value.") class ExtractedMaterial(BaseModel): """ Model for a material extracted from the literature """ material_string: str = Field( ..., description="String of the material as written in paper." ) material_formula: str = Field( ..., description="Normalized formula of the material." ) material_name: str | None = Field(None, description="English name of the material.") phase: str | None = Field( None, description="Phase description of material, such as anatase." ) is_acronym: bool | None = Field( None, description="Whether the material is an acronym, such as LMO for LiMn2O4." ) composition: list[Component] = Field( ..., description="List of components in this material." ) amounts_vars: dict[str, Values] = Field( {}, description="Amount variables (formula subscripts)." ) elements_vars: dict[str, list[str]] = Field( {}, description="Chemical element variables" ) additives: list[str] = Field([], description="list of additives, dopants, etc.") oxygen_deficiency: str | None = Field( None, description="Symbol indicating whether the materials is oxygen deficient." ) ././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1787011023.826609 emmet_core-0.87.2/emmet/core/synthesis/operations.py0000644000175100017510000000306015240717720022226 0ustar00runnerrunnerfrom pydantic import BaseModel, Field from emmet.core.types.enums import ValueEnum class Value(BaseModel): min_value: float | None = Field(None, description="Minimal value.") max_value: float | None = Field(None, description="Maximal value.") values: list[float] = Field([], description="Enumerated values in the literature.") units: str = Field(..., description="Unit of this value.") class Conditions(BaseModel): heating_temperature: list[Value] | None = Field( None, description="Heating temperatures." ) heating_time: list[Value] | None = Field(None, description="Heating times.") heating_atmosphere: list[str] | None = Field( None, description="List of heating atmospheres." ) mixing_device: str | None = Field( None, description="Mixing device, if this operation is MIXING." ) mixing_media: str | None = Field( None, description="Mixing media, if this operation is MIXING." ) class OperationTypeEnum(str, ValueEnum): starting = "StartingSynthesis" mixing = "MixingOperation" shaping = "ShapingOperation" drying = "DryingOperation" heating = "HeatingOperation" quenching = "QuenchingOperation" class Operation(BaseModel): type: OperationTypeEnum = Field( ..., description="Type of the operation as classified by the pipeline." ) token: str = Field( ..., description="Token (word) of the operation as written in paper." ) conditions: Conditions = Field( ..., description="The conditions linked to this operation." ) ././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1787011023.826609 emmet_core-0.87.2/emmet/core/synthesis/reaction.py0000644000175100017510000000154515240717720021655 0ustar00runnerrunnerfrom pydantic import BaseModel, Field __all__ = [ "FormulaPart", "ReactionFormula", ] class FormulaPart(BaseModel): amount: str = Field("1", description="Amount of the compound in a formula.") material: str = Field( ..., description="The compound that participates in a reaction." ) class ReactionFormula(BaseModel): """ Model for a balanced reaction """ left_side: list[FormulaPart] = Field( ..., description="List of materials and their amounts at the left side." ) right_side: list[FormulaPart] = Field( ..., description="List of materials and their amounts at the right side." ) # For example, BaCO3 + MO2 == BaMO3, element_substitution = {"M": "Ti"} element_substitution: dict[str, str] = Field( {}, description="Dictionary that contains elemental substitutions" ) ././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1787011023.826609 emmet_core-0.87.2/emmet/core/task.py0000644000175100017510000000210115240717720016747 0ustar00runnerrunner"""Core definition of a Task Document which represents a calculation from some program""" from pydantic import Field from emmet.core.base import EmmetBaseModel from emmet.core.mpid import MPculeID from emmet.core.types.typing import IdentifierType, DateTimeType, NullableDateTimeType class BaseTaskDocument(EmmetBaseModel): """ Definition of base Task Document """ calc_code: str = Field(description="The calculation code used to compute this task") version: str | None = Field(None, description="The version of the calculation code") dir_name: str | None = Field(None, description="The directory for this task") task_id: IdentifierType | MPculeID | None = Field( None, description="the Task ID For this document" ) completed: bool = Field(False, description="Whether this calcuation completed") completed_at: NullableDateTimeType = Field( description="Timestamp for when this task was completed" ) last_updated: DateTimeType = Field( description="Timestamp for when this task document was last updated", ) ././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1787011023.826609 emmet_core-0.87.2/emmet/core/tasks.py0000644000175100017510000012117515240717720017147 0ustar00runnerrunner"""Define core schemas for VASP calculations.""" from __future__ import annotations import logging import re import warnings from pathlib import Path from typing import TYPE_CHECKING, Annotated, Any import numpy as np from monty.json import MontyDecoder from monty.serialization import loadfn from pydantic import ( BaseModel, BeforeValidator, Field, WrapSerializer, field_validator, model_validator, ) from emmet.core.io.pymatgen import ( ComputedEntry, ComputedStructureEntry, Incar, Kpoints, Poscar, ) from emmet.core.io.pymatgen import Trajectory as PmgTrajectory from emmet.core.io.pymatgen import oxide_type from emmet.core.structure import StructureMetadata from emmet.core.trajectory import RelaxTrajectory, Trajectory from emmet.core.types.enums import TaskState, VaspObject from emmet.core.types.pymatgen_types.computed_entries_adapter import ( ComputedEntryType, ComputedStructureEntryType, ) from emmet.core.types.pymatgen_types.structure_adapter import StructureType from emmet.core.types.typing import ( DateTimeType, IdentifierType, JsonDictType, JsonListType, NullableDateTimeType, _deser_json_like, _ser_json_like, ) from emmet.core.utils import type_override, utcnow from emmet.core.vasp.calc_types import ( CalcType, RunType, TaskType, calc_type, run_type, task_type, ) from emmet.core.vasp.calculation import ( Calculation, CalculationInput, CoreCalculationOutput, PotcarSpec, RunStatistics, get_trajectories_from_calculations, ) from emmet.core.vasp.utils import TASK_NAMES, discover_and_sort_vasp_files if TYPE_CHECKING: from collections.abc import Sequence from typing_extensions import Self monty_decoder = MontyDecoder() logger = logging.getLogger(__name__) _VOLUMETRIC_FILES = ("CHGCAR", "LOCPOT", "AECCAR0", "AECCAR1", "AECCAR2") class OrigInputs(CalculationInput): """Maintained for backward compatibility - deprecated.""" def model_post_init(self, __context): super().model_post_init(__context) warnings.warn( f"The `{self.__class__.__name__}` class has been marked " "for deprecation to ensure parity of the inputs field " "in `TaskDoc`. Please transition to using `CalculationInput`, " f"which is fully backwards compatible with `{self.__class__.__name__}`.", stacklevel=2, ) class InputDoc(OrigInputs): """Maintained for backward compatibility - deprecated.""" class OutputDoc(BaseModel): structure: StructureType | None = Field( None, title="Output Structure", description="Output Structure from the VASP calculation.", ) density: float | None = Field(None, description="Density of in units of g/cc.") energy: float | None = Field(None, description="Total Energy in units of eV.") forces: list[list[float]] | None = Field( None, description="The force on each atom in units of eV/A." ) stress: list[list[float]] | None = Field( None, description="The stress on the cell in units of kB." ) energy_per_atom: float | None = Field( None, description="The final DFT energy per atom for the last calculation" ) bandgap: float | None = Field( None, description="The DFT bandgap for the last calculation" ) @model_validator(mode="before") def set_density_from_structure(cls, values): # Validator to automatically set density from structure if not already # specified. This might happen when importing an older atomate2-format # TaskDocument. if not values.get("density", None): if isinstance(values["structure"], dict): values["density"] = values["structure"].get("density", None) else: values["density"] = values["structure"].density return values @classmethod def from_vasp_calc_doc( cls, calc_doc: Calculation, trajectory: RelaxTrajectory | PmgTrajectory | None = None, ) -> "OutputDoc": """ Create a summary of VASP calculation outputs from a VASP calculation document. This will first look for ionic steps in the calculation document. If found, will use it and ignore the trajectory. If not, will get ionic steps from the trajectory. Parameters ---------- calc_doc A VASP calculation document. trajectory An emmet-core Trajectory. Returns ------- OutputDoc The calculation output summary. """ forces = None stress = None if calc_doc.output.ionic_steps: forces = calc_doc.output.ionic_steps[-1].forces stress = calc_doc.output.ionic_steps[-1].stress elif trajectory: if isinstance(trajectory, PmgTrajectory): forces = trajectory.frame_properties[-1]["forces"] # type: ignore[index] stress = trajectory.frame_properties[-1]["stress"] # type: ignore[index] else: forces = trajectory.forces[-1] stress = trajectory.stress[-1] else: raise RuntimeError("Unable to find ionic steps.") return cls( structure=calc_doc.output.structure, density=calc_doc.output.structure.density, energy=calc_doc.output.energy, energy_per_atom=calc_doc.output.energy_per_atom, bandgap=calc_doc.output.bandgap, forces=forces, stress=stress, ) @type_override({"corrections": str, "job": str}) class CustodianDoc(BaseModel): corrections: JsonListType = Field( None, title="Custodian Corrections", description="List of custodian correction data for calculation.", ) job: JsonDictType = Field( None, title="Custodian Job Data", description="Job data logged by custodian.", ) class AnalysisDoc(BaseModel): delta_volume: float | None = Field( None, title="Volume Change", description="Volume change for the calculation.", ) delta_volume_percent: float | None = Field( None, title="Volume Change Percent", description="Percent volume change for the calculation.", ) max_force: float | None = Field( None, title="Max Force", description="Maximum force on any atom at the end of the calculation.", ) warnings: list[str] | None = Field( None, title="Calculation Warnings", description="Warnings issued after analysis.", ) errors: list[str] | None = Field( None, title="Calculation Errors", description="Errors issued after analysis.", ) @classmethod def from_vasp_calc_docs( cls, calcs_reversed: list[Calculation], volume_change_warning_tol: float = 0.2, ) -> "AnalysisDoc": """ Create analysis summary from VASP calculation documents. Parameters ---------- calcs_reversed A list of VASP calculation documents in reverse order . volume_change_warning_tol Maximum volume change allowed in VASP relaxations before the calculation is tagged with a warning. Returns ------- AnalysisDoc The relaxation analysis. """ initial_vol = calcs_reversed[-1].input.structure.lattice.volume final_vol = calcs_reversed[0].output.structure.lattice.volume delta_vol = final_vol - initial_vol percent_delta_vol = 100 * delta_vol / initial_vol warnings = [] errors = [] if abs(percent_delta_vol) > volume_change_warning_tol * 100: warnings.append(f"Volume change > {volume_change_warning_tol * 100}%") final_calc = calcs_reversed[0] max_force = None if final_calc.has_vasp_completed == TaskState.SUCCESS: # max force and valid structure checks structure = final_calc.output.structure # do not check max force for MD run if calcs_reversed[0].input.parameters.get("IBRION", -1) != 0: max_force = _get_max_force(final_calc) warnings.extend(_get_drift_warnings(final_calc)) if not structure.is_valid(): errors.append("Bad structure (atoms are too close!)") return cls( delta_volume=delta_vol, delta_volume_percent=percent_delta_vol, max_force=max_force, warnings=warnings, errors=errors, ) @type_override({"transformations": str, "vasp_objects": str}) class CoreTaskDoc(StructureMetadata): """Calculation-level details about VASP calculations that power the Materials Project.""" batch_id: str | None = Field( None, description="Identifier for this calculation; should provide rough information about the calculation origin and purpose.", ) calc_type: CalcType | None = Field( None, description="The functional and task type used in the calculation." ) completed_at: NullableDateTimeType = Field( description="Timestamp for when this task was completed" ) dir_name: str | None = Field(None, description="The directory for this VASP task") icsd_id: int | None = Field( None, description="Inorganic Crystal Structure Database id of the structure" ) input: CalculationInput | None = Field( None, description="VASP calculation inputs", ) last_updated: DateTimeType = Field( description="Timestamp for the most recent calculation for this task document", ) orig_inputs: CalculationInput | None = Field( None, description="The exact set of input parameters used to generate the current task document.", ) output: CoreCalculationOutput | None = Field( None, description="VASP calculation outputs.", ) run_type: RunType | None = Field( None, description="The functional used in the calculation." ) structure: StructureType | None = Field( None, description="Final output structure from the task" ) tags: list[str] | None = Field( None, title="tag", description="Metadata tagged to a given task." ) task_id: IdentifierType | None = Field( None, description="The (task) ID of this calculation, used as a universal reference across property documents.", ) task_type: TaskType | CalcType | None = Field( None, description="The type of calculation." ) transformations: JsonDictType = Field( None, description="Information on the structural transformations, parsed from a " "transformations.json file", ) vasp_objects: Annotated[ dict[VaspObject, Any] | None, BeforeValidator(_deser_json_like), WrapSerializer(_ser_json_like), ] = Field(None, description="Vasp objects associated with this task") vasp_version: str | None = Field( None, description="The version of VASP used for this task." ) @field_validator("batch_id", mode="before") def validate_batch_id(cls, batch_id: str): if batch_id: invalid_chars = set( char for char in batch_id if (not char.isalnum()) and (char not in {"-", "_"}) ) if invalid_chars: raise ValueError( f"Invalid characters in batch_id: {' '.join(invalid_chars)}" ) return batch_id @classmethod def from_directory( cls, dir_name: Path | str, volumetric_files: tuple[str, ...] = _VOLUMETRIC_FILES, batch_id: str | None = None, tags: list[str | None] = [], **vasp_calculation_kwargs, ) -> tuple[Self, RelaxTrajectory]: """ Create a CoreTaskDoc and corresponding CoreTrajectory from a directory containing VASP files. Only parses a single calculation. Use TaskDoc.from_directory(...) to parse multiple calculations. Parameters ---------- dir_name The path to the folder containing the calculation outputs. volumetric_files Volumetric files to search for. batch_id Calculation identifier tags Additional metadata tags. Will be concatenated with tags parsed from transformations file (if present in ``dir_name``) **vasp_calculation_kwargs Additional parsing options that will be passed to the :obj:`.Calculation.from_vasp_files` function. Returns ------- CoreTaskDoc A slim task document for the calculation. RelaxTrajectory A low memory document for the calculation's trajectory. """ dir_name = Path(dir_name) task_files = _find_vasp_files(dir_name, volumetric_files=volumetric_files) calc_doc, vasp_objects = Calculation.from_vasp_files( dir_name, "standard", **task_files["standard"], **vasp_calculation_kwargs ) transformations, icsd_id, transformation_tags, author = _parse_transformations( dir_name ) task_doc = cls.from_structure( batch_id=batch_id, calc_type=calc_doc.calc_type, completed_at=calc_doc.completed_at, dir_name=get_uri(dir_name), icsd_id=icsd_id, input=calc_doc.input, meta_structure=calc_doc.output.structure, orig_inputs=_parse_orig_inputs(dir_name), output=CoreCalculationOutput( **calc_doc.output.model_dump( include=set(CoreCalculationOutput.model_fields) ) ), run_type=calc_doc.run_type, structure=calc_doc.output.structure, tags=tags + transformation_tags, task_type=calc_doc.task_type, transformations=transformations, vasp_objects=vasp_objects, vasp_version=calc_doc.vasp_version, ) trajectory = get_trajectories_from_calculations([calc_doc])[0] return (task_doc, trajectory) class ValidationTaskDoc(CoreTaskDoc): """ Wrapper for TaskDoc to ensure compatiblity with validation checks in MaterialsDoc.from_tasks(...) if validation builder is skipped """ is_valid: bool = Field(True) @type_override({"additional_json": str}) class TaskDoc(CoreTaskDoc, extra="allow"): """Flexible wrapper around CoreTaskDoc""" additional_json: JsonDictType = Field( None, description="Additional json loaded from the calculation directory" ) analysis: AnalysisDoc | None = Field( None, title="Calculation Analysis", description="Some analysis of calculation data after collection.", ) author: str | None = Field( None, description="Author extracted from transformations" ) calcs_reversed: list[Calculation] | None = Field( None, title="Calcs reversed data", description="Detailed data for each VASP calculation contributing to the task document.", ) custodian: list[CustodianDoc] | None = Field( None, title="Calcs reversed data", description="Detailed custodian data for each VASP calculation contributing to the task document.", ) entry: ComputedEntryType | None = Field( None, description="The ComputedEntry from the task doc" ) included_objects: list[VaspObject] | None = Field( None, description="List of VASP objects included with this task document" ) output: OutputDoc | None = Field( None, description="The exact set of output parameters used to generate the current task document.", ) run_stats: dict[str, RunStatistics] | None = Field( None, description="Summary of runtime statistics for each calculation in this task", ) state: TaskState | None = Field(None, description="State of this calculation") task_label: str | None = Field(None, description="A description of the task") @model_validator(mode="before") @classmethod def set_model_pre_fields(cls, values: Any) -> Any: """Ensure all important model fields are set and refreshed.""" # Always refresh task_type, calc_type, run_type # if attributes containing input sets are available. # See, e.g. https://github.com/materialsproject/emmet/issues/960 # where run_type's were set incorrectly in older versions of TaskDoc attrs = ["calcs_reversed", "input", "orig_inputs"] for icalc, calc in enumerate(values.get("calcs_reversed", [])): if isinstance(calc, dict): values["calcs_reversed"][icalc] = Calculation(**calc) calcs_reversed = values.get("calcs_reversed") if any(values.get(attr) is not None for attr in attrs): # To determine task and run type, we search for input sets in this order # of precedence: calcs_reversed, inputs, orig_inputs inp_set = None inp_sets_to_check = [values.get("input"), values.get("orig_inputs")] if calcs_reversed: inp_sets_to_check = [calcs_reversed[0].get("input")] + inp_sets_to_check for inp_set in inp_sets_to_check: if inp_set is not None: values["task_type"] = task_type(inp_set) break # calcs_reversed needed below if calcs_reversed: values["run_type"] = cls._get_run_type(calcs_reversed) if inp_set is not None: values["calc_type"] = cls._get_calc_type(calcs_reversed, inp_set) if calcs_reversed: # TODO: remove after imposing TaskDoc schema on older tasks in collection if final_struct := calcs_reversed[0].output.structure: values["structure"] = values.get("structure", final_struct) values["entry"] = cls.get_entry(calcs_reversed, values.get("task_id")) return values @classmethod def from_directory( cls, dir_name: Path | str, volumetric_files: tuple[str, ...] = _VOLUMETRIC_FILES, batch_id: str | None = None, tags: list[str | None] = [], store_additional_json: bool = True, additional_fields: dict[str, Any] | None = None, volume_change_warning_tol: float = 0.2, task_names: list[str] | None = None, **vasp_calculation_kwargs, ) -> Self: """ Create a task document from a directory containing VASP files. Parameters ---------- dir_name The path to the folder containing the calculation outputs. store_additional_json Whether to store additional json files found in the calculation directory. volumetric_files Volumetric files to search for. additional_fields Dictionary of additional fields to add to output document. volume_change_warning_tol Maximum volume change allowed in VASP relaxations before the calculation is tagged with a warning. task_names Naming scheme for multiple calculations in on folder e.g. ["relax1","relax2"]. Can be subfolder or extension. **vasp_calculation_kwargs Additional parsing options that will be passed to the :obj:`.Calculation.from_vasp_files` function. Returns ------- TaskDoc A task document for the calculation. """ logger.info(f"Getting task doc in: {dir_name}") additional_fields = {} if additional_fields is None else additional_fields dir_name = Path(dir_name).resolve() task_files = _find_vasp_files( dir_name, volumetric_files=volumetric_files, task_names=task_names ) if len(task_files) == 0: raise FileNotFoundError("No VASP files found!") calcs_reversed = [] all_vasp_objects = [] for task_name in sorted(task_files): calc_doc, vasp_objects = Calculation.from_vasp_files( dir_name, task_name, **task_files[task_name], **vasp_calculation_kwargs ) calcs_reversed.append(calc_doc) all_vasp_objects.append(vasp_objects) # Reverse the list of calculations in the order: newest calc is the first # To match with calcs_reversed, all_vasp_objects is also reversed. calcs_reversed.reverse() all_vasp_objects.reverse() analysis = AnalysisDoc.from_vasp_calc_docs( calcs_reversed, volume_change_warning_tol=volume_change_warning_tol ) transformations, icsd_id, transformation_tags, author = _parse_transformations( dir_name ) custodian = _parse_custodian(dir_name) orig_inputs = _parse_orig_inputs(dir_name) additional_json = None if store_additional_json: additional_json = _parse_additional_json(dir_name) dir_name = get_uri(dir_name) # convert to full uri path # only store objects from last calculation # TODO: make this an option vasp_objects = all_vasp_objects[0] included_objects = None if vasp_objects: included_objects = list(vasp_objects.keys()) doc = cls.from_structure( batch_id=batch_id, structure=calcs_reversed[0].output.structure, meta_structure=calcs_reversed[0].output.structure, dir_name=dir_name, calcs_reversed=calcs_reversed, analysis=analysis, transformations=transformations, custodian=custodian, orig_inputs=orig_inputs, additional_json=additional_json, icsd_id=icsd_id, tags=tags + transformation_tags, author=author, completed_at=calcs_reversed[0].completed_at, input=calcs_reversed[-1].input, output=OutputDoc.from_vasp_calc_doc( calcs_reversed[0], vasp_objects.get(VaspObject.TRAJECTORY), # type: ignore ), state=_get_state(calcs_reversed, analysis), run_stats=_get_run_stats(calcs_reversed), vasp_objects=vasp_objects, included_objects=included_objects, task_type=calcs_reversed[0].task_type, vasp_version=calcs_reversed[0].vasp_version, ) return doc.model_copy(update=additional_fields) @classmethod def from_vasprun( cls, path: str | Path, additional_fields: dict[str, Any] | None = None, volume_change_warning_tol: float = 0.2, **vasp_calculation_kwargs, ) -> Self: """ Create a task document from a vasprun.xml file. This is not recommended and will raise warnings, since some necessary information is absent from the vasprun.xml file, such as per-atom magnetic moments. However, the majority of the TaskDoc will be complete. Parameters ---------- path The path to the vasprun.xml. additional_fields: dict[str, Any] = None, volume_change_warning_tol Maximum volume change allowed in VASP relaxations before the calculation is tagged with a warning. **vasp_calculation_kwargs Additional parsing options that will be passed to the :obj:`.Calculation.from_vasp_files` function. Returns ------- TaskDoc A task document for the calculation. """ logger.info(f"Getting vasprun.xml at: {path}") path = Path(path) dir_name = path.resolve().parent calc = Calculation.from_vasprun(path, **vasp_calculation_kwargs) calcs_reversed = [calc] analysis = AnalysisDoc.from_vasp_calc_docs( calcs_reversed, volume_change_warning_tol=volume_change_warning_tol ) # assume orig_inputs are those stated in vasprun.xml orig_inputs = CalculationInput( incar=calc.input.incar, structure=calc.input.structure, kpoints=calc.input.kpoints, potcar=calc.input.potcar, ) doc = cls.from_structure( structure=calcs_reversed[0].output.structure, meta_structure=calcs_reversed[0].output.structure, dir_name=get_uri(dir_name), calcs_reversed=calcs_reversed, analysis=analysis, orig_inputs=orig_inputs, completed_at=calcs_reversed[0].completed_at, input=calcs_reversed[-1].input, output=OutputDoc.from_vasp_calc_doc(calcs_reversed[0]), state=_get_state(calcs_reversed, analysis), run_stats=None, vasp_objects={}, included_objects=[], task_type=calcs_reversed[0].task_type, ) if additional_fields: doc = doc.model_copy(update=additional_fields) return doc @staticmethod def get_entry( calcs_reversed: list[Calculation | dict], task_id: IdentifierType | str | int | None = None, ) -> ComputedEntry: """ Get a computed entry from a list of VASP calculation documents. Parameters ---------- calcs_reversed A list of VASP calculation documents in a reverse order. task_id The job identifier. Returns ------- ComputedEntry A computed entry. """ if isinstance(cr := calcs_reversed[0], dict): cr = Calculation(**cr) calc_inp = cr.input calc_out = cr.output entry_dict = { "correction": 0.0, "entry_id": ( f"{task_id}-{cr.run_type}" if task_id is not None and cr.run_type is not None else None ), "composition": calc_out.structure.composition, "energy": calc_out.energy, "parameters": { # Cannot be PotcarSpec document, pymatgen expects a dict # Note that `potcar_spec` is optional "potcar_spec": ( [dict(d) for d in calc_inp.potcar_spec] if calc_inp.potcar_spec else [] ), # Required to be compatible with MontyEncoder for the ComputedEntry "run_type": str(cr.run_type), "is_hubbard": calc_inp.is_hubbard, "hubbards": calc_inp.hubbards, }, "data": { "oxide_type": oxide_type(calc_out.structure), "aspherical": calc_inp.parameters.get("LASPH", False), "last_updated": str(utcnow()), }, } return ComputedEntry.from_dict(entry_dict) @staticmethod def _get_calc_type( calcs_reversed: list[Calculation], orig_inputs: CalculationInput ) -> CalcType: """Get the calc type from calcs_reversed. Returns -------- CalcType The type of calculation. """ if isinstance(calcs_reversed[0], Calculation): cr_inp = calcs_reversed[0].input params = cr_inp.parameters incar = cr_inp.incar else: cr_inp = calcs_reversed[0].get("input", {}) params = cr_inp.get("parameters", {}) incar = cr_inp.get("incar", {}) inputs = cr_inp if len(calcs_reversed) > 0 else orig_inputs return calc_type(inputs, {**params, **incar}) @staticmethod def _get_run_type(calcs_reversed: list[Calculation | dict]) -> RunType: """Get the run type from calcs_reversed. Returns -------- RunType The type of calculation. """ if isinstance(calcs_reversed[0], Calculation): params = calcs_reversed[0].input.parameters incar = calcs_reversed[0].input.incar else: cr_inp = calcs_reversed[0].get("input", {}) params = cr_inp.get("parameters", {}) incar = cr_inp.get("incar", {}) return run_type({**params, **incar}) @property def structure_entry(self) -> ComputedStructureEntry: """ Retrieve a ComputedStructureEntry for this TaskDoc. Returns ------- ComputedStructureEntry The TaskDoc.entry with corresponding TaskDoc.structure added. """ if not self.structure or not self.entry: raise ValueError( "Need both a `structure` and `entry` to return a `ComputedStructureEntry`." ) return ComputedStructureEntry( structure=self.structure, energy=self.entry.energy, correction=self.entry.correction, composition=self.entry.composition, energy_adjustments=self.entry.energy_adjustments, parameters=self.entry.parameters, data=self.entry.data, entry_id=self.entry.entry_id, ) @property def trajectories(self) -> list[Trajectory] | None: """Get Trajectory objects representing calcs_reversed. Note that the Trajectory objects represent the proper calculation order, not the reversed. Thus the first Trajectory represents the first calculation that was performed (`self.calcs_reversed[-1]`). """ if self.calcs_reversed: return get_trajectories_from_calculations( self.calcs_reversed[::-1], separate=False, identifier=str(self.task_id) if self.task_id else None, ) return None class TrajectoryDoc(BaseModel): """Model for task trajectory data.""" task_id: str | None = Field( None, description="The (task) ID of this calculation, used as a universal reference across property documents." "This comes in the form: mp-******.", ) trajectories: list[RelaxTrajectory] | None = Field( None, description="Trajectory data for calculations associated with a task doc.", ) class EntryDoc(BaseModel): """Model for task entry data.""" task_id: str | None = Field( None, description="The (task) ID of this calculation, used as a universal reference across property documents." "This comes in the form: mp-******.", ) entry: ComputedStructureEntryType | None = Field( None, description="Computed structure entry for the calculation associated with the task doc.", ) def get_uri(dir_name: str | Path) -> str: """ Return the URI path for a directory. This allows files hosted on different file servers to have distinct locations. Parameters ---------- dir_name : str or Path A directory name. Returns ------- str Full URI path, e.g., "fileserver.host.com:/full/path/of/dir_name". """ import socket fullpath = Path(dir_name).absolute() hostname = socket.gethostname() try: hostname = socket.gethostbyaddr(hostname)[0] except (socket.gaierror, socket.herror): pass return f"{hostname}:{fullpath}" def _parse_transformations( dir_name: Path, ) -> tuple[dict, int | None, list[str | None], str | None]: """Parse transformations.json file.""" transformations = {} filenames = tuple(dir_name.glob("transformations.json*")) icsd_id = None if len(filenames) >= 1: transformations = loadfn(filenames[0], cls=None) try: match = re.match(r"(\d+)-ICSD", transformations["history"][0]["source"]) if match: icsd_id = int(match.group(1)) except (KeyError, IndexError): pass # We don't want to leave tags or authors in the # transformations file because they'd be copied into # every structure generated after this one. other_parameters = transformations.get("other_parameters", {}) new_tags = other_parameters.pop("tags", []) new_author = other_parameters.pop("author", None) if "other_parameters" in transformations and not other_parameters: # if dict is now empty remove it transformations.pop("other_parameters") return transformations, icsd_id, new_tags, new_author def _parse_custodian(dir_name: Path) -> dict | None: """ Parse custodian.json file. Calculations done using custodian have a custodian.json file which tracks the makers performed and any errors detected and fixed. Parameters ---------- dir_name Path to calculation directory. Returns ------- dict | None The information parsed from custodian.json file. """ filenames = tuple(dir_name.glob("custodian.json*")) if len(filenames) >= 1: return loadfn(filenames[0], cls=None) return None def _parse_orig_inputs( dir_name: Path, suffix: str | None = ".orig" ) -> CalculationInput: """ Parse original input files. Calculations using custodian generate a *.orig file for the inputs. This is useful to know how the calculation originally started. Parameters ---------- dir_name Path to calculation directory. suffix : str or None = ".orig" The suffix of the original input files to use. Returns ------- CalculationInput The original input Structure, Kpoints, PotcarSpec, and Incar data. """ orig_inputs = {} input_mapping: dict[str, Kpoints | Poscar | PotcarSpec | Incar] = { "INCAR": Incar, "KPOINTS": Kpoints, "POTCAR": PotcarSpec, "POSCAR": Poscar, } suffix = suffix or "" for filename in dir_name.glob("*".join(f"{suffix}.".split("."))): if f"POTCAR.spec{suffix}" in str(filename): try: orig_inputs["potcar_spec"] = PotcarSpec.from_file(filename) except Exception: # Can't parse non emmet-core style POTCAR.spec files continue for name, vasp_input in input_mapping.items(): if f"{name}{suffix}" in str(filename): file_suffix = "_spec" if name == "POTCAR" else "" _key = ( "structure" if name == "POSCAR" else f"{name.lower()}{file_suffix}" ) _value = vasp_input.from_file(filename) _assign = _value.structure if _key == "structure" else _value orig_inputs[_key] = _assign return CalculationInput(**orig_inputs) def _parse_additional_json(dir_name: Path) -> dict[str, Any]: """Parse additional json files in the directory.""" additional_json = {} for filename in dir_name.glob("*.json*"): key = filename.name.split(".")[0] # ignore FW.json(.gz) so jobflow doesn't try to parse prev_vasp_dir OutputReferences # was causing atomate2 MP workflows to fail with ValueError: Could not resolve reference # 7f5a7f14-464c-4a5b-85f9-8d11b595be3b not in store or cache # contact @janosh in case of questions if key not in ("custodian", "transformations", "FW"): additional_json[key] = loadfn(filename, cls=None) return additional_json def _get_max_force(calc_doc: Calculation) -> float | None: """Get max force acting on atoms from a calculation document.""" if calc_doc.output.ionic_steps: forces: np.ndarray | list | None = None if calc_doc.output.ionic_steps: forces = calc_doc.output.ionic_steps[-1].forces structure = calc_doc.output.structure if forces: forces = np.array(forces) sdyn = structure.site_properties.get("selective_dynamics") if sdyn: forces[np.logical_not(sdyn)] = 0 return max(np.linalg.norm(forces, axis=1)) return None def _get_drift_warnings(calc_doc: Calculation) -> list[str]: """Get warnings of whether the drift on atoms is too large.""" warnings = [] if calc_doc.input.parameters.get("NSW", 0) > 0: drift = calc_doc.output.outcar.get("drift", [[0, 0, 0]]) max_drift = max(np.linalg.norm(d) for d in drift) # type: ignore[type-var] ediffg = calc_doc.input.parameters.get("EDIFFG", None) max_force = -float(ediffg) if ediffg and float(ediffg) < 0 else np.inf if max_drift > max_force: warnings.append( f"Drift ({drift}) > desired force convergence ({max_force}), structure " "likely not converged to desired accuracy." ) return warnings def _get_state(calcs_reversed: list[Calculation], analysis: AnalysisDoc) -> TaskState: """Get state from calculation documents and relaxation analysis.""" all_calcs_completed = all( c.has_vasp_completed == TaskState.SUCCESS for c in calcs_reversed ) if ( analysis.errors is None or (isinstance(analysis.errors, list) and len(analysis.errors) == 0) ) and all_calcs_completed: return TaskState.SUCCESS # type: ignore return TaskState.FAILED # type: ignore def _get_run_stats(calcs_reversed: list[Calculation]) -> dict[str, RunStatistics]: """Get summary of runtime statistics for each calculation in this task.""" run_stats = {} total = dict( average_memory=0.0, max_memory=0.0, elapsed_time=0.0, system_time=0.0, user_time=0.0, total_time=0.0, cores=0, ) for calc_doc in calcs_reversed: stats = calc_doc.output.run_stats run_stats[calc_doc.task_name] = stats total["average_memory"] = max(total["average_memory"], stats.average_memory) total["max_memory"] = max(total["max_memory"], stats.max_memory) total["cores"] = max(total["cores"], stats.cores) total["elapsed_time"] += stats.elapsed_time total["system_time"] += stats.system_time total["user_time"] += stats.user_time total["total_time"] += stats.total_time run_stats["overall"] = RunStatistics(**total) return run_stats def _find_vasp_files( path: str | Path, volumetric_files: Sequence[str] | None = None, task_names: Sequence[str] | None = None, ) -> dict[str, dict[str, Path | list[Path]]]: """ Find VASP files in a directory. Only files in folders with names matching a task name (or alternatively files with the task name as an extension, e.g., vasprun.relax1.xml) will be returned. VASP files in the current directory will be given the task name "standard". Parameters ---------- path Path to a directory to search. volumetric_files : sequence of str, defaults to _VOLUMETRIC_FILES Volumetric files to search for. task_names : sequence of str, defaults to task_names = ["precondition","standard","relax0","relax1",..."relax8"] Returns ------- dict[str,dict[str,Path | list[Path]]] The filenames of the calculation outputs for each VASP task, given as a ordered dictionary of:: { task_name: { "vasprun_file": vasprun_filename, "outcar_file": outcar_filename, "contcar_file": contcar_filename, "potcar_spec_file": potcar_spec_filename, "volumetric_files": [CHGCAR, LOCPOT, etc] "elph_poscars": [POSCAR.T=300, POSCAR.T=400, etc] }, ... } """ base_path = Path(path) volumetric_files = volumetric_files or _VOLUMETRIC_FILES task_names = task_names or TASK_NAMES task_files: dict[str, dict[str, Path | list[Path]]] = discover_and_sort_vasp_files( base_path ) # TODO: TaskDoc permits matching sub directories if they use one of # `task_names` as a directory name. # Not sure this is behavior we want to keep in the long term, # but is maintained here for backwards compatibility. for task_name in set(task_names).difference(task_files): if (subdir := base_path / task_name).exists(): for task_name, calcs in discover_and_sort_vasp_files(subdir).items(): task_files[task_name].update(calcs) # For old double-relax style jobs, there will only be one POTCAR/.spec/.orig file # even though there will be an INCAR.relax1, INCAR.relax2, etc. # Undo that mapping here if any(k.startswith("relax") for k in task_files) and task_files.get( "standard", {} ).get("potcar_spec_file"): psf = task_files["standard"].pop("potcar_spec_file") for task_name in {k for k in task_files if k.startswith("relax")}: task_files[task_name]["potcar_spec_file"] = psf if not all( task_files.get("standard", {}).get(k) for k in ( "vasprun_file", "outcar_file", "contcar_file", ) ): _ = task_files.pop("standard") return task_files ././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1787011023.826609 emmet_core-0.87.2/emmet/core/testing_utils.py0000644000175100017510000001242515240717720020714 0ustar00runnerrunner"""Define common testing utils used across emmet namespaces.""" from __future__ import annotations import math from contextlib import contextmanager from importlib.resources import files as import_resource from pathlib import Path from tempfile import NamedTemporaryFile, TemporaryDirectory from typing import TYPE_CHECKING import numpy as np import pandas as pd from pydantic import BaseModel from emmet.core.utils import arrow_incompatible if TYPE_CHECKING: from collections.abc import Callable, Generator from typing import Any from emmet.core.types.typing import FSPathType def _get_test_files_dir(module: str) -> Path: """Get path to test files directory.""" return ( Path(import_resource(module)) # type: ignore[arg-type] .parent.parent.parent.joinpath("test_files") .resolve() ) TEST_FILES_DIR = _get_test_files_dir("emmet.core") """Test files directory measured relative to emmet-core install.""" def _float_comparator(x: float, y: float): """Compare floats with defaults set to pytest.approx defaults.""" return math.isclose(x, y, rel_tol=1e-6, abs_tol=1e-12) def assert_schemas_equal( test_schema: Any, valid_schema: Any, float_comparator: Callable = _float_comparator ) -> None: """ Recursively test all items in valid_schema are present and equal in test_schema. While test_schema can be a pydantic schema or dictionary, the valid schema must be a (nested) dictionary. This function automatically handles accessing the attributes of classes in the test_schema. Args: test_schema: A pydantic schema or dictionary of the schema. valid_schema: A (nested) dictionary specifying the key and values that must be present in test_schema. float_comparator (Callable) : A method to compare floats. Defaults to math.isclose. Should return a bool if two floats are approximately equal, and False otherwise. """ if isinstance(valid_schema, dict): for key, sub_valid_schema in valid_schema.items(): if isinstance(key, str) and hasattr(test_schema, key): sub_test_schema = getattr(test_schema, key, {}) if key in ("initial_molecule", "optimized_molecule") and hasattr( sub_test_schema, "as_dict" ): sub_test_schema = sub_test_schema.as_dict() elif not isinstance(test_schema, BaseModel): sub_test_schema = test_schema[key] else: raise ValueError(f"{type(test_schema)} does not have field: {key}") return assert_schemas_equal(sub_test_schema, sub_valid_schema) elif isinstance(valid_schema, list): for i, sub_valid_schema in enumerate(valid_schema): return assert_schemas_equal(test_schema[i], sub_valid_schema) elif isinstance(valid_schema, np.ndarray): assert np.allclose(test_schema, valid_schema) elif isinstance(valid_schema, float): assert float_comparator(test_schema, valid_schema) else: assert test_schema == valid_schema @arrow_incompatible class DataArchive(BaseModel): """Tool to compress test data into a lower disk / innode use file.""" files: dict[str, bytes] @classmethod def from_directory(cls, path: FSPathType): """Find all files in . and parse them.""" files = {} for p in Path(path).glob("*"): if p.is_file(): files[p.name] = p.read_bytes() return cls(files=files) def to_json(self, file_name: FSPathType) -> None: """Write all contents to JSON.""" df = pd.DataFrame({k: [v.hex()] for k, v in self.files.items()}) df.to_json(file_name, orient="columns") @staticmethod def from_json(archive_path: FSPathType) -> pd.DataFrame: df = pd.read_json(archive_path) for c in df.columns: df[c] = df[c].apply(bytes.fromhex) return df @classmethod @contextmanager # type: ignore[arg-type] def extract(cls, archive_path: FSPathType) -> Generator[Path]: """Extract all bytes data from a JSON archive. Parameters ----------- archive_path : FSPathType The name of the JSON file Returns ----------- pandas DataFrame representing the parsed data. """ df = cls.from_json(archive_path) temp_dir = TemporaryDirectory() base_path = Path(temp_dir.name) for c in df.columns: (base_path / c).write_bytes(df[c][0]) try: yield base_path finally: temp_dir.cleanup() @staticmethod def extract_obj( archive_path: FSPathType, file_name: str, func: Callable, *args, **kwargs ) -> Any: """Extract a single file and apply a mapping to it.""" df = pd.read_json(archive_path) if file_name not in df.columns: raise ValueError( f"No object {file_name} found, valid columns: {', '.join(df.columns)}" ) temp_file = NamedTemporaryFile(suffix="".join(Path(file_name).suffixes)) with open(temp_file.name, "wb") as f: f.write(bytes.fromhex(df[file_name][0])) f.seek(0) obj = func(temp_file.name, *args, **kwargs) temp_file.close() return obj ././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1787011023.826609 emmet_core-0.87.2/emmet/core/thermo.py0000644000175100017510000003136015240717720017314 0ustar00runnerrunner"""Core definition of a Thermo Document""" from __future__ import annotations from collections import defaultdict from functools import cached_property from typing import TYPE_CHECKING, Sequence, overload from pydantic import BaseModel, Field from emmet.core.io.pymatgen import PhaseDiagram, ComputedEntry, ComputedStructureEntry from emmet.core.base import EmmetMeta from emmet.core.material import PropertyOrigin from emmet.core.material_property import PropertyDoc from emmet.core.types.enums import ThermoType from emmet.core.types.pymatgen_types.computed_entries_adapter import ( ComputedStructureEntryType, ) from emmet.core.types.pymatgen_types.phase_diagram_adapter import PhaseDiagramType from emmet.core.types.typing import ( DateTimeType, IdentifierType, validate_compound_identifier, ) from emmet.core.utils import type_override, utcnow from emmet.core.vasp.calc_types.enums import RunType if TYPE_CHECKING: from typing import Literal from emmet.core.types.typing import CompoundIDType @overload def validate_thermo_id( idx: str, as_components: Literal[True] = True ) -> CompoundIDType: ... @overload def validate_thermo_id(idx: str, as_components: Literal[False] = False) -> str: ... def validate_thermo_id(idx: str, as_components: bool = False) -> str | CompoundIDType: """Validate a thermo identifier.""" for enum_cls in (ThermoType, RunType): try: return validate_compound_identifier( idx, suffixes=(enum_cls,), separator="_", use_prefix=True, as_components=as_components, ) except ValueError: continue raise ValueError("Could not validate `thermo_id`.") class DecompositionProduct(BaseModel): """ Entry metadata for a decomposition process """ material_id: IdentifierType | None = Field( None, description="The Materials Project ID for the material this decomposition points to.", ) formula: str | None = Field( None, description="The formula of the decomposed material this material decomposes to.", ) amount: float | None = Field( None, description="The amount of the decomposed material by formula units this material decomposes to.", ) class ThermoDoc(PropertyDoc): """ A thermo entry document """ property_name: str = "thermo" thermo_type: ThermoType | RunType = Field( ..., description="Functional types of calculations involved in the energy mixing scheme.", ) uncorrected_energy_per_atom: float = Field( ..., description="The total DFT energy of this material per atom in eV/atom." ) energy_per_atom: float = Field( ..., description="The total corrected DFT energy of this material per atom in eV/atom.", ) energy_uncertainy_per_atom: float | None = Field(None, description="") formation_energy_per_atom: float | None = Field( None, description="The formation energy per atom in eV/atom." ) energy_above_hull: float = Field( ..., description="The energy above the hull in eV/Atom." ) is_stable: bool = Field( False, description="Flag for whether this material is on the hull and therefore stable.", ) equilibrium_reaction_energy_per_atom: float | None = Field( None, description="The reaction energy of a stable entry from the neighboring equilibrium stable materials in eV." " Also known as the inverse distance to hull.", ) decomposes_to: list[DecompositionProduct] | None = Field( None, description="List of decomposition data for this material. Only valid for metastable or unstable material.", ) decomposition_enthalpy: float | None = Field( None, description="Decomposition enthalpy as defined by `get_decomp_and_phase_separation_energy` in pymatgen.", ) decomposition_enthalpy_decomposes_to: list[DecompositionProduct] | None = Field( None, description="List of decomposition data associated with the decomposition_enthalpy quantity.", ) energy_type: str = Field( ..., description="The type of calculation this energy evaluation comes from.", ) entry_types: list[str] = Field( description="List of available energy types computed for this material." ) entries: dict[str, ComputedStructureEntryType] = Field( ..., description="List of all entries that are valid for this material." " The keys for this dictionary are names of various calculation types.", ) @cached_property def thermo_id(self) -> str: """Unique document ID which is composed of the Material ID and thermo data type.""" if not self.material_id or not self.thermo_type: raise ValueError( "Cannot determine thermo_id: missing either `material_id` or `thermo_type`." ) return validate_thermo_id( f"{self.material_id}-{self.thermo_type}", as_components=False ) @classmethod def from_entries( cls, entries: Sequence[ComputedEntry | ComputedStructureEntry], thermo_type: ThermoType | RunType, phase_diagram: PhaseDiagram | None = None, use_max_chemsys: bool = False, **kwargs, ): """Produce a list of ThermoDocs from a list of Entry objects Args: entries (list[ComputedEntry| ComputedStructureEntry]): list of Entry objects thermo_type (ThermoType | RunType): Thermo type phase_diagram (PhaseDiagram | None, optional): Already built phase diagram. Defaults to None. use_max_chemsys (bool, optional): Whether to only produce thermo docs for materials that match the largest chemsys represented in the list. Defaults to False. Returns: list[ThermoDoc]: list of built thermo doc objects. """ pd = phase_diagram or cls.construct_phase_diagram(entries) chemsys = "-".join(sorted([str(e) for e in pd.elements])) docs = [] entries_by_mpid = defaultdict(list) for e in entries: entries_by_mpid[e.data["material_id"]].append(e) entry_quality_scores = {"GGA": 1, "GGA+U": 2, "SCAN": 3, "r2SCAN": 4} def _energy_eval(entry: ComputedStructureEntry | ComputedEntry): """ Helper function to order entries for thermo energy data selection - Run type - LASPH - Energy """ return ( -1 * entry_quality_scores.get(entry.data["run_type"], 0), -1 * int(entry.data.get("aspherical", False)), entry.energy, ) for material_id, entry_group in entries_by_mpid.items(): if ( use_max_chemsys and entry_group[0].composition.chemical_system != chemsys ): continue sorted_entries = sorted(entry_group, key=_energy_eval) blessed_entry = sorted_entries[0] (decomp, ehull) = pd.get_decomp_and_e_above_hull(blessed_entry) # type: ignore[arg-type] builder_meta = EmmetMeta(license=blessed_entry.data.get("license")) # type: ignore[call-arg] d = { "thermo_id": f"{material_id}-{thermo_type}", "material_id": material_id, "thermo_type": thermo_type, "uncorrected_energy_per_atom": blessed_entry.uncorrected_energy / blessed_entry.composition.num_atoms, "energy_per_atom": blessed_entry.energy / blessed_entry.composition.num_atoms, "formation_energy_per_atom": pd.get_form_energy_per_atom(blessed_entry), # type: ignore[arg-type] "energy_above_hull": ehull, "is_stable": blessed_entry in pd.stable_entries, "builder_meta": builder_meta.model_dump(), } # Uncomment to make last_updated line up with materials. # if "last_updated" in blessed_entry.data: # d["last_updated"] = blessed_entry.data["last_updated"] # Store different info if stable vs decomposes if d["is_stable"]: d[ "equilibrium_reaction_energy_per_atom" ] = pd.get_equilibrium_reaction_energy( blessed_entry # type: ignore[arg-type] ) else: d["decomposes_to"] = [ { "material_id": de.data["material_id"], # type: ignore[union-attr] "formula": de.composition.formula, "amount": amt, } for de, amt in decomp.items() # type: ignore[union-attr] ] try: decomp, energy = pd.get_decomp_and_phase_separation_energy( blessed_entry # type: ignore[arg-type] ) d["decomposition_enthalpy"] = energy d["decomposition_enthalpy_decomposes_to"] = [ { "material_id": de.data["material_id"], # type: ignore[union-attr] "formula": de.composition.formula, "amount": amt, } for de, amt in decomp.items() # type: ignore[union-attr] ] except ValueError: # try/except so this quantity does not take down the builder if it fails: # it includes an optimization step that can be fragile in some instances, # most likely failure is ValueError, "invalid value encountered in true_divide" d["warnings"] = [ "Could not calculate decomposition enthalpy for this entry." ] d["energy_type"] = blessed_entry.parameters.get("run_type", "Unknown") d["entry_types"] = [] d["entries"] = {} # Currently, each entry group contains a single entry due to how the compatibility scheme works for entry in entry_group: d["entry_types"].append(entry.parameters.get("run_type", "Unknown")) d["entries"][entry.parameters.get("run_type", "Unknown")] = entry d["origins"] = [ PropertyOrigin( name="energy", task_id=blessed_entry.data["task_id"], last_updated=d.get("last_updated", utcnow()), ) ] docs.append( ThermoDoc.from_structure( meta_structure=blessed_entry.structure, **d, **kwargs # type: ignore[attr-defined] ) ) return docs @staticmethod def construct_phase_diagram(entries) -> PhaseDiagram: """ Efficienty construct a phase diagram using only the lowest entries at every composition represented in the entry data passed. Args: entries (list[ComputedStructureEntry]): list of corrected pymatgen entry objects. Returns: PhaseDiagram: Pymatgen PhaseDiagram object """ entries_by_comp = defaultdict(list) for e in entries: entries_by_comp[e.composition.reduced_formula].append(e) # Only use lowest entry per composition to speed up QHull in Phase Diagram reduced_entries = [ sorted(comp_entries, key=lambda e: e.energy_per_atom)[0] for comp_entries in entries_by_comp.values() ] pd = PhaseDiagram(reduced_entries) # Add back all entries, not just those on the hull pd_computed_data = pd.computed_data pd_computed_data["all_entries"] = entries new_pd = PhaseDiagram( entries, elements=pd.elements, computed_data=pd_computed_data # type: ignore[arg-type] ) return new_pd @type_override({"thermo_type": ThermoType}) class PhaseDiagramDoc(BaseModel): """ A phase diagram document """ property_name: str = "phase_diagram" phase_diagram_id: str = Field( ..., description="Phase diagram ID consisting of the chemical system and thermo type", ) chemsys: str = Field( ..., description="Dash-delimited string of elements in the material", ) thermo_type: ThermoType | RunType = Field( ..., description="Functional types of calculations involved in the energy mixing scheme.", ) phase_diagram: PhaseDiagramType = Field( ..., description="Phase diagram for the chemical system.", ) last_updated: DateTimeType = Field( description="Timestamp for the most recent calculation update for this property", ) ././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1787011023.826609 emmet_core-0.87.2/emmet/core/trajectory.py0000644000175100017510000006550015240717720020207 0ustar00runnerrunner"""Define schemas for trajectories.""" from __future__ import annotations import logging from pathlib import Path from typing import TYPE_CHECKING import numpy as np from monty.dev import requires from monty.serialization import dumpfn from pydantic import BaseModel, Field, model_validator from emmet.core.io.pymatgen import ( Element, Molecule, Structure, Trajectory as PmgTrajectory, ) from emmet.core.math import Vector3D, Matrix3D from emmet.core.types.enums import ValueEnum from emmet.core.vasp.models import ElectronicStep from emmet.core.vasp.calc_types import RunType, TaskType logger = logging.getLogger(__name__) try: import pyarrow as pa import pyarrow.parquet as pa_pq from pyarrow import Table as ArrowTable except ImportError: pa = None # type: ignore[assignment] pa_pq = None # type: ignore[assignment] ArrowTable = None # type: ignore[assignment] if TYPE_CHECKING: from collections.abc import Iterable from typing import Any from typing_extensions import Self from emmet.core.io.pymatgen import Vasprun class TrajFormat(ValueEnum): """Define known trajectory formats.""" PMG = "json" ASE = "traj" PARQUET = "parquet" class _MDMixin(BaseModel): """Mix-in for molecular dynamics runs.""" magmoms: list[list[float]] | None = Field( None, description="The magnetic moments at each ionic step." ) temperature: list[float] | None = Field( None, description="The temperature at each ionic step." ) velocities: list[list[Vector3D]] | None = Field( None, description="The velocities of each atom." ) time_step: float | None = Field( None, description="The time step used in the calculation." ) ionic_step_properties: set[str] = Field( {"magmoms", "temperature", "velocities", "time_step"}, exclude=True, description="The properties included at each ionic step.", ) @model_validator(mode="after") def add_props(self) -> Self: self.ionic_step_properties = self.ionic_step_properties.union( {"magmoms", "temperature", "velocities"} ) return self def _order_z_list(new_z: list[int], ref_z: list[int]) -> tuple[list[int], bool]: """Order a list of proton numbers (Z) according to a reference list. Parameters ----------- new_z : list of int List of new proton numbers to order ref_z : list of in List of reference proton numbers Returns ----------- list of int New site indices with matching order bool Whether the order changed """ running_sites = list(range(len(new_z))) if new_z == ref_z: return running_sites, False reordered_index = [] for z in ref_z: for idx in running_sites: if new_z[idx] == z: reordered_index.append(idx) running_sites.remove(idx) break if len(running_sites) > 0: raise ValueError("Cannot order atoms according to reference Z list.") return reordered_index, True class AtomRelaxTrajectory(BaseModel): """Atomistic only, low-memory schema for relaxation trajectories that can interface with parquet, pymatgen, and ASE.""" elements: list[int] = Field( description="The proton number Z of the elements in the sites" ) cart_coords: list[list[Vector3D] | None] = Field( description="The Cartesian coordinates (Å) of the sites at each ionic step" ) num_ionic_steps: int = Field(description="The number of ionic steps.") lattice: list[Matrix3D] | None = Field( None, description=( "If a list containing only one 3x3 matrix, it is assumed that " "the lattice was held constant through the simulation. " "If a list of (multiple) 3x3 matrices, this should be the lattice " "at each ionic step in the calculation. " "If None, a non-periodic system is assumed." ), ) energy: list[float] | None = Field( None, description="The total energy at each ionic step." ) forces: list[list[Vector3D]] | None = Field( None, description="The interatomic forces." ) stress: list[Matrix3D] | None = Field(None, description="The 3x3 stress tensor.") ionic_step_properties: set[str] = Field( { "energy", "forces", "stress", }, exclude=True, description="The properties included at each ionic step.", ) @model_validator(mode="before") def reset_num_ionic_steps(cls, config: Any) -> Any: config["num_ionic_steps"] = len(config["cart_coords"]) config["elements"] = [ Element(ele).Z if isinstance(ele, str) else int(ele) for ele in config["elements"] ] return config @property def ionic_step_properties_used(self) -> set[str]: """Return list of populated fields.""" used_fields = { k for k in self.ionic_step_properties if getattr(self, k, None) is not None } # Because `self.lattice == 1` can indicate a frozen lattice if `self.num_ionic_steps > 1` # and a static calculation otherwise, check here that the lattice is a property # that varies with frames if isinstance(self.lattice, list) and len(self.lattice) == self.num_ionic_steps: used_fields.add("lattice") return used_fields def __repr__(self) -> str: """Print summary data.""" return ( f"{self.__class__.__module__}.{self.__class__.__name__} " f"({self.num_ionic_steps} ionic steps) with data: " f"{', '.join(sorted(self.ionic_step_properties_used))}." ) def __hash__(self) -> int: """Used to verify roundtrip conversion of Trajectory.""" return hash(self.model_dump_json()) def __len__(self) -> int: """Get number of ionic steps.""" return self.num_ionic_steps def __getitem__(self, index: int | slice) -> AtomRelaxTrajectory: if isinstance(index, int): if index < 0: index = len(self) + index _slice: slice = slice(index, index + 1) if isinstance(index, int) else index config = {} all_ionic_step_props = self.ionic_step_properties.union( { "lattice", "cart_coords", } ) for k in self.__class__.model_fields: v = getattr(self, k) if v and k in all_ionic_step_props: config[k] = v[_slice] elif v: config[k] = v return type(self)(**config) @staticmethod def reorder_sites( structure: Structure | Molecule, ref_z: list[int] ) -> tuple[Structure | Molecule, list[int], bool]: """ Ensure that the sites in a structure match the order in a set of reference Z values. Quick returns if the structure already matches the reference order. Parameters ----------- structure : pymatgen .Structure or .Molecule Structure or Molecule to order sites in ref_z : list of int List of proton numbers / Z values in the reference structure. Returns ----------- pymatgen .Structure or .Molecule : structure matching the reference order of sites list of int : indices in the original structure which are reordered bool : Whether the order changed """ reordered_index, order_changed = _order_z_list( [site.species.elements[0].Z for site in structure], ref_z ) if not order_changed: return structure, reordered_index, order_changed pmg_cls = Structure if isinstance(structure, Structure) else Molecule return ( pmg_cls.from_sites([structure.sites[idx] for idx in reordered_index]), reordered_index, order_changed, ) @classmethod def _from_dict( cls, props: dict[str, Any], constant_lattice: bool = False, lattice_match_tol: float | None = None, **kwargs, ) -> Self: """ Common class constructor to create a Trajectory from a dict. Parameters ----------- props : dict of str to list Dictionary with keys in cls.model_fields and values which are lists constant_lattice : bool (default = False) Whether the lattice is constant thoughout the trajectory lattice_match_tol : float or None (default = 1e-6) If a float and constant_lattice is False, this defines the tolerance for determining if the lattice has changed during the course of the trajectory. If None, no check is performed. **kwargs Other kwargs to pass to Trajectory Returns ----------- Trajectory """ structures = props.pop("structure") is_structure = isinstance(structures[0], Structure) if is_structure and any(not structure.is_ordered for structure in structures): raise ValueError( "At this time, the Trajectory model in emmet does not support disordered structures." ) num_ionic_steps = len(structures) props["elements"] = [site.species.elements[0].Z for site in structures[0]] if ( is_structure and constant_lattice or ( lattice_match_tol and all( np.all( np.abs( structures[i].lattice.matrix - structures[j].lattice.matrix ) < lattice_match_tol ) for i in range(num_ionic_steps) for j in range(i + 1, num_ionic_steps) ) ) ): props["lattice"] = [structures[0].lattice.matrix] elif is_structure: props["lattice"] = [structure.lattice.matrix for structure in structures] reordered: list[tuple[Structure | Molecule, list[int], bool]] = [ cls.reorder_sites(structure, props["elements"]) for structure in structures ] # Ensure that coordinates and properties associated with sites have consistent ordering props["cart_coords"] = [entry[0].cart_coords for entry in reordered] if any(entry[2] for entry in reordered): for site_prop_key in {"magmoms", "forces"}: if (site_prop_val := props.pop(site_prop_key, None)) or ( site_prop_val := kwargs.pop(site_prop_key, None) ): props[site_prop_key] = [ ( [site_prop_val[ion_step_idx][idx] for idx in entry[1]] if entry[2] else site_prop_key[ion_step_idx] ) for ion_step_idx, entry in enumerate(reordered) ] if len(esteps := props.get("electronic_steps", [])) > 0: props["num_electronic_steps"] = [len(estep) for estep in esteps] return cls(**props, **kwargs) @classmethod def from_pmg(cls, traj: PmgTrajectory, **kwargs) -> Self: """ Create a trajectory from a pymatgen .Trajectory. Parameters ----------- traj : pymatgen.core.trajectory.Trajectory constant_lattice : bool (default = False) Whether the lattice is constant thoughout the trajectory lattice_match_tol : float or None (default = 1e-6) If a float and constant_lattice is False, this defines the tolerance for determining if the lattice has changed during the course of the trajectory. If None, no check is performed. Returns ----------- Trajectory """ props: dict[str, list] = { "structure": [structure for structure in traj], } if traj.frame_properties: for k in cls.model_fields: vals = [fp.get(k) for fp in traj.frame_properties] if all(v is not None for v in vals): props[k] = vals return cls._from_dict(props, **kwargs) @requires( pa is not None, message="pyarrow must be installed to de-/serialize to parquet" ) def to_arrow( self, file_name: str | Path | None = None, **write_file_kwargs, ) -> ArrowTable: """ Create a PyArrow Table from a Trajectory. Parameters ----------- file_name : str, .Path, or None (default) If not None, a file to write the parquet-format output to. Accepts any compression extension used by pyarrow.write_table **write_file_kwargs If file_name is not None, any kwargs to pass to pyarrow.parquet.write_file Returns ----------- pyarrow.Table """ pa_config = { k: pa.array([v]) for k, v in self.model_dump(mode="json").items() if hasattr(v, "__len__") } pa_table = pa.table(pa_config) if file_name: pa_pq.write_table(pa_table, file_name, **write_file_kwargs) return pa_table @classmethod def from_arrow(cls, pa_table: ArrowTable, identifier: str | None = None) -> Self: """ Create a trajectory from an arrow Table. Parameters ----------- file_name : str or .Path The parquet file to read from identifier : str or None (default) If not None, the identifier of the trajectory to return from a dataset of multiple trajectories. Returns ----------- Trajectory """ if identifier: pa_table = pa_table.filter( pa.compute.field("identifier") == identifier, null_selection_behavior="drop", ) config = { k: pa_table[k].to_pylist()[0] for k in pa_table.column_names if k in cls.model_fields } return cls(**config) @classmethod def from_parquet(cls, file_name: str | Path, identifier: str | None = None) -> Self: """Create a Trajectory from a parquet file. Parameters ----------- file_name : str | Path Path to the parquet file. Can be a remote path, e.g. AWS. identifier : str | None = None The string identifier for the task. """ kwargs = {} if identifier: kwargs["filters"] = [("identifier", "=", identifier)] return cls.from_arrow(pa_pq.read_table(file_name, **kwargs)) def to_pmg( self, frame_props: Iterable[str] | None = None, indices: int | slice | Iterable[int] | None = None, ) -> PmgTrajectory: """ Create a pymatgen Trajectory. Parameters ----------- frame_props : Sequence of str or None (default) If not None, a list of model fields to populate the frame properties of the pymatgen Trajectory with. If None, defaults to all available fields. If an empty iterable, no frame properties will be returned. indices : int, slice, iterable of int, or None If None (default), returns all frames If an int, the index of the frame to return If a slice, returns that range of frames If an iterable of int, returns those specific frames Frames are always returned in sequential order, even if the user-supplied indices are unsorted. Returns ----------- pymatgen.core.trajectory.Trajectory """ if frame_props is not None: frame_prop_keys: set[str] = set(frame_props).intersection( self.ionic_step_properties ) else: frame_prop_keys = self.ionic_step_properties if "magmoms" in frame_prop_keys: frame_prop_keys.discard("magmoms") if indices: if isinstance(indices, slice): indices = set(range(indices.start, indices.stop, indices.step or 1)) elif isinstance(indices, int): indices = {indices} else: indices = set(range(len(self))) species = [Element.from_Z(z) for z in self.elements] structures = [] frame_properties = [] for i in sorted({i for i in indices if self.cart_coords[i]}): site_properties = {} if magmoms := getattr(self, "magmoms", None): site_properties["magmoms"] = magmoms[i] if self.lattice: structure: Structure | Molecule = Structure( lattice=( self.lattice[0] if len(self.lattice) == 1 else self.lattice[i] # type: ignore[index] ), species=species, coords=self.cart_coords[i], # type: ignore[arg-type] coords_are_cartesian=True, site_properties=site_properties, ) else: structure = Molecule( species=species, coords=self.cart_coords[i], # type: ignore[arg-type] site_properties=site_properties, ) structures.append(structure) props = {} for k in frame_prop_keys: if _prop := getattr(self, k, []): prop = _prop[i] for cmth in ("model_dump", "tolist"): if hasattr(prop, cmth): prop = getattr(prop, cmth)() props[k] = prop frame_properties.append(props) if self.lattice: return PmgTrajectory.from_structures( structures, # type: ignore[arg-type] constant_lattice=len(self.lattice) == 1, frame_properties=frame_properties, ) return PmgTrajectory.from_molecules( structures, # type: ignore[arg-type] frame_properties=frame_properties, ) def to( self, file_name: str | Path | None = None, fmt: TrajFormat | str | None = None, **kwargs, ): """ Generic interface to multiple trajectory formats. file_name : str, .Path, or None (default) If not None, the name of the file to write to. If fmt is None, the file format will be inferred from this. fmt : TrajFormat, str, or None (default) The format of the output trajectory. If file_name is None, and fmt is None, defaults to a parquet-compatible PyArrow table. **kwargs Any kwargs supported by Trajectory.to_* methods. """ if file_name and not fmt: for _fmt in TrajFormat: # type: ignore[attr-defined] if _fmt.value in str(file_name).lower(): fmt = _fmt break elif not file_name and not fmt: fmt = TrajFormat.PARQUET if isinstance(fmt, str) and fmt.upper() in TrajFormat.__members__: fmt = TrajFormat[fmt.upper()] # type: ignore[misc] else: fmt = TrajFormat(fmt) if fmt in (TrajFormat.PMG, TrajFormat.ASE): traj = self.to_pmg(**kwargs) if fmt == TrajFormat.PMG: if file_name: dumpfn(traj, file_name) elif fmt == TrajFormat.ASE: if hasattr(PmgTrajectory, "to_ase"): traj = traj.to_ase(ase_traj_file=file_name) # type: ignore[attr-defined] else: raise ImportError( "Ensure you have pymatgen>=2025.1.23 to use ASE trajectory interfaces." ) elif fmt == TrajFormat.PARQUET: traj = self.to_arrow(file_name=file_name) return traj class AtomTrajectory(AtomRelaxTrajectory, _MDMixin): """Atomistic trajectory with extra keys for molecular dynamics runs.""" class RelaxTrajectory(AtomRelaxTrajectory): """Low memory schema for relaxation trajectories that can interface with parquet, pymatgen, and ASE. This schema is intended primarily for VASP relaxation trajectories, but could be adapted to generic electronic structure trajectories. Note that for VASP calculations, energies are in eV, forces in eV/Å, and stress tensors in kilobar. """ electronic_steps: list[list[ElectronicStep]] | None = Field( None, description="The electronic steps within a given ionic step." ) num_electronic_steps: list[int] | None = Field( None, description="The number of electronic steps within each ionic step." ) e_wo_entrp: list[float] | None = Field( None, description="The total energy in eV without electronic pseudoentropy from smearing of the Fermi surface.", ) e_fr_energy: list[float] | None = Field( None, description="The total electronic free energy in eV." ) identifier: str | None = Field( None, description="Identifier of this trajectory, e.g., task ID." ) task_type: TaskType | None = Field( None, description="The TaskType of the calculation used to generate this trajectory.", ) run_type: RunType | None = Field( None, description="The RunType of the calculation used to generate this trajectory.", ) ionic_step_properties: set[str] = Field( { "energy", "forces", "stress", "e_wo_entrp", "e_fr_energy", "electronic_steps", }, description="The properties included at each ionic step.", exclude=True, ) @property def has_full_output(self) -> bool: """Return true if a trajectory has all structures and SCF convergence info available.""" return all(coord_set is not None for coord_set in self.cart_coords) and all( conv_list is not None for conv_list in self.convergence_data.values() ) @property def convergence_data(self) -> dict[str, list[float] | None]: """Get convergence of energy and interatomic forces. If possible, energy convergence is taken at every electronic step. If not, it is taken at each ionic step (the final electronic step). Forces are taken at each ionic step. """ conv_data: dict[str, list[float] | None] = {} if self.electronic_steps is None: ionic_step_keys = ["energy", "e_fr_energy", "e_wo_entrp", "forces"] else: ionic_step_keys = ["forces"] flattened_e_steps = [] for e_step in self.electronic_steps: flattened_e_steps.extend(e_step) for k, remap in { "e_0_energy": "energy", "e_fr_energy": "e_fr_energy", "e_wo_entrp": "e_wo_entrp", }.items(): list_data = [getattr(e_step, k, None) for e_step in flattened_e_steps] if not all(list_data): ionic_step_keys.append(remap) continue data = np.array(list_data) conv_data[remap] = np.abs(data[-1] - data).tolist() for k in ionic_step_keys: if (list_data := getattr(self, k)) is None: conv_data[k] = None continue if k == "forces": list_data = [np.linalg.norm(f) for f in list_data] data = np.array(list_data) conv_data[k] = np.abs(data[-1] - data).tolist() return conv_data @classmethod def from_vasprun( cls, vasprun: Vasprun, store_electronic_steps: bool = True, **kwargs ) -> Self: """Create a Trajectory from a VASP vasprun.xml. Parameters ----------- vasprun : pymatgen Vasprun object store_electronic_steps : bool = True (default) Whether to store the electronic step information **kwargs : kwargs to pass to _from_dict / the document model """ ionic_step_data = { "structure", "e_fr_energy", "e_wo_entrp", "e_0_energy", "forces", "stress", } remap = {"e_0_energy": "energy"} if store_electronic_steps: ionic_step_data.add("electronic_steps") if ( vasprun.parameters.get("IBRION", -1) == 0 and "time_step" in cls.model_fields and not kwargs.get("time_step") ): kwargs["time_step"] = vasprun.parameters.get("POTIM") istep_attr = "md_data" if vasprun.incar.get("ML_LMLFF") else "ionic_steps" return cls._from_dict( { remap.get(k, k): [ ionic_step.get(k) for ionic_step in getattr(vasprun, istep_attr, []) ] for k in ionic_step_data }, **kwargs, ) @requires( pa is not None, message="pyarrow must be installed to de-/serialize to parquet" ) def to_arrow( self, file_name: str | Path | None = None, store_conv_data: bool = True, **write_file_kwargs, ) -> ArrowTable: """ Create a PyArrow Table from a Trajectory. Parameters ----------- file_name : str, .Path, or None (default) If not None, a file to write the parquet-format output to. Accepts any compression extension used by pyarrow.write_table store_conv_data : bool = True (default) Whether to store the data in `Trajectory.convergence_data`. Defaults to True to ensure that MP website convergence data is stored and easily accesible via parquet. **write_file_kwargs If file_name is not None, any kwargs to pass to pyarrow.parquet.write_file Returns ----------- pyarrow.Table """ pa_config = { k: pa.array([v]) for k, v in self.model_dump(mode="json").items() if hasattr(v, "__len__") } for k in ( "run_type", "task_type", ): if val := getattr(self, k): pa_config[k] = pa.array([val.value]) if store_conv_data: for k, v in self.convergence_data.items(): pa_config[f"{k}_convergence"] = pa.array([v]) pa_config["has_full_output"] = [self.has_full_output] pa_table = pa.table(pa_config) if file_name: pa_pq.write_table(pa_table, file_name, **write_file_kwargs) return pa_table class Trajectory(RelaxTrajectory, _MDMixin): """Trajectory with flexibility for electronic structure molecular dynamics.""" ././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1787011028.977401 emmet_core-0.87.2/emmet/core/types/0000755000175100017510000000000015240717725016612 5ustar00runnerrunner././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1787011023.826609 emmet_core-0.87.2/emmet/core/types/__init__.py0000644000175100017510000000000015240717720020704 0ustar00runnerrunner././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1787011023.827321 emmet_core-0.87.2/emmet/core/types/electronic_structure.py0000644000175100017510000000472715240717720023440 0ustar00runnerrunnerfrom pydantic import BaseModel, Field, model_validator from emmet.core.types.pymatgen_types.bandstructure_symm_line_adapter import ( BandStructureSymmLineType, ) from emmet.core.types.pymatgen_types.dos_adapter import CompleteDosType from emmet.core.types.typing import IdentifierType from emmet.core.utils import arrow_incompatible lmaxmix = int @arrow_incompatible class DosShim(BaseModel): """Light wrapper around DOS data - useful for static analysis and runtime safety""" dos: tuple[IdentifierType, CompleteDosType, lmaxmix] = Field( ..., description="Tuple of a calculation (task) ID, a CompleteDos object, and lmaxmix from the calculation.", ) @arrow_incompatible class BSShim(BaseModel): """ Light wrapper around bandstructure data - useful for static analysis and runtime safety At least one field must be populated with bandstructure data. """ setyawan_curtarolo: ( tuple[IdentifierType, BandStructureSymmLineType, lmaxmix] | None ) = Field( None, description=""" Tuple of a calculation (task) ID, a BandStructureSymmLine object from a calculation run using the Setyawan-Curtarolo k-path convention, and lmaxmix from the calculation. """, ) hinuma: tuple[IdentifierType, BandStructureSymmLineType, lmaxmix] | None = Field( None, description=""" Tuple of a calculation (task) ID, a BandStructureSymmLine object from a calculation run using the Hinuma et al. k-path convention, and lmaxmix from the calculation. """, ) latimer_munro: tuple[IdentifierType, BandStructureSymmLineType, lmaxmix] | None = ( Field( None, description=""" Tuple of a calculation (task) ID, a BandStructureSymmLine object from a calculation run using the Latimer-Munro et al. k-path convention, and lmaxmix from the calculation. """, ) ) @model_validator(mode="after") def _has_at_least_one_bandstructure(self): has_setyawan_curtarolo = self.setyawan_curtarolo is not None has_hinuma = self.hinuma is not None has_latimer_munro = self.latimer_munro is not None if not (has_setyawan_curtarolo or has_hinuma or has_latimer_munro): raise ValueError()( "At least one bandstructure type ('setyawan_curtarolo', 'hinuma', or 'latimer_munro') must be populated" ) return self ././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1787011023.827321 emmet_core-0.87.2/emmet/core/types/enums.py0000644000175100017510000001515315240717720020313 0ustar00runnerrunner"""Define enums with custom de-/serialization behavior. Note that only the base classes for enums and enums that are used widely across the code base should be put here. For example, `ValueEnum` is defined here, with custom pydantic serialization features. Similarly, `TaskState` is used across many sub-modules in `emmet-core` and therefore is defined here. Enums which only have one purpose / exist only within one module, can and should remain in that module. """ from __future__ import annotations from enum import Enum from itertools import product from typing import TYPE_CHECKING from pydantic_core import CoreSchema, core_schema from emmet.core.io.pymatgen import Element if TYPE_CHECKING: from typing import Any from pydantic import GetCoreSchemaHandler, GetJsonSchemaHandler from typing_extensions import Self class ValueEnum(Enum): """ Enum that serializes to string as the value. While this method has an `as_dict` method, this returns a `str`. This is to ensure deserialization to a `str` when functions like `monty.json.jsanitize` are called on a ValueEnum with `strict = True` and `enum_values = False` (occurs often in jobflow). """ def __str__(self): return str(self.value) def __eq__(self, obj: object) -> bool: """Special Equals to enable converting strings back to the enum""" if isinstance(obj, str): return super().__eq__(self.__class__(obj)) elif isinstance(obj, self.__class__): return super().__eq__(obj) return False def __hash__(self): """Get a hash of the enum.""" return hash(str(self)) @classmethod def __get_pydantic_core_schema__( cls, _source_type: Any, _handler: GetCoreSchemaHandler ) -> core_schema.CoreSchema: """Ensure pydantic (de)serialization.""" from_str_schema = core_schema.chain_schema( [ core_schema.str_schema(), core_schema.with_info_plain_validator_function(cls.validate), ] ) return core_schema.json_or_python_schema( json_schema=from_str_schema, python_schema=from_str_schema, serialization=core_schema.plain_serializer_function_ser_schema( lambda instance: instance.value ), ) @classmethod def __get_pydantic_json_schema__( cls, _core_schema: CoreSchema, handler: GetJsonSchemaHandler ) -> dict[str, Any]: json_schema = handler(core_schema.str_schema()) json_schema = handler.resolve_ref_schema(json_schema) return json_schema @classmethod def validate(cls, __input_value: Any, _: core_schema.ValidationInfo) -> Self: """Define pydantic validator for emmet enums.""" if isinstance(__input_value, str): return ( cls[__input_value] if __input_value in cls.__members__ else cls(__input_value) ) elif isinstance(__input_value, ValueEnum): return __input_value # type: ignore[return-value] raise ValueError(f"Invalid {cls.__name__}: {__input_value}") class DocEnum(ValueEnum): """ Enum with docstrings support from: https://stackoverflow.com/a/50473952 """ def __new__(cls, value, doc=None): """add docstring to the member of Enum if exists Args: value: Enum member value doc: Enum member docstring, None if not exists """ self = object.__new__(cls) # calling super().__new__(value) here would fail self._value_ = value if doc is not None: self.__doc__ = doc return self class IgnoreCaseEnum(ValueEnum): """Enum that permits case-insensitve lookup. Reference issue: https://github.com/materialsproject/api/issues/869 """ @classmethod def _missing_(cls, value): for member in cls: if member.value.upper() == value.upper(): return member class VaspObject(ValueEnum): """Types of VASP data objects.""" BANDSTRUCTURE = "bandstructure" DOS = "dos" CHGCAR = "chgcar" AECCAR0 = "aeccar0" AECCAR1 = "aeccar1" AECCAR2 = "aeccar2" TRAJECTORY = "trajectory" ELFCAR = "elfcar" WAVECAR = "wavecar" LOCPOT = "locpot" OPTIC = "optic" PROCAR = "procar" class StoreTrajectoryOption(ValueEnum): FULL = "full" PARTIAL = "partial" NO = "no" class TaskState(ValueEnum): """ VASP Calculation State """ SUCCESS = "successful" FAILED = "failed" ERROR = "error" @classmethod def _missing_(cls, value): if isinstance(value, bool): return cls.SUCCESS if value else cls.FAILED super()._missing_(value) class DeprecationMessage(DocEnum): MANUAL = "M", "Manual deprecation" SYMMETRY = ( "S001", "Could not determine crystalline space group, needed for input set check.", ) KPTS = "C001", "Too few KPoints" KSPACING = "C002", "KSpacing not high enough" ENCUT = "C002", "ENCUT too low" FORCES = "C003", "Forces too large" MAG = "C004", "At least one site magnetization is too large" POTCAR = ( "C005", "At least one POTCAR used does not agree with the pymatgen input set", ) CONVERGENCE = "E001", "Calculation did not converge" MAX_SCF = "E002", "Max SCF gradient too large" LDAU = "I001", "LDAU Parameters don't match the inputset" SET = ("I002", "Cannot validate due to missing or problematic input set") UNKNOWN = "U001", "Cannot validate due to unknown calc type" class BatteryType(str, ValueEnum): """ Enum for battery type """ insertion = "insertion" conversion = "conversion" class ThermoType(ValueEnum): """Thermodynamic hull types used in the database.""" GGA_GGA_U = "GGA_GGA+U" GGA_GGA_U_R2SCAN = "GGA_GGA+U_R2SCAN" R2SCAN = "R2SCAN" UNKNOWN = "UNKNOWN" class XasEdge(ValueEnum): """ The interaction edge for XAS There are 2n-1 sub-components to each edge where K: n=1 L: n=2 M: n=3 N: n=4 """ K = "K" L2 = "L2" L3 = "L3" L2_3 = "L2,3" class XasType(ValueEnum): """ The type of XAS Spectrum XANES - Just the near-edge region EXAFS - Just the extended region XAFS - Fully stitched XANES + EXAFS """ XANES = "XANES" EXAFS = "EXAFS" XAFS = "XAFS" XasLabel = ValueEnum( # type: ignore[call-arg] "XasLabel", { "_".join(x.value for x in v): "-".join(x.value for x in v) for v in product(XasType, Element, XasEdge) }, ) """High combinatorial label for XAS data.""" ././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1787011028.982697 emmet_core-0.87.2/emmet/core/types/pymatgen_types/0000755000175100017510000000000015240717725021662 5ustar00runnerrunner././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1787011023.827321 emmet_core-0.87.2/emmet/core/types/pymatgen_types/__init__.py0000644000175100017510000000000015240717720023754 0ustar00runnerrunner././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8274822 emmet_core-0.87.2/emmet/core/types/pymatgen_types/alloy_adapter.py0000644000175100017510000001373515240717720025060 0ustar00runnerrunnerfrom typing import Annotated, Any, TypeVar from pydantic import ( BaseModel, BeforeValidator, Field, TypeAdapter, WrapSerializer, model_validator, ) from emmet.core.io.pymatgen import Element from typing_extensions import NotRequired, TypedDict from emmet.core.types.pymatgen_types.structure_adapter import ( TypedStructureDict, pop_empty_structure_keys, ) from emmet.core.types.typing import MaterialIdentifierType try: from emmet.core.io.pymatgen import AlloyMember, AlloyPair, AlloySystem except ImportError: raise ImportError( "Install pymatgen-analysis-alloys to use AlloyPairDoc or AlloySystemDoc" ) class PairID(BaseModel): id_a: MaterialIdentifierType id_b: MaterialIdentifierType separator: str = Field(default="_") @model_validator(mode="before") def validate_string(cls, data: Any) -> Any: if isinstance(data, str): sep = cls.model_fields["separator"].default parts = data.split(sep, 1) return {"id_a": parts[0], "id_b": parts[1]} return data def __str__(self) -> str: """Format as a string.""" return self.separator.join([self.id_a.string, self.id_b.string]) def __repr__(self) -> str: """Format display name.""" return f"{self.__class__.__name__}({self})" class TypedSupportedPropertiesDict(TypedDict): energy_above_hull: NotRequired[float | None] formation_energy_per_atom: NotRequired[float | None] band_gap: NotRequired[float | None] is_gap_direct: NotRequired[bool | None] m_n: NotRequired[float | None] m_p: NotRequired[float | None] theoretical: NotRequired[bool | None] is_metal: NotRequired[bool | None] class TypedAlloyMemberDict(TypedDict): id_: NotRequired[MaterialIdentifierType | None] db: NotRequired[str | None] composition: NotRequired[dict[Element, float] | None] x: NotRequired[float | None] is_ordered: NotRequired[bool | None] TypedAlloyPairDict = TypedDict( "TypedAlloyPairDict", { "@module": str, "@class": str, "@version": NotRequired[str | None], "formula_a": NotRequired[str | None], "formula_b": NotRequired[str | None], "structure_a": NotRequired[TypedStructureDict | None], "structure_b": NotRequired[TypedStructureDict | None], "id_a": NotRequired[MaterialIdentifierType | None], "id_b": NotRequired[MaterialIdentifierType | None], "chemsys": NotRequired[str | None], "alloying_element_a": NotRequired[str | None], "alloying_element_b": NotRequired[str | None], "alloying_species_a": NotRequired[str | None], "alloying_species_b": NotRequired[str | None], "observer_elements": NotRequired[list[str] | None], "observer_species": NotRequired[list[str] | None], "anions_a": NotRequired[list[str] | None], "anions_b": NotRequired[list[str] | None], "cations_a": NotRequired[list[str] | None], "cations_b": NotRequired[list[str] | None], "lattice_parameters_a": NotRequired[list[float] | None], "lattice_parameters_b": NotRequired[list[float] | None], "volume_cube_root_a": NotRequired[float | None], "volume_cube_root_b": NotRequired[float | None], "properties_a": NotRequired[TypedSupportedPropertiesDict | None], "properties_b": NotRequired[TypedSupportedPropertiesDict | None], "spacegroup_intl_number_a": NotRequired[int | None], "spacegroup_intl_number_b": NotRequired[int | None], "pair_id": NotRequired[PairID | None], "pair_formula": NotRequired[str | None], "alloy_oxidation_state": NotRequired[float | None], "isoelectronic": NotRequired[bool | None], "anonymous_formula": NotRequired[str | None], "nelements": NotRequired[int | None], "members": NotRequired[list[TypedAlloyMemberDict] | None], }, ) TypedAlloySystemDict = TypedDict( "TypedAlloySystemDict", { "@class": str, "@module": str, "@version": NotRequired[str | None], "alloy_id": NotRequired[str | None], "alloy_pairs": list[TypedAlloyPairDict], "chemsys": NotRequired[str | None], "chemsys_size": NotRequired[int | None], "has_members": NotRequired[bool | None], "members": NotRequired[list[TypedAlloyMemberDict] | None], "additional_members": NotRequired[list[TypedAlloyMemberDict] | None], "n_pairs": NotRequired[int | None], "ids": NotRequired[list[str] | None], "pair_ids": NotRequired[list[PairID] | None], }, ) AlloyMemberTypeVar = TypeVar("AlloyMemberTypeVar", AlloyMember, TypedAlloyMemberDict) AlloyPairTypeVar = TypeVar("AlloyPairTypeVar", AlloyPair, TypedAlloyPairDict) AlloySystemTypeVar = TypeVar("AlloySystemTypeVar", AlloySystem, TypedAlloySystemDict) def pop_empty_alloy_pair_structure_keys(alloy_pair: AlloyPairTypeVar): if isinstance(alloy_pair, dict): for key in ["structure_a", "structure_b"]: alloy_pair[key] = pop_empty_structure_keys(alloy_pair[key], serialize=False) # type: ignore[literal-required] return AlloyPair.from_dict( TypeAdapter(TypedAlloyPairDict).validate_python(alloy_pair) ) def pop_empty_alloy_system_structure_keys(alloy_system: AlloySystemTypeVar): if isinstance(alloy_system, dict): alloy_system["alloy_pairs"] = [ pop_empty_alloy_pair_structure_keys(alloy_pair) for alloy_pair in alloy_system["alloy_pairs"] ] return AlloySystem.from_dict( TypeAdapter(TypedAlloySystemDict).validate_python(alloy_system) ) AlloyPairType = Annotated[ AlloyPairTypeVar, BeforeValidator(pop_empty_alloy_pair_structure_keys), WrapSerializer(lambda x, nxt, info: x.as_dict(), return_type=TypedAlloyPairDict), ] AlloySystemType = Annotated[ AlloySystemTypeVar, BeforeValidator(pop_empty_alloy_system_structure_keys), WrapSerializer(lambda x, nxt, info: x.as_dict(), return_type=TypedAlloySystemDict), ] ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8274822 emmet_core-0.87.2/emmet/core/types/pymatgen_types/bader.py0000644000175100017510000000124515240717720023306 0ustar00runnerrunner"""Annotate Bader charge/spin analysis output types used in pymatgen.""" from pydantic import BaseModel class BaderAnalysis(BaseModel): """Output of pymatgen.command_line.bader_caller.bader_analysis_from_objects We omit the `charge_densities` field, since these are too large to justify storing in the document model. Charge densities can already be stored in TaskDoc.vasp_objects """ min_dist: list[float] charge: list[float] atomic_volume: list[float] vacuum_charge: float vacuum_volume: float reference_used: bool bader_version: float charge_transfer: list[float] | None = None magmom: list[float] | None = None ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8274822 emmet_core-0.87.2/emmet/core/types/pymatgen_types/balanced_reaction_adapter.py0000644000175100017510000000142415240717720027345 0ustar00runnerrunnerfrom typing import Annotated, TypeVar from pydantic import BeforeValidator, WrapSerializer from emmet.core.io.pymatgen import BalancedReaction from typing_extensions import TypedDict TypedBalancedReactionDict = TypedDict( "TypedBalancedReactionDict", { "@module": str, "@class": str, "reactants": dict[str, float], "products": dict[str, float], }, ) BalancedReactionTypeVar = TypeVar( "BalancedReactionTypeVar", BalancedReaction, TypedBalancedReactionDict ) BalancedReactionType = Annotated[ BalancedReactionTypeVar, BeforeValidator( lambda x: BalancedReaction.from_dict(x) if isinstance(x, dict) else x ), WrapSerializer( lambda x, nxt, info: x.as_dict(), return_type=TypedBalancedReactionDict ), ] ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8274822 emmet_core-0.87.2/emmet/core/types/pymatgen_types/bandstructure_symm_line_adapter.py0000644000175100017510000000422515240717720030673 0ustar00runnerrunnerfrom typing import Annotated, TypeVar from pydantic import BeforeValidator, WrapSerializer from emmet.core.io.pymatgen import BandStructureSymmLine from typing_extensions import TypedDict from emmet.core.types.pymatgen_types.kpoint_adapter import KpointType from emmet.core.types.pymatgen_types.lattice_adapter import MSONableTypedLatticeDict from emmet.core.types.pymatgen_types.structure_adapter import ( TypedStructureDict, pop_empty_structure_keys, ) class TypedBandGapDict(TypedDict): direct: bool transition: str energy: float class TypedBranchDict(TypedDict): start_index: int end_index: int name: str class TypedBandDict(TypedDict): """Type def for data stored for cbms or vbms""" band_index: dict[str, list[int]] kpoint_index: list[int] kpoint: KpointType energy: float projections: dict[str, list[list[float]]] TypedBandDictureSymmLineDict = TypedDict( "TypedBandDictureSymmLineDict", { "@module": str, "@class": str, "lattice_rec": MSONableTypedLatticeDict, "efermi": float, "kpoints": list[list[float, float, float]], # type: ignore[type-arg] "bands": dict[str, list[list[float]]], "is_metal": bool, "vbm": TypedBandDict, "cbm": TypedBandDict, "band_gap": TypedBandGapDict, "labels_dict": dict[str, list[float]], "is_spin_polarized": bool, "projections": dict[str, list[list[list[list[float]]]]], "structure": TypedStructureDict, "branches": list[TypedBranchDict], }, ) BandStructureSymmLineTypeVar = TypeVar( "BandStructureSymmLineTypeVar", BandStructureSymmLine, TypedBandDictureSymmLineDict, ) def pop_empty_bs_keys(bs: BandStructureSymmLineTypeVar): if isinstance(bs, dict): bs["structure"] = pop_empty_structure_keys(bs["structure"]) return BandStructureSymmLine.from_dict(bs) # type: ignore[arg-type] return bs BandStructureSymmLineType = Annotated[ BandStructureSymmLineTypeVar, BeforeValidator(pop_empty_bs_keys), WrapSerializer( lambda x, nxt, info: x.as_dict(), return_type=TypedBandDictureSymmLineDict ), ] ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8274822 emmet_core-0.87.2/emmet/core/types/pymatgen_types/compatibility_adapter.py0000644000175100017510000000257415240717720026610 0ustar00runnerrunnerfrom typing import TypeVar from emmet.core.io.pymatgen import ( Compatibility, MaterialsProject2020Compatibility, MaterialsProjectAqueousCompatibility, ) from typing_extensions import TypedDict TypedCompatibilityDict = TypedDict( "TypedCompatibilityDict", { "@module": str, "@class": str, "@version": str, }, ) class TypedMaterialsProject2020CompatibilityAdapterDict(TypedCompatibilityDict): """ Does not define an as_dict() method, parent(Compatibility) is abstract so Monty just serializes to module, class, and version. """ pass class TypedMaterialsProjectAqueousCompatibilityAdapterDict(TypedCompatibilityDict): """ Does not define an as_dict() method, parent(Compatibility) is abstract so Monty just serializes to module, class, and version. """ pass CompatibilityTypeVar = TypeVar( "CompatibilityTypeVar", Compatibility, TypedCompatibilityDict ) MaterialsProjectAqueousCompatibilityAdapterDictTypeVar = TypeVar( "MaterialsProjectAqueousCompatibilityAdapterDictTypeVar", MaterialsProjectAqueousCompatibility, TypedMaterialsProjectAqueousCompatibilityAdapterDict, ) MaterialsProject2020CompatibilityAdapterDictTypeVar = TypeVar( "MaterialsProject2020CompatibilityAdapterDictTypeVar", MaterialsProject2020Compatibility, TypedMaterialsProject2020CompatibilityAdapterDict, ) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8274822 emmet_core-0.87.2/emmet/core/types/pymatgen_types/composition_adapter.py0000644000175100017510000000121215240717720026266 0ustar00runnerrunnerfrom typing import Annotated, TypeVar from pydantic import BeforeValidator, WrapSerializer from emmet.core.io.pymatgen import Composition CompositionTypeVar = TypeVar("CompositionTypeVar", Composition, dict[str, float]) def composition_deserializer(comp): if isinstance(comp, list): comp = {k: v for k, v in comp} if isinstance(comp, dict): return Composition.from_dict(comp) return comp CompositionType = Annotated[ CompositionTypeVar, BeforeValidator(lambda x: Composition.from_dict(x) if isinstance(x, dict) else x), WrapSerializer(lambda x, nxt, info: x.as_dict(), return_type=dict[str, float]), ] ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8274822 emmet_core-0.87.2/emmet/core/types/pymatgen_types/computed_entries_adapter.py0000644000175100017510000001703315240717720027304 0ustar00runnerrunnerfrom typing import Annotated, Any, TypeVar import orjson from pydantic import ( BaseModel, BeforeValidator, Field, TypeAdapter, WrapSerializer, model_validator, ) from emmet.core.io.pymatgen import ComputedEntry, ComputedStructureEntry from typing_extensions import NotRequired, TypedDict from emmet.core.types.enums import ThermoType from emmet.core.types.pymatgen_types.element_adapter import ElementType from emmet.core.types.pymatgen_types.structure_adapter import ( StructureType, pop_empty_structure_keys, ) from emmet.core.types.typing import IdentifierType, MaterialIdentifierType from emmet.core.utils import type_override from emmet.core.vasp.calc_types.enums import RunType from emmet.core.vasp.calculation import PotcarSpec @type_override({"suffix": ThermoType}) class EntryID(BaseModel): identifier: MaterialIdentifierType suffix: RunType | ThermoType separator: str = Field(default="-") @model_validator(mode="before") def validate_string(cls, data: Any) -> Any: if isinstance(data, str): sep = cls.model_fields["separator"].default parts = data.rsplit(sep, 1) return {"identifier": parts[0], "suffix": parts[1]} return data def __repr__(self) -> str: return f"EntryID(identifier={repr(self.identifier)}, suffix={repr(self.suffix)}, separator='{self.separator}')" def __str__(self) -> str: return self.separator.join([self.identifier.string, self.suffix.value]) def __eq__(self, other) -> bool: if other is None: return False if isinstance(other, EntryID): return ( self.identifier == other.identifier and self.suffix == other.suffix and self.separator == other.separator ) if isinstance(other, str): return str(self) == other raise NotImplementedError(f"Cannot compare EntryID with {type(other)}") def __hash__(self): return hash(str(self)) # TypedEnergyAdjustmentDict = TypedDict( # "TypedEnergyAdjustmentDict", # { # "@module": str, # "@class": str, # "@version": str, # "value": float, # "uncertainty": float, # "name": str, # "cls": MaterialsProject2020Compatibility | MaterialsProjectDFTMixingScheme, # "description": str, # }, # ) # TypedCompositionEnergyAdjustmentDict = TypedDict( # "TypedCompositionEnergyAdjustmentDict", # { # "@module": str, # "@class": str, # "@version": str, # "adj_per_atom": float, # "n_atoms": int, # "uncertainty_per_atom": float, # "name": str, # "cls": MaterialsProject2020Compatibility | MaterialsProjectDFTMixingScheme, # "description": str, # }, # ) # TypedTemperatureEnergyAdjustmentDict = TypedDict( # "TypedTemperatureEnergyAdjustmentDict", # { # "@module": str, # "@class": str, # "@version": str, # "adj_per_deg": float, # "temp": float, # "n_atoms": int, # "uncertainty_per_deg": float, # "name": str, # "cls": MaterialsProject2020Compatibility | MaterialsProjectDFTMixingScheme, # "description": str, # }, # ) class TypedCEDataDict(TypedDict): oxide_type: NotRequired[str | None] aspherical: NotRequired[bool | None] last_updated: NotRequired[str | None] task_id: NotRequired[IdentifierType | None] material_id: NotRequired[MaterialIdentifierType | None] oxidation_states: NotRequired[dict[ElementType, float] | None] license: NotRequired[str | None] run_type: NotRequired[str | None] class TypedCEParameterDict(TypedDict): potcar_spec: NotRequired[list[PotcarSpec] | None] run_type: NotRequired[str | None] is_hubbard: NotRequired[bool | None] hubbards: NotRequired[dict[str, float] | None] # type: ignore[type-arg] # Used for running deserialization not dependent on # type of energy_adjustments _TypedComputedEntryDict = TypedDict( # type: ignore[name-match] "_TypedComputedEntryDict", { "@module": str, "@class": str, "energy": float, "composition": dict[ElementType, float], "entry_id": NotRequired[EntryID | None], "correction": NotRequired[float | None], "parameters": NotRequired[TypedCEParameterDict | None], "data": NotRequired[TypedCEDataDict | None], }, ) class TypedComputedEntryDict(_TypedComputedEntryDict): # energy_adjustments: list[ # TypedCompositionEnergyAdjustmentDict # | TypedEnergyAdjustmentDict # | TypedTemperatureEnergyAdjustmentDict # ] energy_adjustments: NotRequired[str | None] class TypedComputedStructureEntryDict(TypedComputedEntryDict): structure: StructureType ComputedEntryTypeVar = TypeVar( "ComputedEntryTypeVar", ComputedEntry, TypedComputedEntryDict, ) ComputedStructureEntryTypeVar = TypeVar( "ComputedStructureEntryTypeVar", ComputedStructureEntry, TypedComputedStructureEntryDict, ) def entry_serializer(entry, nxt, info) -> dict[str, Any]: # need to beat pmg serialization to get correct (material/task/entry)_id serialization entry.data = TypeAdapter(TypedCEDataDict).dump_python(entry.data) default_serialized_object = nxt(entry.as_dict(), info) format = info.context.get("format") if info.context else None if format == "arrow": default_serialized_object["energy_adjustments"] = orjson.dumps( default_serialized_object["energy_adjustments"] ) return default_serialized_object def pop_cse_empty_keys(cse: dict) -> dict[str, Any]: if cse.get("structure"): cse["structure"] = pop_empty_structure_keys(cse["structure"]) cse["data"] = {k: v for k, v in cse["data"].items() if v is not None} # type: ignore[typeddict-item] cse["parameters"] = {k: v for k, v in cse["parameters"].items() if v} # type: ignore[typeddict-item] return cse def entry_deserializer(entry: dict[str, Any] | ComputedEntry | ComputedStructureEntry): if isinstance(entry, dict): entry_dict: dict[str, Any] = TypeAdapter( # type: ignore[assignment] _TypedComputedEntryDict ).validate_python(entry, extra="allow") entry_cls: type[ComputedEntry | ComputedStructureEntry] entry_type: type[TypedComputedEntryDict | TypedComputedStructureEntryDict] match entry_dict["@class"]: case "ComputedEntry": entry_cls = ComputedEntry entry_type = TypedComputedEntryDict case "ComputedStructureEntry": entry_cls = ComputedStructureEntry entry_type = TypedComputedStructureEntryDict entry_dict = pop_cse_empty_keys(entry_dict) if "energy_adjustments" in entry_dict and any( [ isinstance(entry_dict["energy_adjustments"], _type) for _type in (str, bytes) ] ): entry_dict = TypeAdapter(entry_type).validate_python(entry_dict) entry_dict["energy_adjustments"] = orjson.loads( entry_dict["energy_adjustments"] ) return entry_cls.from_dict(entry_dict) return entry ComputedEntryType = Annotated[ ComputedEntryTypeVar, BeforeValidator(entry_deserializer), WrapSerializer(entry_serializer, return_type=dict[str, Any]), ] ComputedStructureEntryType = Annotated[ ComputedStructureEntryTypeVar, BeforeValidator(entry_deserializer), WrapSerializer(entry_serializer, return_type=dict[str, Any]), ] ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8274822 emmet_core-0.87.2/emmet/core/types/pymatgen_types/defect_adapter.py0000644000175100017510000000247315240717720025167 0ustar00runnerrunnerfrom typing import Annotated, TypeVar from monty.json import MontyDecoder from pydantic import BeforeValidator, WrapSerializer from emmet.core.io.pymatgen import Defect from typing_extensions import TypedDict from emmet.core.types.pymatgen_types.sites_adapter import MSONableTypedSiteDict from emmet.core.types.pymatgen_types.structure_adapter import ( TypedStructureDict, pop_empty_structure_keys, ) TypedDefectDict = TypedDict( "TypedDefectDict", { "@module": str, "@class": str, "@version": str, "structure": TypedStructureDict, "site": MSONableTypedSiteDict, "multiplicity": int, "oxi_state": float, "equivalent_sites": list[MSONableTypedSiteDict], "symprec": float, "angle_tolerance": float, "user_changes": list[int], }, ) DefectTypeVar = TypeVar("DefectTypeVar", Defect, TypedDefectDict) def pop_defect_empty_structure_fields(defect: DefectTypeVar): if isinstance(defect, dict): defect["structure"] = pop_empty_structure_keys(defect["structure"]) return MontyDecoder().process_decoded(defect) return defect DefectType = Annotated[ DefectTypeVar, BeforeValidator(pop_defect_empty_structure_fields), WrapSerializer(lambda x, nxt, info: x.as_dict(), return_type=TypedDefectDict), ] ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8274822 emmet_core-0.87.2/emmet/core/types/pymatgen_types/diffraction_pattern_adapter.py0000644000175100017510000000164515240717720027762 0ustar00runnerrunnerfrom typing import Annotated, TypeVar from pydantic import BeforeValidator, WrapSerializer from emmet.core.io.pymatgen import DiffractionPattern from typing_extensions import TypedDict class TypedHKLDict(TypedDict): hkl: list[int] multiplicity: int TypedDiffractionPatternDict = TypedDict( "TypedDiffractionPatternDict", { "@module": str, "@class": str, "x": list[float], "y": list[float], "hkls": list[list[TypedHKLDict]], "d_hkls": list[float], }, ) DiffractionPatternTypeVar = TypeVar( "DiffractionPatternTypeVar", DiffractionPattern, TypedDiffractionPatternDict ) DiffractionPatternType = Annotated[ DiffractionPatternTypeVar, BeforeValidator( lambda x: DiffractionPattern.from_dict(x) if isinstance(x, dict) else x ), WrapSerializer( lambda x, nxt, info: x.as_dict(), return_type=TypedDiffractionPatternDict ), ] ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8274822 emmet_core-0.87.2/emmet/core/types/pymatgen_types/dos_adapter.py0000644000175100017510000000264715240717720024525 0ustar00runnerrunnerfrom typing import Annotated, TypeVar from pydantic import BeforeValidator, WrapSerializer from emmet.core.io.pymatgen import CompleteDos from typing_extensions import TypedDict from emmet.core.types.pymatgen_types.structure_adapter import ( TypedStructureDict, pop_empty_structure_keys, ) TypedDosDict = TypedDict( "TypedDosDict", { "@module": str, "@class": str, "efermi": float, "energies": list[float], "densities": dict[str, list[float]], }, ) TypedCompleteDosDict = TypedDict( "TypedCompleteDosDict", { "@module": str, "@class": str, "efermi": float, "structure": TypedStructureDict, "energies": list[float], "densities": dict[str, list[float]], "pdos": list[dict[str, dict[str, dict[str, list[float]]]]], "atom_dos": dict[str, TypedDosDict], "spd_dos": dict[str, TypedDosDict], }, ) CompleteDosTypeVar = TypeVar("CompleteDosTypeVar", CompleteDos, TypedCompleteDosDict) def pop_empty_dos_keys(dos: CompleteDosTypeVar): if isinstance(dos, dict): dos["structure"] = pop_empty_structure_keys(dos["structure"]) return CompleteDos.from_dict(dos) # type: ignore[arg-type] return dos CompleteDosType = Annotated[ CompleteDosTypeVar, BeforeValidator(pop_empty_dos_keys), WrapSerializer(lambda x, nxt, info: x.as_dict(), return_type=TypedCompleteDosDict), ] ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8274822 emmet_core-0.87.2/emmet/core/types/pymatgen_types/electrode_adapter.py0000644000175100017510000002377415240717720025712 0ustar00runnerrunnerfrom collections.abc import Callable from itertools import chain from typing import Annotated, Any, TypeVar import orjson from pydantic import BeforeValidator, TypeAdapter, WrapSerializer from emmet.core.io.pymatgen import ( ConversionElectrode, ConversionVoltagePair, InsertionElectrode, InsertionVoltagePair, ) from typing_extensions import TypedDict from emmet.core.types.enums import BatteryType from emmet.core.types.pymatgen_types.balanced_reaction_adapter import ( TypedBalancedReactionDict, ) from emmet.core.types.pymatgen_types.computed_entries_adapter import ( TypedCEDataDict, TypedComputedEntryDict, TypedComputedStructureEntryDict, pop_cse_empty_keys, ) _MSONables = TypedDict( "_MSONables", { "@module": str, "@class": str, }, ) class BaseVoltagePairDict(_MSONables): voltage: float frac_charge: float frac_discharge: float framework_formula: str mAh: float mass_charge: float mass_discharge: float vol_charge: float vol_discharge: float class TypedInsertionVoltagePairDict(BaseVoltagePairDict): working_ion_entry: TypedComputedEntryDict entry_charge: TypedComputedStructureEntryDict entry_discharge: TypedComputedStructureEntryDict TypedInsertionElectrodeDict = TypedDict( "TypedInsertionElectrodeDict", { "@module": str, "@class": str, "framework_formula": str, "stable_entries": list[TypedComputedStructureEntryDict], "unstable_entries": list[TypedComputedStructureEntryDict], "voltage_pairs": list[TypedInsertionVoltagePairDict], "working_ion_entry": TypedComputedEntryDict, }, ) InsertionElectrodeTypeVar = TypeVar( "InsertionElectrodeTypeVar", InsertionElectrode, TypedInsertionElectrodeDict, ) class TypedConversionVoltagePairDict(BaseVoltagePairDict): working_ion_entry: TypedComputedStructureEntryDict entries_charge: list[TypedComputedStructureEntryDict] entries_discharge: list[TypedComputedStructureEntryDict] rxn: TypedBalancedReactionDict TypedConversionElectrodeDict = TypedDict( "TypedConversionElectrodeDict", { "@module": str, "@class": str, "framework_formula": str, "initial_comp_formula": str, "voltage_pairs": list[TypedConversionVoltagePairDict], "working_ion_entry": TypedComputedStructureEntryDict, }, ) ConversionElectrodeTypeVar = TypeVar( "ConversionElectrodeTypeVar", ConversionElectrode, TypedConversionElectrodeDict, ) def walk_voltage_pairs(voltage_pairs: list[dict[str, Any]], battery_type: BatteryType): pair: dict[str, Any] voltage_pair_cls: type[InsertionVoltagePair | ConversionVoltagePair] match battery_type: case BatteryType.insertion: voltage_pair_cls = InsertionVoltagePair for pair in voltage_pairs: for key in ["entry_charge", "entry_discharge", "working_ion_entry"]: pair[key] = pop_cse_empty_keys(pair[key]) case BatteryType.conversion: voltage_pair_cls = ConversionVoltagePair for pair in voltage_pairs: for key in ["entries_charge", "entries_discharge"]: pair[key] = [pop_cse_empty_keys(cse) for cse in pair[key]] return [voltage_pair_cls.from_dict(vp) for vp in voltage_pairs] def electrode_object_energy_adjustments_serde( electrode_object: dict[str, Any], battery_type: BatteryType, serde_fn: Callable, ea="energy_adjustments", ): pair: dict[str, Any] electrode_object["working_ion_entry"][ea] = serde_fn( electrode_object["working_ion_entry"][ea] ) match battery_type: case BatteryType.insertion: for pair in electrode_object["voltage_pairs"]: for key in ["working_ion_entry", "entry_charge", "entry_discharge"]: pair[key][ea] = serde_fn(pair[key][ea]) for key in ["stable_entries", "unstable_entries"]: for entry in electrode_object[key]: entry[ea] = serde_fn(entry[ea]) case BatteryType.conversion: for pair in electrode_object["voltage_pairs"]: pair["working_ion_entry"][ea] = serde_fn(pair["working_ion_entry"][ea]) for key in ["entries_charge", "entries_discharge"]: for entry in pair[key]: entry[ea] = serde_fn(entry[ea]) def _serialize_entry_data_field(electrode_object, battery_type): match battery_type: case BatteryType.insertion: for entry in chain( electrode_object.stable_entries, electrode_object.unstable_entries ): entry.data = TypeAdapter(TypedCEDataDict).dump_python(entry.data) for pair in electrode_object.voltage_pairs: pair.working_ion_entry.data = TypeAdapter(TypedCEDataDict).dump_python( pair.working_ion_entry.data ) pair.entry_charge.data = TypeAdapter(TypedCEDataDict).dump_python( pair.entry_charge.data ) pair.entry_discharge.data = TypeAdapter(TypedCEDataDict).dump_python( pair.entry_discharge.data ) case BatteryType.conversion: for pair in electrode_object.voltage_pairs: pair.working_ion_entry.data = TypeAdapter(TypedCEDataDict).dump_python( pair.working_ion_entry.data ) for entry in chain(pair.entries_charge, pair.entries_discharge): entry.data = TypeAdapter(TypedCEDataDict).dump_python(entry.data) electrode_object.working_ion_entry.data = TypeAdapter(TypedCEDataDict).dump_python( electrode_object.working_ion_entry.data ) return electrode_object def electrode_object_serializer(electrode_object, nxt, info) -> dict[str, Any]: battery_type = ( BatteryType.insertion if electrode_object.__class__.__name__ == "InsertionElectrode" else BatteryType.conversion ) # need to beat pmg serialization to get correct (material/task/entry)_id serialization electrode_object = _serialize_entry_data_field(electrode_object, battery_type) default_serialized_object = nxt(electrode_object.as_dict(), info) format = info.context.get("format") if info.context else None if format == "arrow": electrode_object_energy_adjustments_serde( default_serialized_object, battery_type, orjson.dumps ) return default_serialized_object def _deserialize_electrode_object_entries( electrode_object, battery_type ) -> dict[str, Any]: match battery_type: case BatteryType.insertion: electrode_object["working_ion_entry"] = TypeAdapter( TypedComputedEntryDict ).validate_python(electrode_object["working_ion_entry"]) electrode_object["stable_entries"] = [ TypeAdapter(TypedComputedStructureEntryDict).validate_python(entry) for entry in electrode_object["stable_entries"] ] electrode_object["unstable_entries"] = [ TypeAdapter(TypedComputedStructureEntryDict).validate_python(entry) for entry in electrode_object["unstable_entries"] ] for pair in electrode_object["voltage_pairs"]: for key, _type in [ ("working_ion_entry", TypedComputedEntryDict), ("entry_charge", TypedComputedStructureEntryDict), ("entry_discharge", TypedComputedStructureEntryDict), ]: pair[key] = TypeAdapter(_type).validate_python(pair[key]) case BatteryType.conversion: electrode_object["working_ion_entry"] = TypeAdapter( TypedComputedStructureEntryDict ).validate_python(electrode_object["working_ion_entry"]) for pair in electrode_object["voltage_pairs"]: for key in ["entries_charge", "entries_discharge"]: pair[key] = [ TypeAdapter(TypedComputedStructureEntryDict).validate_python( entry ) for entry in pair[key] ] return electrode_object def electrode_object_deserializer( eo: dict[str, Any] | InsertionElectrode | ConversionElectrode, ) -> InsertionElectrode | ConversionElectrode: if isinstance(eo, dict): target_class: type[InsertionElectrode | ConversionElectrode] match eo["@class"]: case "InsertionElectrode": target_class = InsertionElectrode battery_type = BatteryType.insertion eo["working_ion_entry"] = pop_cse_empty_keys(eo["working_ion_entry"]) for key in ["stable_entries", "unstable_entries"]: eo[key] = [pop_cse_empty_keys(cse) for cse in eo[key]] case "ConversionElectrode": target_class = ConversionElectrode battery_type = BatteryType.conversion eo["working_ion_entry"] = pop_cse_empty_keys(eo["working_ion_entry"]) if any( [ isinstance(eo["working_ion_entry"].get("energy_adjustments"), _type) for _type in (str, bytes) ] ): eo = _deserialize_electrode_object_entries(eo, battery_type) electrode_object_energy_adjustments_serde(eo, battery_type, orjson.loads) eo["voltage_pairs"] = walk_voltage_pairs(eo["voltage_pairs"], battery_type) return target_class.from_dict(eo) return eo InsertionElectrodeType = Annotated[ InsertionElectrodeTypeVar, BeforeValidator(electrode_object_deserializer), WrapSerializer(electrode_object_serializer, return_type=dict[str, Any]), ] ConversionElectrodeType = Annotated[ ConversionElectrodeTypeVar, BeforeValidator(electrode_object_deserializer), WrapSerializer(electrode_object_serializer, return_type=dict[str, Any]), ] ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8274822 emmet_core-0.87.2/emmet/core/types/pymatgen_types/element_adapter.py0000644000175100017510000000067415240717720025367 0ustar00runnerrunnerfrom typing import Annotated, TypeVar from pydantic.functional_serializers import WrapSerializer from pydantic.functional_validators import BeforeValidator from emmet.core.io.pymatgen import Element ElementTypeVar = TypeVar("ElementTypeVar", Element, str) ElementType = Annotated[ ElementTypeVar, BeforeValidator(lambda x: Element(x) if isinstance(x, str) else x), WrapSerializer(lambda x, nxt, info: str(x), return_type=str), ] ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8274822 emmet_core-0.87.2/emmet/core/types/pymatgen_types/grain_boundary_adapter.py0000644000175100017510000000417615240717720026742 0ustar00runnerrunnerfrom typing import Annotated, TypeVar from pydantic import BeforeValidator, WrapSerializer from emmet.core.io.pymatgen import GrainBoundary from typing_extensions import TypedDict from emmet.core.types.pymatgen_types.lattice_adapter import TypedLatticeDict from emmet.core.types.pymatgen_types.sites_adapter import TypedSiteDict from emmet.core.types.pymatgen_types.structure_adapter import ( TypedStructureDict, pop_empty_structure_keys, ) TypedGrainBoundaryDict = TypedDict( "TypedGrainBoundaryDict", { "@module": str, "@class": str, "lattice": TypedLatticeDict, "sites": list[TypedSiteDict], "init_cell": TypedStructureDict, "rotation_axis": list[int], "rotation_angle": float, "gb_plane": list[int], "join_plane": list[int], "vacuum_thickness": float, "ab_shift": list[float], "oriented_unit_cell": TypedStructureDict, }, ) GrainBoundaryTypeVar = TypeVar( "GrainBoundaryTypeVar", GrainBoundary, TypedGrainBoundaryDict ) def pop_empty_gb_keys(gb: GrainBoundaryTypeVar) -> GrainBoundary: if isinstance(gb, dict): for key in ["init_cell", "oriented_unit_cell"]: gb[key] = pop_empty_structure_keys(gb[key], serialize=False) # type: ignore[literal-required] for site in gb["sites"]: if "name" in site and not site["name"]: del site["name"] for key in [ "properties", ]: for prop, val in list(site.get(key, {}).items()): if val is None: del site[key][prop] for idx, species_dct in enumerate(site["species"]): keys_to_delete = [k for k, v in species_dct.items() if v is None] for key in keys_to_delete: del site["species"][idx][key] return GrainBoundary.from_dict(gb) # type: ignore[arg-type] return gb GrainBoundaryType = Annotated[ GrainBoundaryTypeVar, BeforeValidator(pop_empty_gb_keys), WrapSerializer( lambda x, nxt, info: x.as_dict(), return_type=TypedGrainBoundaryDict ), ] ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8274822 emmet_core-0.87.2/emmet/core/types/pymatgen_types/ir_dielectric_tensor_adapter.py0000644000175100017510000000164215240717720030125 0ustar00runnerrunnerfrom typing import Annotated, TypeVar from pydantic import BeforeValidator, WrapSerializer from emmet.core.io.pymatgen import IRDielectricTensor from typing_extensions import TypedDict from emmet.core.types.pymatgen_types.structure_adapter import TypedStructureDict TypedIRDTensorDict = TypedDict( "TypedIRDTensorDict", { "@module": str, "@class": str, "oscillator_strength": list[float], "ph_freqs_gamma": list[float], "structure": TypedStructureDict, "epsilon_infinity": list[float], }, ) IRDielectricTensorTypeVar = TypeVar( "IRDielectricTensorTypeVar", IRDielectricTensor, TypedIRDTensorDict ) IRDielectricTensorType = Annotated[ IRDielectricTensorTypeVar, BeforeValidator( lambda x: IRDielectricTensor.from_dict(x) if isinstance(x, dict) else x ), WrapSerializer(lambda x, nxt, info: x.as_dict(), return_type=TypedIRDTensorDict), ] ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8274822 emmet_core-0.87.2/emmet/core/types/pymatgen_types/kpoint_adapter.py0000644000175100017510000000142115240717720025231 0ustar00runnerrunnerfrom typing import Annotated, TypeVar from pydantic import BeforeValidator, WrapSerializer from emmet.core.io.pymatgen import Kpoint from typing_extensions import TypedDict from emmet.core.types.pymatgen_types.lattice_adapter import MSONableTypedLatticeDict TypedKpointDict = TypedDict( "TypedKpointDict", { "@module": str, "@class": str, "lattice": MSONableTypedLatticeDict, "fcoords": list[float], "ccoords": list[float], "label": str, }, ) KpointTypeVar = TypeVar("KpointTypeVar", Kpoint, TypedKpointDict) KpointType = Annotated[ KpointTypeVar, BeforeValidator(lambda x: Kpoint.from_dict(x) if isinstance(x, dict) else x), WrapSerializer(lambda x, nxt, info: x.as_dict(), return_type=TypedKpointDict), ] ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8274822 emmet_core-0.87.2/emmet/core/types/pymatgen_types/kpoints_adapter.py0000644000175100017510000000427515240717720025426 0ustar00runnerrunnerfrom typing import Annotated, Any, TypeAlias, TypeVar from pydantic import BeforeValidator, WrapSerializer from emmet.core.io.pymatgen import Kpoints from typing_extensions import NotRequired, TypedDict TypedKpointsDict = TypedDict( "TypedKpointsDict", { "@module": str, "@class": str, "comment": str, "coord_type": str, "generation_style": str, "kpoints": list[list[int]], "kpts_weights": list[float], "labels": list[str], "nkpoints": int, # "tet_connections": tuple[float, list[float]], "sym_weight": NotRequired[list[float]], # tet_connections "tet_vertices": NotRequired[list[list[float]]], # tet_connections "tet_number": int, "tet_weight": float, "usershift": list[float], }, ) KpointsTypeVar = TypeVar("KpointsTypeVar", Kpoints, TypedKpointsDict) def kpoints_deserializer(kpoints: KpointsTypeVar): if isinstance(kpoints, dict): if "sym_weight" in kpoints: kpoints["tet_connections"] = [ # type: ignore[typeddict-unknown-key] (sym, vert) for sym, vert in zip(kpoints["sym_weight"], kpoints["tet_vertices"]) ] del kpoints["sym_weight"] del kpoints["tet_vertices"] return Kpoints.from_dict(kpoints) # type: ignore[arg-type] return kpoints def kpoints_serializer(kpoints, nxt, info): default_serialized_object = kpoints.as_dict() format = info.context.get("format") if info.context else None if format == "arrow" and default_serialized_object: if default_serialized_object["tet_connections"]: default_serialized_object["sym_weight"] = [ conn[0] for conn in default_serialized_object["tet_connections"] ] default_serialized_object["tet_vertices"] = [ conn[1] for conn in default_serialized_object["tet_connections"] ] del default_serialized_object["tet_connections"] return default_serialized_object KpointsType: TypeAlias = Annotated[ KpointsTypeVar, BeforeValidator(kpoints_deserializer), WrapSerializer(kpoints_serializer, return_type=dict[str, Any]), ] ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8274822 emmet_core-0.87.2/emmet/core/types/pymatgen_types/lattice_adapter.py0000644000175100017510000000211215240717720025350 0ustar00runnerrunnerfrom typing import Annotated, TypeVar from pydantic import BeforeValidator, WrapSerializer from emmet.core.io.pymatgen import Lattice from typing_extensions import NotRequired, TypedDict MSONableTypedLatticeDict = TypedDict( "MSONableTypedLatticeDict", { "@module": str, "@class": str, "matrix": list[list[float]], "pbc": tuple[bool, bool, bool], "a": float, "b": float, "c": float, "alpha": float, "beta": float, "gamma": float, "volume": float, }, ) class TypedLatticeDict(TypedDict): matrix: list[list[float]] pbc: NotRequired[tuple[bool, bool, bool] | None] a: float b: float c: float alpha: float beta: float gamma: float volume: float LatticeTypeVar = TypeVar("LatticeTypeVar", Lattice, MSONableTypedLatticeDict) LatticeType = Annotated[ LatticeTypeVar, BeforeValidator(lambda x: Lattice.from_dict(x) if isinstance(x, dict) else x), WrapSerializer( lambda x, nxt, info: x.as_dict(), return_type=MSONableTypedLatticeDict ), ] ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8274822 emmet_core-0.87.2/emmet/core/types/pymatgen_types/mixing_scheme_adapter.py0000644000175100017510000000272115240717720026550 0ustar00runnerrunnerfrom typing import Annotated, TypeVar from pydantic import BeforeValidator, WrapSerializer from emmet.core.io.pymatgen import MaterialsProjectDFTMixingScheme from typing_extensions import TypedDict from emmet.core.types.pymatgen_types.compatibility_adapter import ( TypedCompatibilityDict, TypedMaterialsProject2020CompatibilityAdapterDict, ) from emmet.core.types.pymatgen_types.structure_matcher_adapter import ( TypedStructureMatcherDict, ) TypedMaterialsProjectDFTMixingSchemeDict = TypedDict( "TypedMaterialsProjectDFTMixingSchemeDict", { "@module": str, "@class": str, "@version": str, "structure_matcher": TypedStructureMatcherDict, "run_type_1": str, "run_type_2": str, "compat_1": TypedMaterialsProject2020CompatibilityAdapterDict, "compat_2": TypedCompatibilityDict, "fuzzy_matching": bool, "check_potcar": bool, }, ) MaterialsProjectDFTMixingSchemeTypeVar = TypeVar( "MaterialsProjectDFTMixingSchemeTypeVar", MaterialsProjectDFTMixingScheme, TypedMaterialsProjectDFTMixingSchemeDict, ) MaterialsProjectDFTMixingSchemeType = Annotated[ MaterialsProjectDFTMixingSchemeTypeVar, BeforeValidator( lambda x: ( MaterialsProjectDFTMixingScheme.from_dict(x) if isinstance(x, dict) else x ) ), WrapSerializer( lambda x, nxt, info: x.as_dict(), return_type=TypedMaterialsProjectDFTMixingSchemeDict, ), ] ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8274822 emmet_core-0.87.2/emmet/core/types/pymatgen_types/outcar_adapter.py0000644000175100017510000000304415240717720025225 0ustar00runnerrunnerfrom typing_extensions import TypedDict, NotRequired from emmet.core.math import Matrix3D class TypedPerIonProps(TypedDict): s: float p: float d: float tot: float TypedOutcarDict = TypedDict( "TypedOutcarDict", { "@module": str, "@class": str, "efermi": NotRequired[float | None], "magnetization": NotRequired[tuple[TypedPerIonProps, ...] | None], "charge": NotRequired[tuple[TypedPerIonProps, ...] | None], "total_magnetization": NotRequired[float | None], "nelect": NotRequired[float | None], "is_stopped": NotRequired[bool | None], "drift": NotRequired[list[list[float]] | None], "ngf": NotRequired[list[int] | None], "sampling_radii": NotRequired[list[float] | None], "electrostatic_potential": NotRequired[list[float] | None], # `zval_dict` and `p_elec` are only required for ferroelectric stuff in atomate2 "zval_dict": NotRequired[dict[str, float] | None], "p_elec": NotRequired[tuple[float, float, float] | None], # `born` is needed for phonon workflows "born": NotRequired[list[Matrix3D] | None], }, ) # would be nice to figure out a constructor for a pmg Outcar from a dict input # OutcarTypeVar = TypeVar("OutcarTypeVar", Outcar, TypedOutcarDict) # OutcarType = Annotated[ # OutcarTypeVar, # BeforeValidator(lambda x: Outcar(**x) if isinstance(x, dict) else x), # WrapSerializer(lambda x, nxt, info: x.as_dict(), return_type=TypedOutcarDict), # ] OutcarType = TypedOutcarDict ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8274822 emmet_core-0.87.2/emmet/core/types/pymatgen_types/phase_diagram_adapter.py0000644000175100017510000000745115240717720026522 0ustar00runnerrunnerfrom collections.abc import Callable from enum import Enum, auto from itertools import chain from typing import Annotated, Any, TypeVar, ValuesView import orjson from pydantic import BeforeValidator, TypeAdapter, WrapSerializer from emmet.core.io.pymatgen import PhaseDiagram from typing_extensions import TypedDict from emmet.core.types.pymatgen_types.computed_entries_adapter import ( TypedCEDataDict, TypedComputedStructureEntryDict, ) class Mode(Enum): SHRED = auto() STITCH = auto() TypedMSONableElementDict = TypedDict( "TypedMSONableElementDict", {"@module": str, "@class": str, "element": str} ) TypedSimplexDict = TypedDict( "TypedSimplexDict", {"@module": str, "@class": str, "@version": str, "coords": list[list[float]]}, ) # Used for running deserialization not dependent on # 'shape' of el_refs _TypedComputedDataDict = TypedDict( "_TypedComputedDataDict", { "facets": list[list[int]], "qhull_data": list[list[float]], "qhull_entries": list[int], "all_entries": list[TypedComputedStructureEntryDict], }, ) class TypedComputedDataDict(_TypedComputedDataDict): # el_refs: list[[str, int]] el_refs_elements: list[str] el_refs_entries: list[int] TypedPhaseDiagramDict = TypedDict( "TypedPhaseDiagramDict", { "@module": str, "@class": str, "elements": list[str], "computed_data": TypedComputedDataDict, }, ) PhaseDiagramTypeVar = TypeVar( "PhaseDiagramTypeVar", PhaseDiagram, TypedPhaseDiagramDict ) def entries_list_serde(entries_list: ValuesView | list[dict], serde_fn: Callable): for entry in entries_list: entry["energy_adjustments"] = serde_fn(entry["energy_adjustments"]) def phase_diagram_serde(d: dict, mode: Mode, serde_fn: Callable): entries_list_serde(d["computed_data"]["all_entries"], serde_fn) match mode: case Mode.SHRED: el_ref_pairs = d["computed_data"].pop("el_refs") elements = [] el_refs_entries = [] for element, entry_idx in el_ref_pairs: elements.append(str(element)) el_refs_entries.append(entry_idx) d["computed_data"]["el_refs_elements"] = elements d["computed_data"]["el_refs_entries"] = el_refs_entries case Mode.STITCH: elements = d["computed_data"].pop("el_refs_elements") el_refs_entries = d["computed_data"].pop("el_refs_entries") d["computed_data"]["el_refs"] = [ (i, j) for i, j in zip(elements, el_refs_entries) ] def phase_diagram_serializer(phase_diagram, nxt, info) -> dict[str, Any]: for entry in chain( phase_diagram.computed_data["qhull_entries"], phase_diagram.computed_data["all_entries"], ): entry.data = TypeAdapter(TypedCEDataDict).dump_python(entry.data) default_serialized_object = nxt(phase_diagram.as_dict(), info) format = info.context.get("format") if info.context else None if format == "arrow": phase_diagram_serde( default_serialized_object, mode=Mode.SHRED, serde_fn=orjson.dumps ) return default_serialized_object def phase_diagram_deserializer(value) -> PhaseDiagram: if isinstance(value, dict): value["computed_data"] = TypeAdapter(_TypedComputedDataDict).validate_python( value["computed_data"], extra="allow" ) if all( key in value["computed_data"] for key in ["el_refs_elements", "el_refs_entries"] ): phase_diagram_serde(value, mode=Mode.STITCH, serde_fn=orjson.loads) return PhaseDiagram.from_dict(value) return value PhaseDiagramType = Annotated[ PhaseDiagramTypeVar, BeforeValidator(phase_diagram_deserializer), WrapSerializer(phase_diagram_serializer), ] ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8274822 emmet_core-0.87.2/emmet/core/types/pymatgen_types/phonon_adapter.py0000644000175100017510000000351415240717720025233 0ustar00runnerrunnerfrom typing import Annotated, TypeVar from pydantic import BeforeValidator from emmet.core.io.pymatgen import PhononBandStructureSymmLine, PhononDos from typing_extensions import TypedDict from emmet.core.types.pymatgen_types.lattice_adapter import MSONableTypedLatticeDict from emmet.core.types.pymatgen_types.structure_adapter import ( TypedStructureDict, pop_empty_structure_keys, ) TypedPhononBandStructureSymmLineDict = TypedDict( "TypedPhononBandStructureSymmLineDict", { "@module": str, "@class": str, "bands": list[list[float]], "eigendisplacements": dict[str, list[list[list[list[float]]]]], "has_nac": bool, "labels_dict": dict[str, list[float, float, float]], # type: ignore[type-arg] "lattice_rec": MSONableTypedLatticeDict, "qpoints": list[list[float]], "structure": TypedStructureDict, }, ) TypedPhononDosDict = TypedDict( "TypedPhononDosDict", { "@module": str, "@class": str, "densities": list[float], "frequencies": list[float], "pdos": list[list[float]], "structure": TypedStructureDict, }, ) PhononBandStructureSymmLineTypeVar = TypeVar( "PhononBandStructureSymmLineTypeVar", PhononBandStructureSymmLine, TypedPhononBandStructureSymmLineDict, ) PhononDosTypeVar = TypeVar("PhononDosTypeVar", PhononDos, TypedPhononDosDict) def pop_empty_keys_from_structure( d: PhononDosTypeVar | PhononBandStructureSymmLineTypeVar, ): if isinstance(d, dict): d["structure"] = pop_empty_structure_keys(d["structure"]) return d AnnotatedPhononBandStructureSymmLine = Annotated[ PhononBandStructureSymmLineTypeVar, BeforeValidator(pop_empty_keys_from_structure) ] AnnotatedPhononDos = Annotated[ PhononDosTypeVar, BeforeValidator(pop_empty_keys_from_structure) ] ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8274822 emmet_core-0.87.2/emmet/core/types/pymatgen_types/poscar_adapter.py0000644000175100017510000000225115240717720025216 0ustar00runnerrunnerfrom typing import Annotated, TypeVar from pydantic import BeforeValidator, WrapSerializer from emmet.core.io.pymatgen import Poscar from typing_extensions import TypedDict from emmet.core.types.pymatgen_types.structure_adapter import ( TypedStructureDict, pop_empty_structure_keys, ) TypedPoscarDict = TypedDict( "TypedPoscarDict", { "@module": str, "@class": str, "structure": TypedStructureDict, "true_names": bool, "selective_dynamics": list[list[bool]], "velocities": list[list[float]], "predictor_corrector": list[list[float]], "comment": str, }, ) PoscarTypeVar = TypeVar("PoscarTypeVar", Poscar, TypedPoscarDict) def pop_poscar_empty_structure_keys(poscar: PoscarTypeVar): if isinstance(poscar, dict): clean_structure = pop_empty_structure_keys(poscar["structure"]) poscar["structure"] = clean_structure return Poscar.from_dict(poscar) # type: ignore[arg-type] return poscar PoscarType = Annotated[ PoscarTypeVar, BeforeValidator(pop_poscar_empty_structure_keys), WrapSerializer(lambda x, nxt, info: x.as_dict(), return_type=TypedPoscarDict), ] ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8274822 emmet_core-0.87.2/emmet/core/types/pymatgen_types/properties.py0000644000175100017510000000131315240717720024421 0ustar00runnerrunnerfrom typing_extensions import NotRequired, TypedDict from emmet.core.math import Vector3D class TypedSiteProperitesDict(TypedDict): magmom: NotRequired[float | None] charge: NotRequired[float | None] velocities: NotRequired[Vector3D | None] selective_dynamics: NotRequired[tuple[bool, bool, bool] | None] coordination_no: NotRequired[int | None] forces: NotRequired[list[float, float, float] | None] # type: ignore[type-arg] class TypedAggregateProperitesDict(TypedDict): magmom: NotRequired[list[float] | None] charge: NotRequired[list[float] | None] velocities: NotRequired[list[Vector3D] | None] selective_dynamics: NotRequired[list[tuple[bool, bool, bool]] | None] ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8274822 emmet_core-0.87.2/emmet/core/types/pymatgen_types/sites_adapter.py0000644000175100017510000000173315240717720025062 0ustar00runnerrunnerfrom typing_extensions import NotRequired, TypedDict from emmet.core.types.pymatgen_types.properties import TypedSiteProperitesDict from emmet.core.types.pymatgen_types.species_adapter import TypedSpeciesDict MSONableTypedSiteDict = TypedDict( "MSONableTypedSiteDict", { "@class": str, "@module": str, "label": str, "name": str, "properties": TypedSiteProperitesDict, "species": list[TypedSpeciesDict], "abc": list[float, float, float], # type: ignore[type-arg] "xyz": list[float, float, float], # type: ignore[type-arg] }, ) class TypedSiteDict(TypedDict): label: NotRequired[str | None] name: NotRequired[str | None] properties: NotRequired[TypedSiteProperitesDict | None] species: NotRequired[list[TypedSpeciesDict] | None] abc: NotRequired[list[float, float, float] | None] # type: ignore[type-arg] xyz: NotRequired[list[float, float, float] | None] # type: ignore[type-arg] ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8274822 emmet_core-0.87.2/emmet/core/types/pymatgen_types/species_adapter.py0000644000175100017510000000156215240717720025366 0ustar00runnerrunnerfrom typing import Annotated, TypeVar from pydantic import BeforeValidator, WrapSerializer from emmet.core.io.pymatgen import Species from typing_extensions import NotRequired, TypedDict MSONableTypedSpeciesDict = TypedDict( "MSONableTypedSpeciesDict", { "@module": str, "@class": str, "element": str, "oxidation_state": float, "spin": float, }, ) class TypedSpeciesDict(TypedDict): element: NotRequired[str | None] oxidation_state: NotRequired[float | None] spin: NotRequired[float | None] occu: NotRequired[float | None] SpeciesTypeVar = TypeVar("SpeciesTypeVar", Species, TypedSpeciesDict) SpeciesType = Annotated[ SpeciesTypeVar, BeforeValidator(lambda x: Species.from_dict(x) if isinstance(x, dict) else x), WrapSerializer(lambda x, nxt, info: x.as_dict(), return_type=TypedSpeciesDict), ] ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8274822 emmet_core-0.87.2/emmet/core/types/pymatgen_types/structure_adapter.py0000644000175100017510000000514315240717720025772 0ustar00runnerrunnerfrom typing import Annotated, TypeVar from pydantic import BeforeValidator, WrapSerializer from emmet.core.io.pymatgen import Structure, Molecule from typing_extensions import NotRequired, TypedDict from emmet.core.types.pymatgen_types.lattice_adapter import TypedLatticeDict from emmet.core.types.pymatgen_types.properties import TypedAggregateProperitesDict from emmet.core.types.pymatgen_types.sites_adapter import TypedSiteDict TypedStructureDict = TypedDict( "TypedStructureDict", { "@module": str, "@class": str, "charge": NotRequired[float | None], "lattice": TypedLatticeDict, "sites": list[TypedSiteDict], "properties": NotRequired[TypedAggregateProperitesDict | None], }, ) StructureTypeVar = TypeVar("StructureTypeVar", Structure, TypedStructureDict) TypedMoleculeDict = TypedDict( "TypedMoleculeDict", { "@module": str, "@class": str, "charge": float, "spin_multiplicity": int, "sites": list[TypedSiteDict], "properties": TypedAggregateProperitesDict, }, ) MoleculeTypeVar = TypeVar("MoleculeTypeVar", Molecule, TypedMoleculeDict) def pop_empty_structure_keys( inp: StructureTypeVar | MoleculeTypeVar, serialize: bool = True ): if isinstance(inp, dict): target_cls = None if serialize: target_cls = Structure if inp["@class"] == "Structure" else Molecule if inp.get("properties"): for prop, val in list(inp["properties"].items()): # type: ignore[union-attr] if val is None: del inp["properties"][prop] # type: ignore[union-attr] for site in inp["sites"]: if "name" in site: if not site["name"]: del site["name"] if site.get("properties"): for prop, val in list(site["properties"].items()): if val is None: del site["properties"][prop] for species in site["species"]: for prop, val in list(species.items()): if val is None: del species[prop] if target_cls: return target_cls.from_dict(inp) # type: ignore[arg-type] return inp MoleculeType = Annotated[ MoleculeTypeVar, BeforeValidator(pop_empty_structure_keys), WrapSerializer(lambda x, nxt, info: x.as_dict(), return_type=TypedMoleculeDict), ] StructureType = Annotated[ StructureTypeVar, BeforeValidator(pop_empty_structure_keys), WrapSerializer(lambda x, nxt, info: x.as_dict(), return_type=TypedStructureDict), ] ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8274822 emmet_core-0.87.2/emmet/core/types/pymatgen_types/structure_graph_adapter.py0000644000175100017510000000514315240717720027153 0ustar00runnerrunnerfrom typing import Annotated, TypeVar from pydantic import BeforeValidator, WrapSerializer from emmet.core.io.pymatgen import MoleculeGraph, StructureGraph from typing_extensions import NotRequired, TypedDict from emmet.core.types.pymatgen_types.structure_adapter import ( TypedMoleculeDict, TypedStructureDict, pop_empty_structure_keys, ) class GraphDescriptorDict(TypedDict): name: str edge_weight_name: NotRequired[str] edge_weight_units: NotRequired[str] GraphDescriptors = tuple[str, str] class TypedNodeDict(TypedDict): id: int class TypedAdjacencyDict(TypedDict): to_jimage: tuple[int, int, int] weight: float id: int key: int class TypedGraphDict(TypedDict): directed: bool multigraph: bool graph: list[GraphDescriptors] nodes: list[TypedNodeDict] adjacency: list[list[TypedAdjacencyDict]] TypedStructureGraphDict = TypedDict( "TypedStructureGraphDict", { "@module": str, "@class": str, "structure": TypedStructureDict, "graphs": TypedGraphDict, }, ) TypedMoleculeGraphDict = TypedDict( "TypedMoleculeGraphDict", { "@module": str, "@class": str, "molecule": TypedMoleculeDict, "graphs": TypedGraphDict, }, ) StructureGraphTypeVar = TypeVar( "StructureGraphTypeVar", StructureGraph, TypedStructureGraphDict ) MoleculeGraphTypeVar = TypeVar( "MoleculeGraphTypeVar", MoleculeGraph, TypedMoleculeGraphDict ) def pop_empty_graph_keys(graph: StructureGraphTypeVar | MoleculeGraphTypeVar): if isinstance(graph, dict): target_cls: type[StructureGraph | MoleculeGraph] match graph["@class"]: case "StructureGraph": target_cls = StructureGraph graph["structure"] = pop_empty_structure_keys(graph["structure"], serialize=False) # type: ignore[typeddict-unknown-key, typeddict-item] case "MoleculeGraph": target_cls = MoleculeGraph graph["molecule"] = pop_empty_structure_keys(graph["molecule"], serialize=False) # type: ignore[typeddict-unknown-key, typeddict-item] return target_cls.from_dict(graph) # type: ignore[arg-type] return graph StructureGraphType = Annotated[ StructureGraphTypeVar, BeforeValidator(pop_empty_graph_keys), WrapSerializer( lambda x, nxt, info: x.as_dict(), return_type=TypedStructureGraphDict ), ] MoleculeGraphType = Annotated[ MoleculeGraphTypeVar, BeforeValidator(pop_empty_graph_keys), WrapSerializer( lambda x, nxt, info: x.as_dict(), return_type=TypedMoleculeGraphDict ), ] ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8274822 emmet_core-0.87.2/emmet/core/types/pymatgen_types/structure_matcher_adapter.py0000644000175100017510000000252015240717720027471 0ustar00runnerrunnerfrom typing import Annotated, TypeVar from pydantic import BeforeValidator, WrapSerializer from emmet.core.io.pymatgen import StructureMatcher from typing_extensions import TypedDict from emmet.core.types.pymatgen_types.species_adapter import MSONableTypedSpeciesDict TypedComparatorDict = TypedDict( "TypedComparatorDict", { "@module": str, "@class": str, "@version": str, }, ) # No concrete comparator class defines an as_dict() method, parent(Comparator) is abstract TypedStructureMatcherDict = TypedDict( "TypedStructureMatcherDict", { "@module": str, "@class": str, "@version": str, "ltol": float, "stol": float, "angle_tol": float, "primitive_cell": bool, "scale": bool, "attempt_supercell": bool, "comparator": TypedComparatorDict, "supercell_size": str, "ignored_species": list[MSONableTypedSpeciesDict], }, ) StructureMatcherTypeVar = TypeVar( "StructureMatcherTypeVar", StructureMatcher, TypedStructureMatcherDict ) StructureMatcherType = Annotated[ StructureMatcherTypeVar, BeforeValidator( lambda x: StructureMatcher.from_dict(x) if isinstance(x, dict) else x ), WrapSerializer( lambda x, nxt, info: x.as_dict(), return_type=TypedStructureMatcherDict ), ] ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8274822 emmet_core-0.87.2/emmet/core/types/pymatgen_types/trajectory_adapter.py0000644000175100017510000000226115240717720026116 0ustar00runnerrunnerfrom typing import Annotated, TypeVar from pydantic import BeforeValidator, WrapSerializer from emmet.core.io.pymatgen import Trajectory from typing_extensions import TypedDict from emmet.core.math import Matrix3D, Vector3D from emmet.core.types.pymatgen_types.properties import TypedAggregateProperitesDict class FrameProps(TypedDict): energy: float TypedTrajectoryDict = TypedDict( "TypedTrajectoryDict", { "@module": str, "@class": str, "species": list[str], "coords": list[list[Vector3D]], "charge": float, "spin_multiplicity": float, "lattice": list[Matrix3D], "site_properties": TypedAggregateProperitesDict, "frame_properties": FrameProps, "constant_lattice": bool, "time_step": float, "coords_are_displacement": bool, "base_positions": list[Vector3D], }, ) TrajectoryTypeVar = TypeVar("TrajectoryTypeVar", Trajectory, TypedTrajectoryDict) TrajectoryType = Annotated[ TrajectoryTypeVar, BeforeValidator(lambda x: Trajectory.from_dict(x) if isinstance(x, dict) else x), WrapSerializer(lambda x, nxt, info: x.as_dict(), return_type=TypedTrajectoryDict), ] ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8274822 emmet_core-0.87.2/emmet/core/types/pymatgen_types/xas_adapter.py0000644000175100017510000000325015240717720024522 0ustar00runnerrunnerfrom typing import Any, Annotated, TypeVar import numpy as np from pydantic import BeforeValidator, WrapSerializer from emmet.core.io.pymatgen import XAS from typing_extensions import TypedDict from emmet.core.types.pymatgen_types.structure_adapter import ( TypedStructureDict, pop_empty_structure_keys, ) TypedElementDict = TypedDict( "TypedElementDict", {"@module": str, "@class": str, "element": str}, ) TypedXASSpectrumDict = TypedDict( "TypedXASSpectrumDict", { "@module": str, "@class": str, "@version": str, "x": list[float], "y": list[float], "structure": TypedStructureDict, "absorbing_element": TypedElementDict, "edge": str, "spectrum_type": str, "absorbing_index": int, }, ) XASTypeVar = TypeVar("XASTypeVar", XAS, TypedXASSpectrumDict) def pop_xas_empty_structure_fields(xas: XASTypeVar, eps: float = 1e-12): """Remove empty keys in the XAS dict and ensure that the spectral data are positive.""" if isinstance(xas, dict): clean_structure = pop_empty_structure_keys(xas["structure"]) xas["structure"] = clean_structure xas["y"] = [y if y > 0.0 else abs(eps) for y in xas["y"]] return XAS.from_dict(xas) return xas def _serialize_xas(xas: XAS) -> dict[str, Any]: xas_dct = xas.as_dict() for k in ("x", "y"): if isinstance(xas_dct[k], np.ndarray): xas_dct[k] = xas_dct[k].tolist() return xas_dct XASType = Annotated[ XASTypeVar, BeforeValidator(pop_xas_empty_structure_fields), WrapSerializer( lambda x, nxt, info: _serialize_xas(x), return_type=TypedXASSpectrumDict ), ] ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8282716 emmet_core-0.87.2/emmet/core/types/typing.py0000644000175100017510000003777715240717720020516 0ustar00runnerrunner"""Define custom type annotations for emmet-core. Note that only type annotations which are used across the code base should be put here. Types which only have one purpose / exist only within one module, can and should remain in that module. """ from __future__ import annotations import os from datetime import datetime from enum import Enum from functools import partial from pathlib import Path from typing import TYPE_CHECKING, Annotated, Any, Union, overload import orjson from pydantic import BeforeValidator, Field, PlainSerializer, WrapSerializer from typing_extensions import TypedDict from emmet.core.mpid import MPID, AlphaID from emmet.core.utils import convert_datetime, utcnow if TYPE_CHECKING: from typing import Literal from typing_extensions import TypeAlias FSPathType: TypeAlias = Annotated[ Union[str | Path | os.DirEntry[str] | os.PathLike[str]], PlainSerializer(lambda x: str(x), return_type=str), ] """Type of a generic path-like object""" DateTimeType: TypeAlias = Annotated[ datetime, Field(default_factory=utcnow), BeforeValidator(convert_datetime), ] """Datetime serde.""" NullableDateTimeType: TypeAlias = Annotated[ datetime | None, Field(default_factory=utcnow), BeforeValidator(convert_datetime), ] """Nullable datetime serde. See: https://docs.pydantic.dev/latest/concepts/fields/#the-annotated-pattern for why this separate class is necesary instead of `DateTimeType | None` """ def _fault_tolerant_id_serde( val: Any, legacy: bool = False, serialize: bool = False, **kwargs, ) -> Any: """Needed for the API and safe de-/serialization behavior.""" try: alpha_id = AlphaID(val, **kwargs) if serialize: return str(alpha_id) return alpha_id.formatted if legacy else alpha_id except Exception: return val _id_base_metadata = (BeforeValidator(_fault_tolerant_id_serde),) ID_PADLEN: int = 8 ID_PREFIX: str = "mp" @overload def format_identifier( idx: str | MPID | AlphaID, legacy: bool, prefix: str | None = ID_PREFIX, padlen: int = ID_PADLEN, ) -> str | None: ... @overload def format_identifier( idx: list[str | MPID | AlphaID], legacy: bool, prefix: str | None = ID_PREFIX, padlen: int = ID_PADLEN, ) -> list[str | None] | None: ... def format_identifier( idx: Any, legacy: bool, prefix: str | None = ID_PREFIX, padlen: int = ID_PADLEN, ) -> str | list[str | None] | None: """Render an MP identifier as either the legacy `mp-` form or the padded AlphaID form. This is the canonical display-formatting helper for MP identifiers. Use it anywhere you need to switch a `material_id`, plain prefix-number id, or AlphaID-form id between the two human-readable representations. Args: idx: An MPID, AlphaID, or string that parses as either. May be passed in any form (legacy `mp-149`, unpadded alpha `mp-ft`, padded alpha `mp-aaaaaaft`, bare int `149`, etc.); the function normalizes. Lists of ids are acceptable inputs as well (e.g., InsertionElectrodeDoc.material_ids). Processes lists of ids as expected. legacy: If True, returns the legacy form (e.g. "mp-149") for identifiers below the AlphaID cutoff. Above the cutoff there is no legacy form, so the alpha form is returned instead. If False, returns the AlphaID form padded to `padlen` characters (e.g. "mp-aaaaaaft" for value 149 with the default padlen=8). prefix: The id prefix to apply on the alpha-form output. Defaults to "mp"; pass None to omit the prefix entirely. padlen: The minimum identifier length on the alpha-form output (the identifier is left-padded with the alphabet's first letter to reach this length). Defaults to 8. Returns: The formatted string. If `idx` is None or empty, it is returned unchanged. If `idx` cannot be parsed as an AlphaID, it is coerced to a string and returned unchanged (defensive: never raises from a display helper). Examples: >>> format_identifier("mp-149", legacy=True) 'mp-149' >>> format_identifier("mp-149", legacy=False) 'mp-aaaaaaft' >>> format_identifier("mp-aaaaaaft", legacy=True) 'mp-149' """ # Guard against None and the empty string. The ``str``-instance check # avoids triggering ``MPID.__eq__`` (which raises ValueError on # ``MPID(...) == ""``) when ``idx`` is an MPID/AlphaID subclass instance # rather than a plain string. if idx is None: return idx if isinstance(idx, str) and not idx: return idx if isinstance(idx, list): return [format_identifier(_id, legacy, prefix, padlen) for _id in idx] try: alpha = AlphaID(idx) except (ValueError, TypeError): return str(idx) if legacy: return alpha.string # Re-instantiate with the requested display pad length, preserving the # separator from the parsed instance (defaulting to "-" if absent). return str( AlphaID( int(alpha), padlen=padlen, prefix=prefix, separator=alpha._separator or "-", ) ) # Separators used by composite MP identifiers # ("mp-2658_Al" for battery ids, "mp-67-XANES-O-K" for XAS spectrum ids). # The first segment is the canonical MPID/AlphaID; any trailing segments # are preserved verbatim during reformatting. _COMPOSITE_ID_SEPARATORS: tuple[str, ...] = ("_", "-") def _split_composite_identifier( value: str, separators: tuple[str, ...] = _COMPOSITE_ID_SEPARATORS, ) -> tuple[str, str]: """Split a composite MP identifier into ``(base_id, suffix)``. The base_id is the longest leading substring that parses as a valid MP identifier via ``AlphaID``; the suffix is everything from the splitting separator onward (separator included). If no valid split exists, returns ``(value, "")`` so callers can pass the value through unchanged. Examples: >>> _split_composite_identifier("mp-149") ('mp-149', '') >>> _split_composite_identifier("mp-2658_Al") ('mp-2658', '_Al') """ from emmet.core.mpid import validate_identifier if not isinstance(value, str) or not value: return value, "" # If the whole thing parses, no split is needed. try: validate_identifier(value) return value, "" except ValueError: pass # Walk separators from the end of the string; the longest leading prefix # that validates is the base id. for i in range(len(value) - 1, 0, -1): if value[i] in separators: candidate = value[:i] try: validate_identifier(candidate) return candidate, value[i:] except ValueError: continue return value, "" def format_compound_identifier( idx: Any, legacy: bool, prefix: str | None = ID_PREFIX, padlen: int = ID_PADLEN, ) -> str | None: """Render a composite MP identifier in the requested format, preserving the suffix. Composite identifiers include battery ids (`mp-2658_Al`) and XAS spectrum ids (`mp-67-XANES-O-K`). The leading MP identifier portion is reformatted via `format_identifier`; the trailing suffix (working ion, task index, XAS components, etc.) is preserved verbatim. Plain (non-composite) MP identifiers pass through to `format_identifier`. This is the fault-tolerant, suffix-agnostic counterpart of `validate_compound_identifier`, which performs strict, typed validation against a known suffix tuple. Use *this* function when you need to reformat an id for display and don't know (or don't care to verify) the specific suffix shape. Args: idx: The composite identifier string. legacy: If True, the leading id is rendered in legacy form; otherwise in padded AlphaID form. See `format_identifier`. prefix: Forwarded to `format_identifier` for the alpha-form path. padlen: Forwarded to `format_identifier` for the alpha-form path. Returns: The formatted string. Returns the input unchanged if `idx` is None, empty, or cannot be parsed. Examples: >>> format_compound_identifier("mp-2658_Al", legacy=False) 'mp-aaaaadyg_Al' """ # See ``format_identifier`` for the rationale behind this two-step guard. if idx is None: return idx if isinstance(idx, str) and not idx: return idx base, suffix = _split_composite_identifier(str(idx)) if not suffix: return format_identifier(base, legacy, prefix=prefix, padlen=padlen) return f"{format_identifier(base, legacy, prefix=prefix, padlen=padlen)}{suffix}" @overload def format_task_id( task_id: str | MPID | AlphaID, legacy: bool, prefix: str = ID_PREFIX, padlen: int = ID_PADLEN, ) -> str | None: ... @overload def format_task_id( task_id: list[str | MPID | AlphaID], legacy: bool, prefix: str = ID_PREFIX, padlen: int = ID_PADLEN, ) -> list[str | None] | None: ... def format_task_id( task_id: str | MPID | AlphaID | list[str | MPID | AlphaID], legacy: bool, prefix: str = ID_PREFIX, padlen: int = ID_PADLEN, ) -> str | list[str | None] | None: """Render a calculation task id in either legacy or new alpha form. Task ids have a different shape convention than other MP identifiers: - Legacy form: ``mp-`` (prefixed, same shape as a plain mpid, e.g. ``mp-149``). - New alpha form: bare padded alpha string with **no** prefix and no suffix (e.g. ``aaaaaaft``). This is unlike `material_id` (prefix preserved in both modes) and `battery_id` (composite with a working-ion suffix). The general-purpose :func:`format_identifier` / :func:`format_compound_identifier` helpers do not capture this prefix-dropping rule, so task ids are rendered through this dedicated helper instead. Args: task_id: A task id in either form, or any value coercible to an :class:`AlphaID`. May be a bare int, a prefixed string, or an :class:`AlphaID` object. Lists of task ids are acceptable inputs as well (e.g., SummaryDoc.task_ids). Processes lists of tasks ids as expected. legacy: If True, returns the legacy form (e.g. ``mp-149``). If False, returns the bare padded alpha form (e.g. ``aaaaaaft``) with no prefix. prefix: The id prefix used in the legacy form. Defaults to ``"mp"``. padlen: The minimum identifier length on the alpha-form output. Defaults to 8. Returns: The formatted string. If ``task_id`` is None or empty, it is returned unchanged. If ``task_id`` cannot be parsed as an :class:`AlphaID`, it is coerced to a string and returned unchanged (defensive: this helper never raises from a display path). Examples: >>> format_task_id("mp-149", legacy=True) 'mp-149' >>> format_task_id("mp-149", legacy=False) 'aaaaaaft' >>> format_task_id("aaaaaaft", legacy=True) 'mp-149' """ # See ``format_identifier`` for the rationale behind this two-step guard. if task_id is None: return task_id if isinstance(task_id, str) and not task_id: return task_id if isinstance(task_id, list): return [format_task_id(tid, legacy, prefix, padlen) for tid in task_id] try: value = int(AlphaID(task_id)) except (ValueError, TypeError): return str(task_id) if legacy: return f"{prefix}-{value}" # Alpha display: bare padded identifier with no prefix. return str(AlphaID(value, padlen=padlen, prefix=None)) def _make_id_type(render_order, **kwargs) -> Any: _order: Any match render_order: case 0: _order = Union[AlphaID, MPID] case 1: _order = Union[MPID, AlphaID] case _: raise NotImplementedError( f"No implementation for render_order: {render_order}" ) return Annotated[ _order, BeforeValidator(partial(_fault_tolerant_id_serde, **kwargs)), PlainSerializer(partial(_fault_tolerant_id_serde, serialize=True, **kwargs)), ] IdentifierType = _make_id_type(0, padlen=ID_PADLEN) MaterialIdentifierType = _make_id_type( 1, legacy=True, prefix=ID_PREFIX, padlen=ID_PADLEN ) """MPID / AlphaID serde.""" class CompoundIDType(TypedDict): """Define layout of compound identifiers for static type analysis.""" identifier: AlphaID suffix: tuple[Enum] separator: str @overload def validate_compound_identifier( idx: str, suffixes: tuple[Enum], separator: str = "_", use_prefix: bool = False, as_components: Literal[False] = False, ) -> str: ... @overload def validate_compound_identifier( idx: str, suffixes: tuple[Enum], separator: str = "_", use_prefix: bool = False, as_components: Literal[True] = True, ) -> CompoundIDType: ... def validate_compound_identifier( idx: str, suffixes: tuple[Enum], separator: str = "_", use_prefix: bool = False, as_components: bool = False, ) -> str | CompoundIDType: """Serde for compound identifier types. Examples: - Thermo: mp-149_GGA - Insertion electrodes: mp-75_Li - XAS: mp-67-XANES-O-K Args: idx (str) : The compound identifier suffixes (tuple of Enum) : Suffixes used in the identifier. Must be enums, ex.: ThermoType, RunType, pymatgen.core.periodic_table.Element separator (str) : Separator between distinct ID components. use_prefix (bool) : Whether to strip the prefix from the base ID component: use_prefix = True --> mp-aaaaaaft-GGA use_prefix = False --> aaaaaaft-GGA as_components (bool) : Whether to serialize to a str (True), or return as a dict of validated components. """ for _split_method in ("split", "rsplit"): try: id_components = getattr(idx, _split_method)(separator, len(suffixes)) base_id = AlphaID( int(AlphaID(id_components[0])), prefix=ID_PREFIX if use_prefix else None, padlen=ID_PADLEN, ) validated_suffixes = [ suffix(id_components[1 + idx]) for idx, suffix in enumerate(suffixes) # type: ignore[operator] ] break except Exception: continue else: raise ValueError("Could not identify components of compound ID.") if as_components: return CompoundIDType( identifier=base_id, suffix=tuple(validated_suffixes), separator=separator, ) return separator.join([str(base_id), *[sfx.value for sfx in validated_suffixes]]) def _ser_json_like(d, default_serializer, info): """Serialize a generic JSON-like object to a str for arrow, and a dict otherwise.""" default_serialized_object = default_serializer(d, info) format = info.context.get("format") if info.context else None if format == "arrow" and default_serialized_object is not None: return orjson.dumps(default_serialized_object).decode() return default_serialized_object def _deser_json_like(d): """Deserialize a generic JSON-like object from a str or object.""" if hasattr(d, "as_dict"): d = d.as_dict() return orjson.loads(d) if isinstance(d, str | bytes) else d JsonDictType = Annotated[ dict[str, Any] | None, BeforeValidator(_deser_json_like), WrapSerializer(_ser_json_like), ] """Annotation for free-form JSON-like dict (INCAR-like, ddec6, etc.)""" JsonListType = Annotated[ list[Any] | None, BeforeValidator(_deser_json_like), WrapSerializer(_ser_json_like), ] """Annotation for free-form JSON-like list (some custodian metadata)""" def _dict_items_zipper( dict_like: dict[str, Any] | list[tuple[str, Any]] | None, ) -> dict[str, Any] | None: """Zip output of dict(...).items() back into a dict.""" if isinstance(dict_like, list): return {k: v for k, v in dict_like} return dict_like ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8282716 emmet_core-0.87.2/emmet/core/utils.py0000644000175100017510000004451215240717720017161 0ustar00runnerrunner"""Define utility functions used across the emmet namespace packages.""" from __future__ import annotations import copy import datetime import hashlib import inspect import logging from enum import Enum from importlib import import_module from itertools import groupby from math import gcd from typing import TYPE_CHECKING, get_args import numpy as np from monty.json import MontyDecoder, MSONable from pydantic import BaseModel, RootModel from pydantic._internal._utils import lenient_issubclass from emmet.core.io.pymatgen import ( Deformation, MoleculeGraph, OpenBabelNN, metal_edge_extender, MoleculeMatcher, AbstractComparator, ElementComparator, StructureMatcher, Molecule, Structure, DeformStructureTransformation, weisfeiler_lehman_graph_hash, ) from typing_extensions import TypedDict from emmet.core import ARROW_COMPATIBLE from emmet.core.mpid import MPculeID from emmet.core.settings import EmmetSettings try: import blake3 except ImportError: blake3 = None # type: ignore try: import bson except ImportError: bson = None # type: ignore if TYPE_CHECKING: from collections.abc import Callable, Iterator from typing import Any from emmet.core.types.typing import FSPathType logger = logging.getLogger(__name__) SETTINGS = EmmetSettings() def type_override(overrides: dict[str, Any]): """ Pydantic model decorator for declaring a field should be serialized as a type that does not match the type hint for the field. Examples: narrow a union type to a single value or hint at a the return type of a field_serializer used during model dumping. """ def wrapped(cls): cls.type_overrides = {**getattr(cls, "type_overrides", {}), **overrides} return cls return wrapped def set_msonable_type_adapter(cls): """ Decorator for MSONables defined in emmet with arrow compatible type hints. Shortcut for generating type adapters rather than writing a dedicated stub file. """ TypedClsDict = TypedDict( f"Typed{cls.__name__}Dict", { "@module": str, "@class": str, "@version": str, **{ key: field.annotation for key, field in inspect.signature(cls).parameters.items() }, }, ) class ClsTypeAdapter(RootModel): root: TypedClsDict # type: ignore[annotation-unchecked] setattr(cls, "__type_adapter__", ClsTypeAdapter) return cls def arrow_incompatible(cls): """ Simple decorator to mark a Pydantic model as being incompatible with serialization using pyarrow. This should only be applied as a temporary measure, all document models should aim for full type introspection. """ cls.arrow_incompatible = True return cls def get_sg(struc, symprec=SETTINGS.SYMPREC) -> int: """helper function to get spacegroup with a loose tolerance""" try: return struc.get_space_group_info(symprec=symprec)[1] except Exception: return -1 def get_num_formula_units(composition: dict[Any, int | float]) -> int: """Get the number of formula units in a dict-like composition. This implementation differs slightly from how some pymatgen/atomate2 internals work. In those, certain formulas, e.g., N, will assume a smallest formula unit of N2. Thus even if a specified composition is `{"N": 1}`, the reduced composition will be `{"N": 2}`, and the number of formula units 1/2. This always just returns the greatest common divisor of a composition. """ num_form_u = 1 if all(abs(int(val) - val) < 1e-6 for val in composition.values()): num_form_u = gcd(*[int(sc) for sc in composition.values()]) return num_form_u def group_structures( structures: list[Structure], ltol: float = SETTINGS.LTOL, stol: float = SETTINGS.STOL, angle_tol: float = SETTINGS.ANGLE_TOL, symprec: float = SETTINGS.SYMPREC, comparator: AbstractComparator = ElementComparator(), ) -> Iterator[list[Structure]]: """ Groups structures according to space group and structure matching Args: structures ([Structure]): list of structures to group ltol (float): StructureMatcher tuning parameter for matching tasks to materials stol (float): StructureMatcher tuning parameter for matching tasks to materials angle_tol (float): StructureMatcher tuning parameter for matching tasks to materials symprec (float): symmetry tolerance for space group finding """ sm = StructureMatcher( ltol=ltol, stol=stol, angle_tol=angle_tol, primitive_cell=True, scale=True, attempt_supercell=False, allow_subset=False, comparator=comparator, ) def _get_sg(struc): return get_sg(struc, symprec=symprec) # First group by spacegroup number then by structure matching for _, pregroup in groupby(sorted(structures, key=_get_sg), key=_get_sg): for group in sm.group_structures(list(pregroup)): yield group def undeform_structure(structure: Structure, transformations: dict) -> Structure: """ Get an undeformed structure by applying transformations in a reverse order. Args: structure: deformed structure transformation: transformation that deforms the structure Returns: undeformed structure """ for transformation in reversed(transformations.get("history", [])): if transformation["@class"] == "DeformStructureTransformation": deform = Deformation(transformation["deformation"]) dst = DeformStructureTransformation(deform.inv) structure = dst.apply_transformation(structure) else: raise RuntimeError( "Expect transformation to be `DeformStructureTransformation`; " f"got {transformation['@class']}" ) return structure def generate_robocrys_condensed_struct_and_description( structure: Structure, mineral_matcher=None, symprecs: list[float] = [0.01, 0.1, 1.0e-3], ) -> tuple[dict[str, Any], Any]: """ Get robocrystallographer description of a structure. Input ------ structure : pymatgen .Structure mineral_matcher : optional robocrys MineralMatcher object Slightly reduces load time by storing mineral data in memory, rather than reloading for each structure. symprecs : list[float] A list of symprec values to try for symmetry identification. The first value is the default used by robocrys, then the default used by emmet (looser), then a tighter symprec. Output ------- A robocrys condensed structure and description. """ from emmet.core.featurization import StructureCondenser, StructureDescriber for isymprec, symprec in enumerate(symprecs): # occasionally, symmetry detection fails - give a few chances to modify symprec try: condenser = StructureCondenser( mineral_matcher=mineral_matcher, symprec=symprec ) condensed_structure = condenser.condense_structure(structure) break except ValueError as exc: if isymprec == len(symprecs) - 1: raise exc for desc_fmt in ["unicode", "html", "raw"]: try: describer = StructureDescriber( describe_symmetry_labels=False, fmt=desc_fmt, return_parts=False ) description = describer.describe(condensed_structure) break except ValueError as exc: # pymatgen won't convert a "subscript period" character to unicode # in these cases, the description is still generated but unicode # parsing failed - use html instead if "subscript period" not in str(exc): raise exc return condensed_structure, description def group_molecules(molecules: list[Molecule]): """ Groups molecules according to composition, charge, and equality Note: this function is (currently) only used in the MoleculesAssociationBuilder. At that stage, we want to link calculations that are performed on identical structures. Collapsing similar structures on the basis of e.g. graph isomorphism happens at a later stage. Args: molecules (list[Molecule]) """ def _mol_form(mol_solv): return mol_solv.composition.alphabetical_formula # Extremely tight tolerance is desirable # We want to match only calculations that are EXACTLY the same # Molecules with slight differences in bonding (which might be caused by, for instance, # different solvent environments) # This tolerance was chosen based on trying to distinguish CO optimized in # two different solvents mm = MoleculeMatcher(tolerance=0.000001) # First, group by formula # Hopefully this step is unnecessary - builders should already be doing this for mol_key, pregroup in groupby(sorted(molecules, key=_mol_form), key=_mol_form): groups: list[dict[str, Any]] = list() for mol in pregroup: mol_copy = copy.deepcopy(mol) # Single atoms could always have identical structure # So grouping by geometry isn't enough # Need to also group by charge if len(mol_copy) > 1: mol_copy.set_charge_and_spin(0) matched = False # Group by structure for group in groups: if ( (mm.fit(mol_copy, group["mol"]) or mol_copy == group["mol"]) and mol_copy.charge == group["mol"].charge and mol_copy.spin_multiplicity == group["mol"].spin_multiplicity ): group["mol_list"].append(mol) matched = True break if not matched: groups.append({"mol": mol_copy, "mol_list": [mol]}) for group in groups: yield group["mol_list"] def confirm_molecule(mol: Molecule | dict): """ Check that something that we expect to be a molecule is actually a Molecule object, and not a dictionary representation. :param mol (Molecule): :return: """ if isinstance(mol, dict): return Molecule.from_dict(mol) else: return mol def make_mol_graph( mol: Molecule, critic_bonds: list[list[int]] | None = None ) -> MoleculeGraph: """ Construct a MoleculeGraph using OpenBabelNN with metal_edge_extender and (optionally) Critic2 bonding information. This bonding scheme was used to define bonding for the Lithium-Ion Battery Electrolyte (LIBE) dataset (DOI: 10.1038/s41597-021-00986-9) :param mol: Molecule to be converted to MoleculeGraph :param critic_bonds: (optional) List of lists [a, b], where a and b are atom indices (0-indexed) :return: mol_graph, a MoleculeGraph """ mol_graph = MoleculeGraph.from_local_env_strategy(mol, OpenBabelNN()) mol_graph = metal_edge_extender(mol_graph) if critic_bonds: mg_edges = mol_graph.graph.edges() for bond in critic_bonds: bond.sort() if bond[0] != bond[1]: bond_tup = (bond[0], bond[1]) if bond_tup not in mg_edges: mol_graph.add_edge(bond_tup[0], bond_tup[1]) return mol_graph def get_graph_hash(mol: Molecule, node_attr: str | None = None): """ Return the Weisfeiler Lehman (WL) graph hash of the MoleculeGraph described by this molecule, using the OpenBabelNN strategy with extension for metal coordinate bonds :param mol: Molecule :param node_attr: Node attribute to be used to compute the WL hash :return: string of the WL graph hash """ mg = make_mol_graph(mol) return weisfeiler_lehman_graph_hash( mg.graph.to_undirected(), node_attr=node_attr, ) def get_molecule_id(mol: Molecule, node_attr: str | None = None): """ Return an MPculeID for a molecule, with the hash component based on a particular attribute of the molecule graph representation. :param mol: Molecule :param node_attr:Node attribute to be used to compute the WL hash :return: MPculeID """ graph_hash = get_graph_hash(mol, node_attr=node_attr) return MPculeID( "{}-{}-{}-{}".format( graph_hash, mol.composition.alphabetical_formula.replace(" ", ""), str(int(mol.charge)).replace("-", "m"), str(mol.spin_multiplicity), ) ) def jsanitize(obj, strict=False, allow_bson=False): """ This method cleans an input json-like object, either a list or a dict or some sequence, nested or otherwise, by converting all non-string dictionary keys (such as int and float) to strings, and also recursively encodes all objects using Monty's as_dict() protocol. Args: obj: input json-like object. strict (bool): This parameters sets the behavior when jsanitize encounters an object it does not understand. If strict is True, jsanitize will try to get the as_dict() attribute of the object. If no such attribute is found, an attribute error will be thrown. If strict is False, jsanitize will simply call str(object) to convert the object to a string representation. allow_bson (bool): This parameters sets the behavior when jsanitize encounters an bson supported type such as objectid and datetime. If True, such bson types will be ignored, allowing for proper insertion into MongoDb databases. Returns: Sanitized dict that can be json serialized. """ if allow_bson and ( isinstance(obj, (datetime.datetime, bytes)) or (bson is not None and isinstance(obj, bson.objectid.ObjectId)) ): return obj if isinstance(obj, (list, tuple, set)): return [jsanitize(i, strict=strict, allow_bson=allow_bson) for i in obj] if np is not None and isinstance(obj, np.ndarray): return [ jsanitize(i, strict=strict, allow_bson=allow_bson) for i in obj.tolist() ] if isinstance(obj, Enum): return obj.value if isinstance(obj, dict): return { k.__str__(): jsanitize(v, strict=strict, allow_bson=allow_bson) for k, v in obj.items() } if isinstance(obj, MSONable): return { k.__str__(): jsanitize(v, strict=strict, allow_bson=allow_bson) for k, v in obj.as_dict().items() } if isinstance(obj, BaseModel): return { k.__str__(): jsanitize(v, strict=strict, allow_bson=allow_bson) for k, v in obj.model_dump().items() } if isinstance(obj, (int, float)): if np.isnan(obj): return 0 return obj if obj is None: return None if not strict: return obj.__str__() if isinstance(obj, str): return obj.__str__() return jsanitize(obj.as_dict(), strict=strict, allow_bson=allow_bson) def utcnow() -> datetime.datetime: """Get UTC time right now.""" return datetime.datetime.now(datetime.timezone.utc) def convert_datetime( v: datetime.datetime | dict[str, str] | str | None, ) -> datetime.datetime: """Validate datetime-like objects. Parameters ----------- v : datetime, dict[str,str], str or None Returns ----------- datetime """ if not v: return utcnow() if isinstance(v, dict): if v.get("$date"): dt = datetime.datetime.fromisoformat(v["$date"]) if not dt.tzinfo: dt = dt.replace(tzinfo=datetime.timezone.utc) return dt if isinstance(v, str): dt = datetime.datetime.fromisoformat(v) if not dt.tzinfo: dt = dt.replace(tzinfo=datetime.timezone.utc) return dt v = MontyDecoder().process_decoded(v) if isinstance(v, datetime.datetime) and not v.tzinfo: v = v.replace(tzinfo=datetime.timezone.utc) return v # type: ignore[return-value] def get_hash_blocked( file_path: FSPathType, chunk_size: int = 4 * 1024 * 1024, hasher: Any | None = None ) -> str: """ Get the hash of a file in byte chunks. Parameters ----------- file_path : FSPathType chunk_size : int = 1,000,000 bytes (default) The byte chunk size to use in iteratively computing the hash. hahser : function to compute hashes. Defaults to blake3 if available, and MD5 if not. Returns ----------- The hash as a str """ if hasher is None: if blake3: hasher = blake3.blake3() else: hasher = hashlib.md5() with open(str(file_path), "rb") as f: while True: data = f.read(chunk_size) if not data: break hasher.update(data) return hasher.hexdigest() def dynamic_import(module_path: str) -> Any: """Import arbitrary module or object.""" paths = module_path.split(".") for i in range(len(paths), 0, -1): try: ob = import_module(".".join(paths[:i])) for path in paths[i:]: ob = getattr(ob, path) return ob except Exception: continue raise ValueError(f"Could not import string:\n{module_path}") def get_flat_models_from_model( model: BaseModel, known_models: set[BaseModel] = set() ) -> set[BaseModel]: """Get all sub-models from a pydantic model. Args: model (BaseModel): Pydantic model known_models (set[BaseModel]) : set of identified pydantic sub-models Returns: (set[BaseModel]): Set of pydantic models """ known_models = set() def get_sub_models(model: Any): if lenient_issubclass(model, BaseModel): known_models.add(model) for field_info in model.model_fields.values(): get_sub_models(field_info.annotation) else: for type_anno in get_args(model): get_sub_models(type_anno) get_sub_models(model) return known_models def requires_arrow(func: Callable) -> Callable: """Decorator for pyarrow-dependent functionality.""" def wrap(*args, **kwargs): if not ARROW_COMPATIBLE: raise ImportError( "You must `pip install pyarrow` to use this functionality." ) return func(*args, **kwargs) return wrap ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011028.9841135 emmet_core-0.87.2/emmet/core/vasp/0000755000175100017510000000000015240717725016417 5ustar00runnerrunner././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8282716 emmet_core-0.87.2/emmet/core/vasp/__init__.py0000644000175100017510000000000015240717720020511 0ustar00runnerrunner././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011028.9848013 emmet_core-0.87.2/emmet/core/vasp/calc_types/0000755000175100017510000000000015240717725020545 5ustar00runnerrunner././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8289607 emmet_core-0.87.2/emmet/core/vasp/calc_types/__init__.py0000644000175100017510000000043115240717720022647 0ustar00runnerrunner"""Module defining VASP calculation types.""" from emmet.core.vasp.calc_types.enums import CalcType, RunType, TaskType from emmet.core.vasp.calc_types.utils import calc_type, run_type, task_type __all__ = ["CalcType", "RunType", "TaskType", "calc_type", "run_type", "task_type"] ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8290114 emmet_core-0.87.2/emmet/core/vasp/calc_types/calc_types.yaml0000644000175100017510000000465515240717720023564 0ustar00runnerrunnerRUN_TYPES: LDA: # Note that GGA = CA is the same as GGA = PZ for VASP LDA: GGA: CA GGA: AM05: GGA: AM GGA: GGA: -- PBE: GGA: PE PBEsol: GGA: PS revPBE+PADE: GGA: RP optB86b: GGA: MK optB88: GGA: BO optPBE: GGA: OR revPBE: GGA: RE HF: B3LYP: AEXX: 0.2 AGGAC: 0.81 AGGAX: 0.72 ALDAC: 0.19 GGA: B3 LHFCALC: true HF: AEXX: 1.0 AGGAC: 0.0 AGGAX: 1.0 ALDAC: 0.0 LHFCALC: true HSE03: AEXX: 0.25 AGGAC: 1.0 AGGAX: 0.75 ALDAC: 1.0 HFSCREEN: 0.3 LHFCALC: true HSE06: AEXX: 0.25 AGGAC: 1.0 AGGAX: 0.75 ALDAC: 1.0 HFSCREEN: 0.2 LHFCALC: true PBE0: AEXX: 0.25 AGGAC: 1.0 AGGAX: 0.75 ALDAC: 1.0 LHFCALC: true # From the VASP manual, the default for setting LHFCALC = True is PBE0 # HFCus is the same as PBE0 HFCus: LHFCALC: true METAGGA: M06L: METAGGA: M06L MBJL: METAGGA: MBJL MS0: METAGGA: MS0 MS1: METAGGA: MS1 MS2: METAGGA: MS2 RTPSS: METAGGA: RTPSS SCAN: METAGGA: SCAN r2SCAN: METAGGA: R2SCAN TPSS: METAGGA: TPSS VDW: r2SCAN-rVV10: BPARAM: 15.7 LASPH: true LUSE_VDW: true METAGGA: R2SCAN SCAN-rVV10: BPARAM: 15.7 LASPH: true LUSE_VDW: true METAGGA: SCAN optB86b-vdW: AGGAC: 0.0 GGA: MK LASPH: true LUSE_VDW: true PARAM1: 0.1234 PARAM2: 1.0 optB88-vdW: AGGAC: 0.0 GGA: BO LUSE_VDW: true PARAM1: 0.1833333333 PARAM2: 0.22 optPBE-vdW: AGGAC: 0.0 GGA: OR LASPH: true LUSE_VDW: true rev-vdW-DF2: AGGAC: 0.0 GGA: MK LASPH: true LUSE_VDW: true PARAM1: 0.1234 PARAM2: 0.711357 Zab_vdW: -1.8867 revPBE-vdW: AGGAC: 0.0 GGA: RE LASPH: true LUSE_VDW: true vdW-DF2: AGGAC: 0.0 GGA: ML LASPH: true LUSE_VDW: true Zab_vdW: -1.8867 GW: G0W0: ALGO: Gw0 TASK_TYPES: - NSCF Line - SCF Line - NSCF Uniform - Dielectric - DFPT - DFPT Dielectric - NMR Nuclear Shielding - NMR Electric Field Gradient - Static - Structure Optimization - Deformation - Optic - Molecular Dynamics - Unrecognized ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8290114 emmet_core-0.87.2/emmet/core/vasp/calc_types/enums.py0000644000175100017510000014525215240717720022252 0ustar00runnerrunner""" Autogenerated Enums for VASP RunType, TaskType, and CalcType. Do not edit this by hand to add or remove enums. Instead, edit dev_scripts/generate_enums.py and/or emmet/core/vasp/calc_types/calc_types.yaml """ from emmet.core.types.enums import ValueEnum, IgnoreCaseEnum class RunType(IgnoreCaseEnum): """VASP calculation run types.""" AM05 = "AM05" AM05_U = "AM05+U" B3LYP = "B3LYP" B3LYP_U = "B3LYP+U" G0W0 = "G0W0" G0W0_U = "G0W0+U" GGA = "GGA" GGA_U = "GGA+U" HF = "HF" HFCus = "HFCus" HFCus_U = "HFCus+U" HF_U = "HF+U" HSE03 = "HSE03" HSE03_U = "HSE03+U" HSE06 = "HSE06" HSE06_U = "HSE06+U" LDA = "LDA" LDA_U = "LDA+U" M06L = "M06L" M06L_U = "M06L+U" MBJL = "MBJL" MBJL_U = "MBJL+U" MS0 = "MS0" MS0_U = "MS0+U" MS1 = "MS1" MS1_U = "MS1+U" MS2 = "MS2" MS2_U = "MS2+U" PBE = "PBE" PBE0 = "PBE0" PBE0_U = "PBE0+U" PBE_U = "PBE+U" PBEsol = "PBEsol" PBEsol_U = "PBEsol+U" RTPSS = "RTPSS" RTPSS_U = "RTPSS+U" SCAN = "SCAN" SCAN_U = "SCAN+U" SCAN_rVV10 = "SCAN-rVV10" SCAN_rVV10_U = "SCAN-rVV10+U" TPSS = "TPSS" TPSS_U = "TPSS+U" optB86b = "optB86b" optB86b_U = "optB86b+U" optB86b_vdW = "optB86b-vdW" optB86b_vdW_U = "optB86b-vdW+U" optB88 = "optB88" optB88_U = "optB88+U" optB88_vdW = "optB88-vdW" optB88_vdW_U = "optB88-vdW+U" optPBE = "optPBE" optPBE_U = "optPBE+U" optPBE_vdW = "optPBE-vdW" optPBE_vdW_U = "optPBE-vdW+U" r2SCAN = "r2SCAN" r2SCAN_U = "r2SCAN+U" r2SCAN_rVV10 = "r2SCAN-rVV10" r2SCAN_rVV10_U = "r2SCAN-rVV10+U" revPBE = "revPBE" revPBE_PADE = "revPBE+PADE" revPBE_PADE_U = "revPBE+PADE+U" revPBE_U = "revPBE+U" revPBE_vdW = "revPBE-vdW" revPBE_vdW_U = "revPBE-vdW+U" rev_vdW_DF2 = "rev-vdW-DF2" rev_vdW_DF2_U = "rev-vdW-DF2+U" vdW_DF2 = "vdW-DF2" vdW_DF2_U = "vdW-DF2+U" class TaskType(ValueEnum): """VASP calculation task types.""" DFPT = "DFPT" DFPT_Dielectric = "DFPT Dielectric" Deformation = "Deformation" Dielectric = "Dielectric" Molecular_Dynamics = "Molecular Dynamics" NMR_Electric_Field_Gradient = "NMR Electric Field Gradient" NMR_Nuclear_Shielding = "NMR Nuclear Shielding" NSCF_Line = "NSCF Line" NSCF_Uniform = "NSCF Uniform" Optic = "Optic" SCF_Line = "SCF Line" Static = "Static" Structure_Optimization = "Structure Optimization" Unrecognized = "Unrecognized" class CalcType(IgnoreCaseEnum): """VASP calculation types.""" AM05_DFPT = "AM05 DFPT" AM05_DFPT_Dielectric = "AM05 DFPT Dielectric" AM05_Deformation = "AM05 Deformation" AM05_Dielectric = "AM05 Dielectric" AM05_Molecular_Dynamics = "AM05 Molecular Dynamics" AM05_NMR_Electric_Field_Gradient = "AM05 NMR Electric Field Gradient" AM05_NMR_Nuclear_Shielding = "AM05 NMR Nuclear Shielding" AM05_NSCF_Line = "AM05 NSCF Line" AM05_NSCF_Uniform = "AM05 NSCF Uniform" AM05_Optic = "AM05 Optic" AM05_SCF_Line = "AM05 SCF Line" AM05_Static = "AM05 Static" AM05_Structure_Optimization = "AM05 Structure Optimization" AM05_U_DFPT = "AM05+U DFPT" AM05_U_DFPT_Dielectric = "AM05+U DFPT Dielectric" AM05_U_Deformation = "AM05+U Deformation" AM05_U_Dielectric = "AM05+U Dielectric" AM05_U_Molecular_Dynamics = "AM05+U Molecular Dynamics" AM05_U_NMR_Electric_Field_Gradient = "AM05+U NMR Electric Field Gradient" AM05_U_NMR_Nuclear_Shielding = "AM05+U NMR Nuclear Shielding" AM05_U_NSCF_Line = "AM05+U NSCF Line" AM05_U_NSCF_Uniform = "AM05+U NSCF Uniform" AM05_U_Optic = "AM05+U Optic" AM05_U_SCF_Line = "AM05+U SCF Line" AM05_U_Static = "AM05+U Static" AM05_U_Structure_Optimization = "AM05+U Structure Optimization" AM05_U_Unrecognized = "AM05+U Unrecognized" AM05_Unrecognized = "AM05 Unrecognized" B3LYP_DFPT = "B3LYP DFPT" B3LYP_DFPT_Dielectric = "B3LYP DFPT Dielectric" B3LYP_Deformation = "B3LYP Deformation" B3LYP_Dielectric = "B3LYP Dielectric" B3LYP_Molecular_Dynamics = "B3LYP Molecular Dynamics" B3LYP_NMR_Electric_Field_Gradient = "B3LYP NMR Electric Field Gradient" B3LYP_NMR_Nuclear_Shielding = "B3LYP NMR Nuclear Shielding" B3LYP_NSCF_Line = "B3LYP NSCF Line" B3LYP_NSCF_Uniform = "B3LYP NSCF Uniform" B3LYP_Optic = "B3LYP Optic" B3LYP_SCF_Line = "B3LYP SCF Line" B3LYP_Static = "B3LYP Static" B3LYP_Structure_Optimization = "B3LYP Structure Optimization" B3LYP_U_DFPT = "B3LYP+U DFPT" B3LYP_U_DFPT_Dielectric = "B3LYP+U DFPT Dielectric" B3LYP_U_Deformation = "B3LYP+U Deformation" B3LYP_U_Dielectric = "B3LYP+U Dielectric" B3LYP_U_Molecular_Dynamics = "B3LYP+U Molecular Dynamics" B3LYP_U_NMR_Electric_Field_Gradient = "B3LYP+U NMR Electric Field Gradient" B3LYP_U_NMR_Nuclear_Shielding = "B3LYP+U NMR Nuclear Shielding" B3LYP_U_NSCF_Line = "B3LYP+U NSCF Line" B3LYP_U_NSCF_Uniform = "B3LYP+U NSCF Uniform" B3LYP_U_Optic = "B3LYP+U Optic" B3LYP_U_SCF_Line = "B3LYP+U SCF Line" B3LYP_U_Static = "B3LYP+U Static" B3LYP_U_Structure_Optimization = "B3LYP+U Structure Optimization" B3LYP_U_Unrecognized = "B3LYP+U Unrecognized" B3LYP_Unrecognized = "B3LYP Unrecognized" G0W0_DFPT = "G0W0 DFPT" G0W0_DFPT_Dielectric = "G0W0 DFPT Dielectric" G0W0_Deformation = "G0W0 Deformation" G0W0_Dielectric = "G0W0 Dielectric" G0W0_Molecular_Dynamics = "G0W0 Molecular Dynamics" G0W0_NMR_Electric_Field_Gradient = "G0W0 NMR Electric Field Gradient" G0W0_NMR_Nuclear_Shielding = "G0W0 NMR Nuclear Shielding" G0W0_NSCF_Line = "G0W0 NSCF Line" G0W0_NSCF_Uniform = "G0W0 NSCF Uniform" G0W0_Optic = "G0W0 Optic" G0W0_SCF_Line = "G0W0 SCF Line" G0W0_Static = "G0W0 Static" G0W0_Structure_Optimization = "G0W0 Structure Optimization" G0W0_U_DFPT = "G0W0+U DFPT" G0W0_U_DFPT_Dielectric = "G0W0+U DFPT Dielectric" G0W0_U_Deformation = "G0W0+U Deformation" G0W0_U_Dielectric = "G0W0+U Dielectric" G0W0_U_Molecular_Dynamics = "G0W0+U Molecular Dynamics" G0W0_U_NMR_Electric_Field_Gradient = "G0W0+U NMR Electric Field Gradient" G0W0_U_NMR_Nuclear_Shielding = "G0W0+U NMR Nuclear Shielding" G0W0_U_NSCF_Line = "G0W0+U NSCF Line" G0W0_U_NSCF_Uniform = "G0W0+U NSCF Uniform" G0W0_U_Optic = "G0W0+U Optic" G0W0_U_SCF_Line = "G0W0+U SCF Line" G0W0_U_Static = "G0W0+U Static" G0W0_U_Structure_Optimization = "G0W0+U Structure Optimization" G0W0_U_Unrecognized = "G0W0+U Unrecognized" G0W0_Unrecognized = "G0W0 Unrecognized" GGA_DFPT = "GGA DFPT" GGA_DFPT_Dielectric = "GGA DFPT Dielectric" GGA_Deformation = "GGA Deformation" GGA_Dielectric = "GGA Dielectric" GGA_Molecular_Dynamics = "GGA Molecular Dynamics" GGA_NMR_Electric_Field_Gradient = "GGA NMR Electric Field Gradient" GGA_NMR_Nuclear_Shielding = "GGA NMR Nuclear Shielding" GGA_NSCF_Line = "GGA NSCF Line" GGA_NSCF_Uniform = "GGA NSCF Uniform" GGA_Optic = "GGA Optic" GGA_SCF_Line = "GGA SCF Line" GGA_Static = "GGA Static" GGA_Structure_Optimization = "GGA Structure Optimization" GGA_U_DFPT = "GGA+U DFPT" GGA_U_DFPT_Dielectric = "GGA+U DFPT Dielectric" GGA_U_Deformation = "GGA+U Deformation" GGA_U_Dielectric = "GGA+U Dielectric" GGA_U_Molecular_Dynamics = "GGA+U Molecular Dynamics" GGA_U_NMR_Electric_Field_Gradient = "GGA+U NMR Electric Field Gradient" GGA_U_NMR_Nuclear_Shielding = "GGA+U NMR Nuclear Shielding" GGA_U_NSCF_Line = "GGA+U NSCF Line" GGA_U_NSCF_Uniform = "GGA+U NSCF Uniform" GGA_U_Optic = "GGA+U Optic" GGA_U_SCF_Line = "GGA+U SCF Line" GGA_U_Static = "GGA+U Static" GGA_U_Structure_Optimization = "GGA+U Structure Optimization" GGA_U_Unrecognized = "GGA+U Unrecognized" GGA_Unrecognized = "GGA Unrecognized" HFCus_DFPT = "HFCus DFPT" HFCus_DFPT_Dielectric = "HFCus DFPT Dielectric" HFCus_Deformation = "HFCus Deformation" HFCus_Dielectric = "HFCus Dielectric" HFCus_Molecular_Dynamics = "HFCus Molecular Dynamics" HFCus_NMR_Electric_Field_Gradient = "HFCus NMR Electric Field Gradient" HFCus_NMR_Nuclear_Shielding = "HFCus NMR Nuclear Shielding" HFCus_NSCF_Line = "HFCus NSCF Line" HFCus_NSCF_Uniform = "HFCus NSCF Uniform" HFCus_Optic = "HFCus Optic" HFCus_SCF_Line = "HFCus SCF Line" HFCus_Static = "HFCus Static" HFCus_Structure_Optimization = "HFCus Structure Optimization" HFCus_U_DFPT = "HFCus+U DFPT" HFCus_U_DFPT_Dielectric = "HFCus+U DFPT Dielectric" HFCus_U_Deformation = "HFCus+U Deformation" HFCus_U_Dielectric = "HFCus+U Dielectric" HFCus_U_Molecular_Dynamics = "HFCus+U Molecular Dynamics" HFCus_U_NMR_Electric_Field_Gradient = "HFCus+U NMR Electric Field Gradient" HFCus_U_NMR_Nuclear_Shielding = "HFCus+U NMR Nuclear Shielding" HFCus_U_NSCF_Line = "HFCus+U NSCF Line" HFCus_U_NSCF_Uniform = "HFCus+U NSCF Uniform" HFCus_U_Optic = "HFCus+U Optic" HFCus_U_SCF_Line = "HFCus+U SCF Line" HFCus_U_Static = "HFCus+U Static" HFCus_U_Structure_Optimization = "HFCus+U Structure Optimization" HFCus_U_Unrecognized = "HFCus+U Unrecognized" HFCus_Unrecognized = "HFCus Unrecognized" HF_DFPT = "HF DFPT" HF_DFPT_Dielectric = "HF DFPT Dielectric" HF_Deformation = "HF Deformation" HF_Dielectric = "HF Dielectric" HF_Molecular_Dynamics = "HF Molecular Dynamics" HF_NMR_Electric_Field_Gradient = "HF NMR Electric Field Gradient" HF_NMR_Nuclear_Shielding = "HF NMR Nuclear Shielding" HF_NSCF_Line = "HF NSCF Line" HF_NSCF_Uniform = "HF NSCF Uniform" HF_Optic = "HF Optic" HF_SCF_Line = "HF SCF Line" HF_Static = "HF Static" HF_Structure_Optimization = "HF Structure Optimization" HF_U_DFPT = "HF+U DFPT" HF_U_DFPT_Dielectric = "HF+U DFPT Dielectric" HF_U_Deformation = "HF+U Deformation" HF_U_Dielectric = "HF+U Dielectric" HF_U_Molecular_Dynamics = "HF+U Molecular Dynamics" HF_U_NMR_Electric_Field_Gradient = "HF+U NMR Electric Field Gradient" HF_U_NMR_Nuclear_Shielding = "HF+U NMR Nuclear Shielding" HF_U_NSCF_Line = "HF+U NSCF Line" HF_U_NSCF_Uniform = "HF+U NSCF Uniform" HF_U_Optic = "HF+U Optic" HF_U_SCF_Line = "HF+U SCF Line" HF_U_Static = "HF+U Static" HF_U_Structure_Optimization = "HF+U Structure Optimization" HF_U_Unrecognized = "HF+U Unrecognized" HF_Unrecognized = "HF Unrecognized" HSE03_DFPT = "HSE03 DFPT" HSE03_DFPT_Dielectric = "HSE03 DFPT Dielectric" HSE03_Deformation = "HSE03 Deformation" HSE03_Dielectric = "HSE03 Dielectric" HSE03_Molecular_Dynamics = "HSE03 Molecular Dynamics" HSE03_NMR_Electric_Field_Gradient = "HSE03 NMR Electric Field Gradient" HSE03_NMR_Nuclear_Shielding = "HSE03 NMR Nuclear Shielding" HSE03_NSCF_Line = "HSE03 NSCF Line" HSE03_NSCF_Uniform = "HSE03 NSCF Uniform" HSE03_Optic = "HSE03 Optic" HSE03_SCF_Line = "HSE03 SCF Line" HSE03_Static = "HSE03 Static" HSE03_Structure_Optimization = "HSE03 Structure Optimization" HSE03_U_DFPT = "HSE03+U DFPT" HSE03_U_DFPT_Dielectric = "HSE03+U DFPT Dielectric" HSE03_U_Deformation = "HSE03+U Deformation" HSE03_U_Dielectric = "HSE03+U Dielectric" HSE03_U_Molecular_Dynamics = "HSE03+U Molecular Dynamics" HSE03_U_NMR_Electric_Field_Gradient = "HSE03+U NMR Electric Field Gradient" HSE03_U_NMR_Nuclear_Shielding = "HSE03+U NMR Nuclear Shielding" HSE03_U_NSCF_Line = "HSE03+U NSCF Line" HSE03_U_NSCF_Uniform = "HSE03+U NSCF Uniform" HSE03_U_Optic = "HSE03+U Optic" HSE03_U_SCF_Line = "HSE03+U SCF Line" HSE03_U_Static = "HSE03+U Static" HSE03_U_Structure_Optimization = "HSE03+U Structure Optimization" HSE03_U_Unrecognized = "HSE03+U Unrecognized" HSE03_Unrecognized = "HSE03 Unrecognized" HSE06_DFPT = "HSE06 DFPT" HSE06_DFPT_Dielectric = "HSE06 DFPT Dielectric" HSE06_Deformation = "HSE06 Deformation" HSE06_Dielectric = "HSE06 Dielectric" HSE06_Molecular_Dynamics = "HSE06 Molecular Dynamics" HSE06_NMR_Electric_Field_Gradient = "HSE06 NMR Electric Field Gradient" HSE06_NMR_Nuclear_Shielding = "HSE06 NMR Nuclear Shielding" HSE06_NSCF_Line = "HSE06 NSCF Line" HSE06_NSCF_Uniform = "HSE06 NSCF Uniform" HSE06_Optic = "HSE06 Optic" HSE06_SCF_Line = "HSE06 SCF Line" HSE06_Static = "HSE06 Static" HSE06_Structure_Optimization = "HSE06 Structure Optimization" HSE06_U_DFPT = "HSE06+U DFPT" HSE06_U_DFPT_Dielectric = "HSE06+U DFPT Dielectric" HSE06_U_Deformation = "HSE06+U Deformation" HSE06_U_Dielectric = "HSE06+U Dielectric" HSE06_U_Molecular_Dynamics = "HSE06+U Molecular Dynamics" HSE06_U_NMR_Electric_Field_Gradient = "HSE06+U NMR Electric Field Gradient" HSE06_U_NMR_Nuclear_Shielding = "HSE06+U NMR Nuclear Shielding" HSE06_U_NSCF_Line = "HSE06+U NSCF Line" HSE06_U_NSCF_Uniform = "HSE06+U NSCF Uniform" HSE06_U_Optic = "HSE06+U Optic" HSE06_U_SCF_Line = "HSE06+U SCF Line" HSE06_U_Static = "HSE06+U Static" HSE06_U_Structure_Optimization = "HSE06+U Structure Optimization" HSE06_U_Unrecognized = "HSE06+U Unrecognized" HSE06_Unrecognized = "HSE06 Unrecognized" LDA_DFPT = "LDA DFPT" LDA_DFPT_Dielectric = "LDA DFPT Dielectric" LDA_Deformation = "LDA Deformation" LDA_Dielectric = "LDA Dielectric" LDA_Molecular_Dynamics = "LDA Molecular Dynamics" LDA_NMR_Electric_Field_Gradient = "LDA NMR Electric Field Gradient" LDA_NMR_Nuclear_Shielding = "LDA NMR Nuclear Shielding" LDA_NSCF_Line = "LDA NSCF Line" LDA_NSCF_Uniform = "LDA NSCF Uniform" LDA_Optic = "LDA Optic" LDA_SCF_Line = "LDA SCF Line" LDA_Static = "LDA Static" LDA_Structure_Optimization = "LDA Structure Optimization" LDA_U_DFPT = "LDA+U DFPT" LDA_U_DFPT_Dielectric = "LDA+U DFPT Dielectric" LDA_U_Deformation = "LDA+U Deformation" LDA_U_Dielectric = "LDA+U Dielectric" LDA_U_Molecular_Dynamics = "LDA+U Molecular Dynamics" LDA_U_NMR_Electric_Field_Gradient = "LDA+U NMR Electric Field Gradient" LDA_U_NMR_Nuclear_Shielding = "LDA+U NMR Nuclear Shielding" LDA_U_NSCF_Line = "LDA+U NSCF Line" LDA_U_NSCF_Uniform = "LDA+U NSCF Uniform" LDA_U_Optic = "LDA+U Optic" LDA_U_SCF_Line = "LDA+U SCF Line" LDA_U_Static = "LDA+U Static" LDA_U_Structure_Optimization = "LDA+U Structure Optimization" LDA_U_Unrecognized = "LDA+U Unrecognized" LDA_Unrecognized = "LDA Unrecognized" M06L_DFPT = "M06L DFPT" M06L_DFPT_Dielectric = "M06L DFPT Dielectric" M06L_Deformation = "M06L Deformation" M06L_Dielectric = "M06L Dielectric" M06L_Molecular_Dynamics = "M06L Molecular Dynamics" M06L_NMR_Electric_Field_Gradient = "M06L NMR Electric Field Gradient" M06L_NMR_Nuclear_Shielding = "M06L NMR Nuclear Shielding" M06L_NSCF_Line = "M06L NSCF Line" M06L_NSCF_Uniform = "M06L NSCF Uniform" M06L_Optic = "M06L Optic" M06L_SCF_Line = "M06L SCF Line" M06L_Static = "M06L Static" M06L_Structure_Optimization = "M06L Structure Optimization" M06L_U_DFPT = "M06L+U DFPT" M06L_U_DFPT_Dielectric = "M06L+U DFPT Dielectric" M06L_U_Deformation = "M06L+U Deformation" M06L_U_Dielectric = "M06L+U Dielectric" M06L_U_Molecular_Dynamics = "M06L+U Molecular Dynamics" M06L_U_NMR_Electric_Field_Gradient = "M06L+U NMR Electric Field Gradient" M06L_U_NMR_Nuclear_Shielding = "M06L+U NMR Nuclear Shielding" M06L_U_NSCF_Line = "M06L+U NSCF Line" M06L_U_NSCF_Uniform = "M06L+U NSCF Uniform" M06L_U_Optic = "M06L+U Optic" M06L_U_SCF_Line = "M06L+U SCF Line" M06L_U_Static = "M06L+U Static" M06L_U_Structure_Optimization = "M06L+U Structure Optimization" M06L_U_Unrecognized = "M06L+U Unrecognized" M06L_Unrecognized = "M06L Unrecognized" MBJL_DFPT = "MBJL DFPT" MBJL_DFPT_Dielectric = "MBJL DFPT Dielectric" MBJL_Deformation = "MBJL Deformation" MBJL_Dielectric = "MBJL Dielectric" MBJL_Molecular_Dynamics = "MBJL Molecular Dynamics" MBJL_NMR_Electric_Field_Gradient = "MBJL NMR Electric Field Gradient" MBJL_NMR_Nuclear_Shielding = "MBJL NMR Nuclear Shielding" MBJL_NSCF_Line = "MBJL NSCF Line" MBJL_NSCF_Uniform = "MBJL NSCF Uniform" MBJL_Optic = "MBJL Optic" MBJL_SCF_Line = "MBJL SCF Line" MBJL_Static = "MBJL Static" MBJL_Structure_Optimization = "MBJL Structure Optimization" MBJL_U_DFPT = "MBJL+U DFPT" MBJL_U_DFPT_Dielectric = "MBJL+U DFPT Dielectric" MBJL_U_Deformation = "MBJL+U Deformation" MBJL_U_Dielectric = "MBJL+U Dielectric" MBJL_U_Molecular_Dynamics = "MBJL+U Molecular Dynamics" MBJL_U_NMR_Electric_Field_Gradient = "MBJL+U NMR Electric Field Gradient" MBJL_U_NMR_Nuclear_Shielding = "MBJL+U NMR Nuclear Shielding" MBJL_U_NSCF_Line = "MBJL+U NSCF Line" MBJL_U_NSCF_Uniform = "MBJL+U NSCF Uniform" MBJL_U_Optic = "MBJL+U Optic" MBJL_U_SCF_Line = "MBJL+U SCF Line" MBJL_U_Static = "MBJL+U Static" MBJL_U_Structure_Optimization = "MBJL+U Structure Optimization" MBJL_U_Unrecognized = "MBJL+U Unrecognized" MBJL_Unrecognized = "MBJL Unrecognized" MS0_DFPT = "MS0 DFPT" MS0_DFPT_Dielectric = "MS0 DFPT Dielectric" MS0_Deformation = "MS0 Deformation" MS0_Dielectric = "MS0 Dielectric" MS0_Molecular_Dynamics = "MS0 Molecular Dynamics" MS0_NMR_Electric_Field_Gradient = "MS0 NMR Electric Field Gradient" MS0_NMR_Nuclear_Shielding = "MS0 NMR Nuclear Shielding" MS0_NSCF_Line = "MS0 NSCF Line" MS0_NSCF_Uniform = "MS0 NSCF Uniform" MS0_Optic = "MS0 Optic" MS0_SCF_Line = "MS0 SCF Line" MS0_Static = "MS0 Static" MS0_Structure_Optimization = "MS0 Structure Optimization" MS0_U_DFPT = "MS0+U DFPT" MS0_U_DFPT_Dielectric = "MS0+U DFPT Dielectric" MS0_U_Deformation = "MS0+U Deformation" MS0_U_Dielectric = "MS0+U Dielectric" MS0_U_Molecular_Dynamics = "MS0+U Molecular Dynamics" MS0_U_NMR_Electric_Field_Gradient = "MS0+U NMR Electric Field Gradient" MS0_U_NMR_Nuclear_Shielding = "MS0+U NMR Nuclear Shielding" MS0_U_NSCF_Line = "MS0+U NSCF Line" MS0_U_NSCF_Uniform = "MS0+U NSCF Uniform" MS0_U_Optic = "MS0+U Optic" MS0_U_SCF_Line = "MS0+U SCF Line" MS0_U_Static = "MS0+U Static" MS0_U_Structure_Optimization = "MS0+U Structure Optimization" MS0_U_Unrecognized = "MS0+U Unrecognized" MS0_Unrecognized = "MS0 Unrecognized" MS1_DFPT = "MS1 DFPT" MS1_DFPT_Dielectric = "MS1 DFPT Dielectric" MS1_Deformation = "MS1 Deformation" MS1_Dielectric = "MS1 Dielectric" MS1_Molecular_Dynamics = "MS1 Molecular Dynamics" MS1_NMR_Electric_Field_Gradient = "MS1 NMR Electric Field Gradient" MS1_NMR_Nuclear_Shielding = "MS1 NMR Nuclear Shielding" MS1_NSCF_Line = "MS1 NSCF Line" MS1_NSCF_Uniform = "MS1 NSCF Uniform" MS1_Optic = "MS1 Optic" MS1_SCF_Line = "MS1 SCF Line" MS1_Static = "MS1 Static" MS1_Structure_Optimization = "MS1 Structure Optimization" MS1_U_DFPT = "MS1+U DFPT" MS1_U_DFPT_Dielectric = "MS1+U DFPT Dielectric" MS1_U_Deformation = "MS1+U Deformation" MS1_U_Dielectric = "MS1+U Dielectric" MS1_U_Molecular_Dynamics = "MS1+U Molecular Dynamics" MS1_U_NMR_Electric_Field_Gradient = "MS1+U NMR Electric Field Gradient" MS1_U_NMR_Nuclear_Shielding = "MS1+U NMR Nuclear Shielding" MS1_U_NSCF_Line = "MS1+U NSCF Line" MS1_U_NSCF_Uniform = "MS1+U NSCF Uniform" MS1_U_Optic = "MS1+U Optic" MS1_U_SCF_Line = "MS1+U SCF Line" MS1_U_Static = "MS1+U Static" MS1_U_Structure_Optimization = "MS1+U Structure Optimization" MS1_U_Unrecognized = "MS1+U Unrecognized" MS1_Unrecognized = "MS1 Unrecognized" MS2_DFPT = "MS2 DFPT" MS2_DFPT_Dielectric = "MS2 DFPT Dielectric" MS2_Deformation = "MS2 Deformation" MS2_Dielectric = "MS2 Dielectric" MS2_Molecular_Dynamics = "MS2 Molecular Dynamics" MS2_NMR_Electric_Field_Gradient = "MS2 NMR Electric Field Gradient" MS2_NMR_Nuclear_Shielding = "MS2 NMR Nuclear Shielding" MS2_NSCF_Line = "MS2 NSCF Line" MS2_NSCF_Uniform = "MS2 NSCF Uniform" MS2_Optic = "MS2 Optic" MS2_SCF_Line = "MS2 SCF Line" MS2_Static = "MS2 Static" MS2_Structure_Optimization = "MS2 Structure Optimization" MS2_U_DFPT = "MS2+U DFPT" MS2_U_DFPT_Dielectric = "MS2+U DFPT Dielectric" MS2_U_Deformation = "MS2+U Deformation" MS2_U_Dielectric = "MS2+U Dielectric" MS2_U_Molecular_Dynamics = "MS2+U Molecular Dynamics" MS2_U_NMR_Electric_Field_Gradient = "MS2+U NMR Electric Field Gradient" MS2_U_NMR_Nuclear_Shielding = "MS2+U NMR Nuclear Shielding" MS2_U_NSCF_Line = "MS2+U NSCF Line" MS2_U_NSCF_Uniform = "MS2+U NSCF Uniform" MS2_U_Optic = "MS2+U Optic" MS2_U_SCF_Line = "MS2+U SCF Line" MS2_U_Static = "MS2+U Static" MS2_U_Structure_Optimization = "MS2+U Structure Optimization" MS2_U_Unrecognized = "MS2+U Unrecognized" MS2_Unrecognized = "MS2 Unrecognized" PBE0_DFPT = "PBE0 DFPT" PBE0_DFPT_Dielectric = "PBE0 DFPT Dielectric" PBE0_Deformation = "PBE0 Deformation" PBE0_Dielectric = "PBE0 Dielectric" PBE0_Molecular_Dynamics = "PBE0 Molecular Dynamics" PBE0_NMR_Electric_Field_Gradient = "PBE0 NMR Electric Field Gradient" PBE0_NMR_Nuclear_Shielding = "PBE0 NMR Nuclear Shielding" PBE0_NSCF_Line = "PBE0 NSCF Line" PBE0_NSCF_Uniform = "PBE0 NSCF Uniform" PBE0_Optic = "PBE0 Optic" PBE0_SCF_Line = "PBE0 SCF Line" PBE0_Static = "PBE0 Static" PBE0_Structure_Optimization = "PBE0 Structure Optimization" PBE0_U_DFPT = "PBE0+U DFPT" PBE0_U_DFPT_Dielectric = "PBE0+U DFPT Dielectric" PBE0_U_Deformation = "PBE0+U Deformation" PBE0_U_Dielectric = "PBE0+U Dielectric" PBE0_U_Molecular_Dynamics = "PBE0+U Molecular Dynamics" PBE0_U_NMR_Electric_Field_Gradient = "PBE0+U NMR Electric Field Gradient" PBE0_U_NMR_Nuclear_Shielding = "PBE0+U NMR Nuclear Shielding" PBE0_U_NSCF_Line = "PBE0+U NSCF Line" PBE0_U_NSCF_Uniform = "PBE0+U NSCF Uniform" PBE0_U_Optic = "PBE0+U Optic" PBE0_U_SCF_Line = "PBE0+U SCF Line" PBE0_U_Static = "PBE0+U Static" PBE0_U_Structure_Optimization = "PBE0+U Structure Optimization" PBE0_U_Unrecognized = "PBE0+U Unrecognized" PBE0_Unrecognized = "PBE0 Unrecognized" PBE_DFPT = "PBE DFPT" PBE_DFPT_Dielectric = "PBE DFPT Dielectric" PBE_Deformation = "PBE Deformation" PBE_Dielectric = "PBE Dielectric" PBE_Molecular_Dynamics = "PBE Molecular Dynamics" PBE_NMR_Electric_Field_Gradient = "PBE NMR Electric Field Gradient" PBE_NMR_Nuclear_Shielding = "PBE NMR Nuclear Shielding" PBE_NSCF_Line = "PBE NSCF Line" PBE_NSCF_Uniform = "PBE NSCF Uniform" PBE_Optic = "PBE Optic" PBE_SCF_Line = "PBE SCF Line" PBE_Static = "PBE Static" PBE_Structure_Optimization = "PBE Structure Optimization" PBE_U_DFPT = "PBE+U DFPT" PBE_U_DFPT_Dielectric = "PBE+U DFPT Dielectric" PBE_U_Deformation = "PBE+U Deformation" PBE_U_Dielectric = "PBE+U Dielectric" PBE_U_Molecular_Dynamics = "PBE+U Molecular Dynamics" PBE_U_NMR_Electric_Field_Gradient = "PBE+U NMR Electric Field Gradient" PBE_U_NMR_Nuclear_Shielding = "PBE+U NMR Nuclear Shielding" PBE_U_NSCF_Line = "PBE+U NSCF Line" PBE_U_NSCF_Uniform = "PBE+U NSCF Uniform" PBE_U_Optic = "PBE+U Optic" PBE_U_SCF_Line = "PBE+U SCF Line" PBE_U_Static = "PBE+U Static" PBE_U_Structure_Optimization = "PBE+U Structure Optimization" PBE_U_Unrecognized = "PBE+U Unrecognized" PBE_Unrecognized = "PBE Unrecognized" PBEsol_DFPT = "PBEsol DFPT" PBEsol_DFPT_Dielectric = "PBEsol DFPT Dielectric" PBEsol_Deformation = "PBEsol Deformation" PBEsol_Dielectric = "PBEsol Dielectric" PBEsol_Molecular_Dynamics = "PBEsol Molecular Dynamics" PBEsol_NMR_Electric_Field_Gradient = "PBEsol NMR Electric Field Gradient" PBEsol_NMR_Nuclear_Shielding = "PBEsol NMR Nuclear Shielding" PBEsol_NSCF_Line = "PBEsol NSCF Line" PBEsol_NSCF_Uniform = "PBEsol NSCF Uniform" PBEsol_Optic = "PBEsol Optic" PBEsol_SCF_Line = "PBEsol SCF Line" PBEsol_Static = "PBEsol Static" PBEsol_Structure_Optimization = "PBEsol Structure Optimization" PBEsol_U_DFPT = "PBEsol+U DFPT" PBEsol_U_DFPT_Dielectric = "PBEsol+U DFPT Dielectric" PBEsol_U_Deformation = "PBEsol+U Deformation" PBEsol_U_Dielectric = "PBEsol+U Dielectric" PBEsol_U_Molecular_Dynamics = "PBEsol+U Molecular Dynamics" PBEsol_U_NMR_Electric_Field_Gradient = "PBEsol+U NMR Electric Field Gradient" PBEsol_U_NMR_Nuclear_Shielding = "PBEsol+U NMR Nuclear Shielding" PBEsol_U_NSCF_Line = "PBEsol+U NSCF Line" PBEsol_U_NSCF_Uniform = "PBEsol+U NSCF Uniform" PBEsol_U_Optic = "PBEsol+U Optic" PBEsol_U_SCF_Line = "PBEsol+U SCF Line" PBEsol_U_Static = "PBEsol+U Static" PBEsol_U_Structure_Optimization = "PBEsol+U Structure Optimization" PBEsol_U_Unrecognized = "PBEsol+U Unrecognized" PBEsol_Unrecognized = "PBEsol Unrecognized" RTPSS_DFPT = "RTPSS DFPT" RTPSS_DFPT_Dielectric = "RTPSS DFPT Dielectric" RTPSS_Deformation = "RTPSS Deformation" RTPSS_Dielectric = "RTPSS Dielectric" RTPSS_Molecular_Dynamics = "RTPSS Molecular Dynamics" RTPSS_NMR_Electric_Field_Gradient = "RTPSS NMR Electric Field Gradient" RTPSS_NMR_Nuclear_Shielding = "RTPSS NMR Nuclear Shielding" RTPSS_NSCF_Line = "RTPSS NSCF Line" RTPSS_NSCF_Uniform = "RTPSS NSCF Uniform" RTPSS_Optic = "RTPSS Optic" RTPSS_SCF_Line = "RTPSS SCF Line" RTPSS_Static = "RTPSS Static" RTPSS_Structure_Optimization = "RTPSS Structure Optimization" RTPSS_U_DFPT = "RTPSS+U DFPT" RTPSS_U_DFPT_Dielectric = "RTPSS+U DFPT Dielectric" RTPSS_U_Deformation = "RTPSS+U Deformation" RTPSS_U_Dielectric = "RTPSS+U Dielectric" RTPSS_U_Molecular_Dynamics = "RTPSS+U Molecular Dynamics" RTPSS_U_NMR_Electric_Field_Gradient = "RTPSS+U NMR Electric Field Gradient" RTPSS_U_NMR_Nuclear_Shielding = "RTPSS+U NMR Nuclear Shielding" RTPSS_U_NSCF_Line = "RTPSS+U NSCF Line" RTPSS_U_NSCF_Uniform = "RTPSS+U NSCF Uniform" RTPSS_U_Optic = "RTPSS+U Optic" RTPSS_U_SCF_Line = "RTPSS+U SCF Line" RTPSS_U_Static = "RTPSS+U Static" RTPSS_U_Structure_Optimization = "RTPSS+U Structure Optimization" RTPSS_U_Unrecognized = "RTPSS+U Unrecognized" RTPSS_Unrecognized = "RTPSS Unrecognized" SCAN_DFPT = "SCAN DFPT" SCAN_DFPT_Dielectric = "SCAN DFPT Dielectric" SCAN_Deformation = "SCAN Deformation" SCAN_Dielectric = "SCAN Dielectric" SCAN_Molecular_Dynamics = "SCAN Molecular Dynamics" SCAN_NMR_Electric_Field_Gradient = "SCAN NMR Electric Field Gradient" SCAN_NMR_Nuclear_Shielding = "SCAN NMR Nuclear Shielding" SCAN_NSCF_Line = "SCAN NSCF Line" SCAN_NSCF_Uniform = "SCAN NSCF Uniform" SCAN_Optic = "SCAN Optic" SCAN_SCF_Line = "SCAN SCF Line" SCAN_Static = "SCAN Static" SCAN_Structure_Optimization = "SCAN Structure Optimization" SCAN_U_DFPT = "SCAN+U DFPT" SCAN_U_DFPT_Dielectric = "SCAN+U DFPT Dielectric" SCAN_U_Deformation = "SCAN+U Deformation" SCAN_U_Dielectric = "SCAN+U Dielectric" SCAN_U_Molecular_Dynamics = "SCAN+U Molecular Dynamics" SCAN_U_NMR_Electric_Field_Gradient = "SCAN+U NMR Electric Field Gradient" SCAN_U_NMR_Nuclear_Shielding = "SCAN+U NMR Nuclear Shielding" SCAN_U_NSCF_Line = "SCAN+U NSCF Line" SCAN_U_NSCF_Uniform = "SCAN+U NSCF Uniform" SCAN_U_Optic = "SCAN+U Optic" SCAN_U_SCF_Line = "SCAN+U SCF Line" SCAN_U_Static = "SCAN+U Static" SCAN_U_Structure_Optimization = "SCAN+U Structure Optimization" SCAN_U_Unrecognized = "SCAN+U Unrecognized" SCAN_Unrecognized = "SCAN Unrecognized" SCAN_rVV10_DFPT = "SCAN-rVV10 DFPT" SCAN_rVV10_DFPT_Dielectric = "SCAN-rVV10 DFPT Dielectric" SCAN_rVV10_Deformation = "SCAN-rVV10 Deformation" SCAN_rVV10_Dielectric = "SCAN-rVV10 Dielectric" SCAN_rVV10_Molecular_Dynamics = "SCAN-rVV10 Molecular Dynamics" SCAN_rVV10_NMR_Electric_Field_Gradient = "SCAN-rVV10 NMR Electric Field Gradient" SCAN_rVV10_NMR_Nuclear_Shielding = "SCAN-rVV10 NMR Nuclear Shielding" SCAN_rVV10_NSCF_Line = "SCAN-rVV10 NSCF Line" SCAN_rVV10_NSCF_Uniform = "SCAN-rVV10 NSCF Uniform" SCAN_rVV10_Optic = "SCAN-rVV10 Optic" SCAN_rVV10_SCF_Line = "SCAN-rVV10 SCF Line" SCAN_rVV10_Static = "SCAN-rVV10 Static" SCAN_rVV10_Structure_Optimization = "SCAN-rVV10 Structure Optimization" SCAN_rVV10_U_DFPT = "SCAN-rVV10+U DFPT" SCAN_rVV10_U_DFPT_Dielectric = "SCAN-rVV10+U DFPT Dielectric" SCAN_rVV10_U_Deformation = "SCAN-rVV10+U Deformation" SCAN_rVV10_U_Dielectric = "SCAN-rVV10+U Dielectric" SCAN_rVV10_U_Molecular_Dynamics = "SCAN-rVV10+U Molecular Dynamics" SCAN_rVV10_U_NMR_Electric_Field_Gradient = ( "SCAN-rVV10+U NMR Electric Field Gradient" ) SCAN_rVV10_U_NMR_Nuclear_Shielding = "SCAN-rVV10+U NMR Nuclear Shielding" SCAN_rVV10_U_NSCF_Line = "SCAN-rVV10+U NSCF Line" SCAN_rVV10_U_NSCF_Uniform = "SCAN-rVV10+U NSCF Uniform" SCAN_rVV10_U_Optic = "SCAN-rVV10+U Optic" SCAN_rVV10_U_SCF_Line = "SCAN-rVV10+U SCF Line" SCAN_rVV10_U_Static = "SCAN-rVV10+U Static" SCAN_rVV10_U_Structure_Optimization = "SCAN-rVV10+U Structure Optimization" SCAN_rVV10_U_Unrecognized = "SCAN-rVV10+U Unrecognized" SCAN_rVV10_Unrecognized = "SCAN-rVV10 Unrecognized" TPSS_DFPT = "TPSS DFPT" TPSS_DFPT_Dielectric = "TPSS DFPT Dielectric" TPSS_Deformation = "TPSS Deformation" TPSS_Dielectric = "TPSS Dielectric" TPSS_Molecular_Dynamics = "TPSS Molecular Dynamics" TPSS_NMR_Electric_Field_Gradient = "TPSS NMR Electric Field Gradient" TPSS_NMR_Nuclear_Shielding = "TPSS NMR Nuclear Shielding" TPSS_NSCF_Line = "TPSS NSCF Line" TPSS_NSCF_Uniform = "TPSS NSCF Uniform" TPSS_Optic = "TPSS Optic" TPSS_SCF_Line = "TPSS SCF Line" TPSS_Static = "TPSS Static" TPSS_Structure_Optimization = "TPSS Structure Optimization" TPSS_U_DFPT = "TPSS+U DFPT" TPSS_U_DFPT_Dielectric = "TPSS+U DFPT Dielectric" TPSS_U_Deformation = "TPSS+U Deformation" TPSS_U_Dielectric = "TPSS+U Dielectric" TPSS_U_Molecular_Dynamics = "TPSS+U Molecular Dynamics" TPSS_U_NMR_Electric_Field_Gradient = "TPSS+U NMR Electric Field Gradient" TPSS_U_NMR_Nuclear_Shielding = "TPSS+U NMR Nuclear Shielding" TPSS_U_NSCF_Line = "TPSS+U NSCF Line" TPSS_U_NSCF_Uniform = "TPSS+U NSCF Uniform" TPSS_U_Optic = "TPSS+U Optic" TPSS_U_SCF_Line = "TPSS+U SCF Line" TPSS_U_Static = "TPSS+U Static" TPSS_U_Structure_Optimization = "TPSS+U Structure Optimization" TPSS_U_Unrecognized = "TPSS+U Unrecognized" TPSS_Unrecognized = "TPSS Unrecognized" optB86b_DFPT = "optB86b DFPT" optB86b_DFPT_Dielectric = "optB86b DFPT Dielectric" optB86b_Deformation = "optB86b Deformation" optB86b_Dielectric = "optB86b Dielectric" optB86b_Molecular_Dynamics = "optB86b Molecular Dynamics" optB86b_NMR_Electric_Field_Gradient = "optB86b NMR Electric Field Gradient" optB86b_NMR_Nuclear_Shielding = "optB86b NMR Nuclear Shielding" optB86b_NSCF_Line = "optB86b NSCF Line" optB86b_NSCF_Uniform = "optB86b NSCF Uniform" optB86b_Optic = "optB86b Optic" optB86b_SCF_Line = "optB86b SCF Line" optB86b_Static = "optB86b Static" optB86b_Structure_Optimization = "optB86b Structure Optimization" optB86b_U_DFPT = "optB86b+U DFPT" optB86b_U_DFPT_Dielectric = "optB86b+U DFPT Dielectric" optB86b_U_Deformation = "optB86b+U Deformation" optB86b_U_Dielectric = "optB86b+U Dielectric" optB86b_U_Molecular_Dynamics = "optB86b+U Molecular Dynamics" optB86b_U_NMR_Electric_Field_Gradient = "optB86b+U NMR Electric Field Gradient" optB86b_U_NMR_Nuclear_Shielding = "optB86b+U NMR Nuclear Shielding" optB86b_U_NSCF_Line = "optB86b+U NSCF Line" optB86b_U_NSCF_Uniform = "optB86b+U NSCF Uniform" optB86b_U_Optic = "optB86b+U Optic" optB86b_U_SCF_Line = "optB86b+U SCF Line" optB86b_U_Static = "optB86b+U Static" optB86b_U_Structure_Optimization = "optB86b+U Structure Optimization" optB86b_U_Unrecognized = "optB86b+U Unrecognized" optB86b_Unrecognized = "optB86b Unrecognized" optB86b_vdW_DFPT = "optB86b-vdW DFPT" optB86b_vdW_DFPT_Dielectric = "optB86b-vdW DFPT Dielectric" optB86b_vdW_Deformation = "optB86b-vdW Deformation" optB86b_vdW_Dielectric = "optB86b-vdW Dielectric" optB86b_vdW_Molecular_Dynamics = "optB86b-vdW Molecular Dynamics" optB86b_vdW_NMR_Electric_Field_Gradient = "optB86b-vdW NMR Electric Field Gradient" optB86b_vdW_NMR_Nuclear_Shielding = "optB86b-vdW NMR Nuclear Shielding" optB86b_vdW_NSCF_Line = "optB86b-vdW NSCF Line" optB86b_vdW_NSCF_Uniform = "optB86b-vdW NSCF Uniform" optB86b_vdW_Optic = "optB86b-vdW Optic" optB86b_vdW_SCF_Line = "optB86b-vdW SCF Line" optB86b_vdW_Static = "optB86b-vdW Static" optB86b_vdW_Structure_Optimization = "optB86b-vdW Structure Optimization" optB86b_vdW_U_DFPT = "optB86b-vdW+U DFPT" optB86b_vdW_U_DFPT_Dielectric = "optB86b-vdW+U DFPT Dielectric" optB86b_vdW_U_Deformation = "optB86b-vdW+U Deformation" optB86b_vdW_U_Dielectric = "optB86b-vdW+U Dielectric" optB86b_vdW_U_Molecular_Dynamics = "optB86b-vdW+U Molecular Dynamics" optB86b_vdW_U_NMR_Electric_Field_Gradient = ( "optB86b-vdW+U NMR Electric Field Gradient" ) optB86b_vdW_U_NMR_Nuclear_Shielding = "optB86b-vdW+U NMR Nuclear Shielding" optB86b_vdW_U_NSCF_Line = "optB86b-vdW+U NSCF Line" optB86b_vdW_U_NSCF_Uniform = "optB86b-vdW+U NSCF Uniform" optB86b_vdW_U_Optic = "optB86b-vdW+U Optic" optB86b_vdW_U_SCF_Line = "optB86b-vdW+U SCF Line" optB86b_vdW_U_Static = "optB86b-vdW+U Static" optB86b_vdW_U_Structure_Optimization = "optB86b-vdW+U Structure Optimization" optB86b_vdW_U_Unrecognized = "optB86b-vdW+U Unrecognized" optB86b_vdW_Unrecognized = "optB86b-vdW Unrecognized" optB88_DFPT = "optB88 DFPT" optB88_DFPT_Dielectric = "optB88 DFPT Dielectric" optB88_Deformation = "optB88 Deformation" optB88_Dielectric = "optB88 Dielectric" optB88_Molecular_Dynamics = "optB88 Molecular Dynamics" optB88_NMR_Electric_Field_Gradient = "optB88 NMR Electric Field Gradient" optB88_NMR_Nuclear_Shielding = "optB88 NMR Nuclear Shielding" optB88_NSCF_Line = "optB88 NSCF Line" optB88_NSCF_Uniform = "optB88 NSCF Uniform" optB88_Optic = "optB88 Optic" optB88_SCF_Line = "optB88 SCF Line" optB88_Static = "optB88 Static" optB88_Structure_Optimization = "optB88 Structure Optimization" optB88_U_DFPT = "optB88+U DFPT" optB88_U_DFPT_Dielectric = "optB88+U DFPT Dielectric" optB88_U_Deformation = "optB88+U Deformation" optB88_U_Dielectric = "optB88+U Dielectric" optB88_U_Molecular_Dynamics = "optB88+U Molecular Dynamics" optB88_U_NMR_Electric_Field_Gradient = "optB88+U NMR Electric Field Gradient" optB88_U_NMR_Nuclear_Shielding = "optB88+U NMR Nuclear Shielding" optB88_U_NSCF_Line = "optB88+U NSCF Line" optB88_U_NSCF_Uniform = "optB88+U NSCF Uniform" optB88_U_Optic = "optB88+U Optic" optB88_U_SCF_Line = "optB88+U SCF Line" optB88_U_Static = "optB88+U Static" optB88_U_Structure_Optimization = "optB88+U Structure Optimization" optB88_U_Unrecognized = "optB88+U Unrecognized" optB88_Unrecognized = "optB88 Unrecognized" optB88_vdW_DFPT = "optB88-vdW DFPT" optB88_vdW_DFPT_Dielectric = "optB88-vdW DFPT Dielectric" optB88_vdW_Deformation = "optB88-vdW Deformation" optB88_vdW_Dielectric = "optB88-vdW Dielectric" optB88_vdW_Molecular_Dynamics = "optB88-vdW Molecular Dynamics" optB88_vdW_NMR_Electric_Field_Gradient = "optB88-vdW NMR Electric Field Gradient" optB88_vdW_NMR_Nuclear_Shielding = "optB88-vdW NMR Nuclear Shielding" optB88_vdW_NSCF_Line = "optB88-vdW NSCF Line" optB88_vdW_NSCF_Uniform = "optB88-vdW NSCF Uniform" optB88_vdW_Optic = "optB88-vdW Optic" optB88_vdW_SCF_Line = "optB88-vdW SCF Line" optB88_vdW_Static = "optB88-vdW Static" optB88_vdW_Structure_Optimization = "optB88-vdW Structure Optimization" optB88_vdW_U_DFPT = "optB88-vdW+U DFPT" optB88_vdW_U_DFPT_Dielectric = "optB88-vdW+U DFPT Dielectric" optB88_vdW_U_Deformation = "optB88-vdW+U Deformation" optB88_vdW_U_Dielectric = "optB88-vdW+U Dielectric" optB88_vdW_U_Molecular_Dynamics = "optB88-vdW+U Molecular Dynamics" optB88_vdW_U_NMR_Electric_Field_Gradient = ( "optB88-vdW+U NMR Electric Field Gradient" ) optB88_vdW_U_NMR_Nuclear_Shielding = "optB88-vdW+U NMR Nuclear Shielding" optB88_vdW_U_NSCF_Line = "optB88-vdW+U NSCF Line" optB88_vdW_U_NSCF_Uniform = "optB88-vdW+U NSCF Uniform" optB88_vdW_U_Optic = "optB88-vdW+U Optic" optB88_vdW_U_SCF_Line = "optB88-vdW+U SCF Line" optB88_vdW_U_Static = "optB88-vdW+U Static" optB88_vdW_U_Structure_Optimization = "optB88-vdW+U Structure Optimization" optB88_vdW_U_Unrecognized = "optB88-vdW+U Unrecognized" optB88_vdW_Unrecognized = "optB88-vdW Unrecognized" optPBE_DFPT = "optPBE DFPT" optPBE_DFPT_Dielectric = "optPBE DFPT Dielectric" optPBE_Deformation = "optPBE Deformation" optPBE_Dielectric = "optPBE Dielectric" optPBE_Molecular_Dynamics = "optPBE Molecular Dynamics" optPBE_NMR_Electric_Field_Gradient = "optPBE NMR Electric Field Gradient" optPBE_NMR_Nuclear_Shielding = "optPBE NMR Nuclear Shielding" optPBE_NSCF_Line = "optPBE NSCF Line" optPBE_NSCF_Uniform = "optPBE NSCF Uniform" optPBE_Optic = "optPBE Optic" optPBE_SCF_Line = "optPBE SCF Line" optPBE_Static = "optPBE Static" optPBE_Structure_Optimization = "optPBE Structure Optimization" optPBE_U_DFPT = "optPBE+U DFPT" optPBE_U_DFPT_Dielectric = "optPBE+U DFPT Dielectric" optPBE_U_Deformation = "optPBE+U Deformation" optPBE_U_Dielectric = "optPBE+U Dielectric" optPBE_U_Molecular_Dynamics = "optPBE+U Molecular Dynamics" optPBE_U_NMR_Electric_Field_Gradient = "optPBE+U NMR Electric Field Gradient" optPBE_U_NMR_Nuclear_Shielding = "optPBE+U NMR Nuclear Shielding" optPBE_U_NSCF_Line = "optPBE+U NSCF Line" optPBE_U_NSCF_Uniform = "optPBE+U NSCF Uniform" optPBE_U_Optic = "optPBE+U Optic" optPBE_U_SCF_Line = "optPBE+U SCF Line" optPBE_U_Static = "optPBE+U Static" optPBE_U_Structure_Optimization = "optPBE+U Structure Optimization" optPBE_U_Unrecognized = "optPBE+U Unrecognized" optPBE_Unrecognized = "optPBE Unrecognized" optPBE_vdW_DFPT = "optPBE-vdW DFPT" optPBE_vdW_DFPT_Dielectric = "optPBE-vdW DFPT Dielectric" optPBE_vdW_Deformation = "optPBE-vdW Deformation" optPBE_vdW_Dielectric = "optPBE-vdW Dielectric" optPBE_vdW_Molecular_Dynamics = "optPBE-vdW Molecular Dynamics" optPBE_vdW_NMR_Electric_Field_Gradient = "optPBE-vdW NMR Electric Field Gradient" optPBE_vdW_NMR_Nuclear_Shielding = "optPBE-vdW NMR Nuclear Shielding" optPBE_vdW_NSCF_Line = "optPBE-vdW NSCF Line" optPBE_vdW_NSCF_Uniform = "optPBE-vdW NSCF Uniform" optPBE_vdW_Optic = "optPBE-vdW Optic" optPBE_vdW_SCF_Line = "optPBE-vdW SCF Line" optPBE_vdW_Static = "optPBE-vdW Static" optPBE_vdW_Structure_Optimization = "optPBE-vdW Structure Optimization" optPBE_vdW_U_DFPT = "optPBE-vdW+U DFPT" optPBE_vdW_U_DFPT_Dielectric = "optPBE-vdW+U DFPT Dielectric" optPBE_vdW_U_Deformation = "optPBE-vdW+U Deformation" optPBE_vdW_U_Dielectric = "optPBE-vdW+U Dielectric" optPBE_vdW_U_Molecular_Dynamics = "optPBE-vdW+U Molecular Dynamics" optPBE_vdW_U_NMR_Electric_Field_Gradient = ( "optPBE-vdW+U NMR Electric Field Gradient" ) optPBE_vdW_U_NMR_Nuclear_Shielding = "optPBE-vdW+U NMR Nuclear Shielding" optPBE_vdW_U_NSCF_Line = "optPBE-vdW+U NSCF Line" optPBE_vdW_U_NSCF_Uniform = "optPBE-vdW+U NSCF Uniform" optPBE_vdW_U_Optic = "optPBE-vdW+U Optic" optPBE_vdW_U_SCF_Line = "optPBE-vdW+U SCF Line" optPBE_vdW_U_Static = "optPBE-vdW+U Static" optPBE_vdW_U_Structure_Optimization = "optPBE-vdW+U Structure Optimization" optPBE_vdW_U_Unrecognized = "optPBE-vdW+U Unrecognized" optPBE_vdW_Unrecognized = "optPBE-vdW Unrecognized" r2SCAN_DFPT = "r2SCAN DFPT" r2SCAN_DFPT_Dielectric = "r2SCAN DFPT Dielectric" r2SCAN_Deformation = "r2SCAN Deformation" r2SCAN_Dielectric = "r2SCAN Dielectric" r2SCAN_Molecular_Dynamics = "r2SCAN Molecular Dynamics" r2SCAN_NMR_Electric_Field_Gradient = "r2SCAN NMR Electric Field Gradient" r2SCAN_NMR_Nuclear_Shielding = "r2SCAN NMR Nuclear Shielding" r2SCAN_NSCF_Line = "r2SCAN NSCF Line" r2SCAN_NSCF_Uniform = "r2SCAN NSCF Uniform" r2SCAN_Optic = "r2SCAN Optic" r2SCAN_SCF_Line = "r2SCAN SCF Line" r2SCAN_Static = "r2SCAN Static" r2SCAN_Structure_Optimization = "r2SCAN Structure Optimization" r2SCAN_U_DFPT = "r2SCAN+U DFPT" r2SCAN_U_DFPT_Dielectric = "r2SCAN+U DFPT Dielectric" r2SCAN_U_Deformation = "r2SCAN+U Deformation" r2SCAN_U_Dielectric = "r2SCAN+U Dielectric" r2SCAN_U_Molecular_Dynamics = "r2SCAN+U Molecular Dynamics" r2SCAN_U_NMR_Electric_Field_Gradient = "r2SCAN+U NMR Electric Field Gradient" r2SCAN_U_NMR_Nuclear_Shielding = "r2SCAN+U NMR Nuclear Shielding" r2SCAN_U_NSCF_Line = "r2SCAN+U NSCF Line" r2SCAN_U_NSCF_Uniform = "r2SCAN+U NSCF Uniform" r2SCAN_U_Optic = "r2SCAN+U Optic" r2SCAN_U_SCF_Line = "r2SCAN+U SCF Line" r2SCAN_U_Static = "r2SCAN+U Static" r2SCAN_U_Structure_Optimization = "r2SCAN+U Structure Optimization" r2SCAN_U_Unrecognized = "r2SCAN+U Unrecognized" r2SCAN_Unrecognized = "r2SCAN Unrecognized" r2SCAN_rVV10_DFPT = "r2SCAN-rVV10 DFPT" r2SCAN_rVV10_DFPT_Dielectric = "r2SCAN-rVV10 DFPT Dielectric" r2SCAN_rVV10_Deformation = "r2SCAN-rVV10 Deformation" r2SCAN_rVV10_Dielectric = "r2SCAN-rVV10 Dielectric" r2SCAN_rVV10_Molecular_Dynamics = "r2SCAN-rVV10 Molecular Dynamics" r2SCAN_rVV10_NMR_Electric_Field_Gradient = ( "r2SCAN-rVV10 NMR Electric Field Gradient" ) r2SCAN_rVV10_NMR_Nuclear_Shielding = "r2SCAN-rVV10 NMR Nuclear Shielding" r2SCAN_rVV10_NSCF_Line = "r2SCAN-rVV10 NSCF Line" r2SCAN_rVV10_NSCF_Uniform = "r2SCAN-rVV10 NSCF Uniform" r2SCAN_rVV10_Optic = "r2SCAN-rVV10 Optic" r2SCAN_rVV10_SCF_Line = "r2SCAN-rVV10 SCF Line" r2SCAN_rVV10_Static = "r2SCAN-rVV10 Static" r2SCAN_rVV10_Structure_Optimization = "r2SCAN-rVV10 Structure Optimization" r2SCAN_rVV10_U_DFPT = "r2SCAN-rVV10+U DFPT" r2SCAN_rVV10_U_DFPT_Dielectric = "r2SCAN-rVV10+U DFPT Dielectric" r2SCAN_rVV10_U_Deformation = "r2SCAN-rVV10+U Deformation" r2SCAN_rVV10_U_Dielectric = "r2SCAN-rVV10+U Dielectric" r2SCAN_rVV10_U_Molecular_Dynamics = "r2SCAN-rVV10+U Molecular Dynamics" r2SCAN_rVV10_U_NMR_Electric_Field_Gradient = ( "r2SCAN-rVV10+U NMR Electric Field Gradient" ) r2SCAN_rVV10_U_NMR_Nuclear_Shielding = "r2SCAN-rVV10+U NMR Nuclear Shielding" r2SCAN_rVV10_U_NSCF_Line = "r2SCAN-rVV10+U NSCF Line" r2SCAN_rVV10_U_NSCF_Uniform = "r2SCAN-rVV10+U NSCF Uniform" r2SCAN_rVV10_U_Optic = "r2SCAN-rVV10+U Optic" r2SCAN_rVV10_U_SCF_Line = "r2SCAN-rVV10+U SCF Line" r2SCAN_rVV10_U_Static = "r2SCAN-rVV10+U Static" r2SCAN_rVV10_U_Structure_Optimization = "r2SCAN-rVV10+U Structure Optimization" r2SCAN_rVV10_U_Unrecognized = "r2SCAN-rVV10+U Unrecognized" r2SCAN_rVV10_Unrecognized = "r2SCAN-rVV10 Unrecognized" revPBE_DFPT = "revPBE DFPT" revPBE_DFPT_Dielectric = "revPBE DFPT Dielectric" revPBE_Deformation = "revPBE Deformation" revPBE_Dielectric = "revPBE Dielectric" revPBE_Molecular_Dynamics = "revPBE Molecular Dynamics" revPBE_NMR_Electric_Field_Gradient = "revPBE NMR Electric Field Gradient" revPBE_NMR_Nuclear_Shielding = "revPBE NMR Nuclear Shielding" revPBE_NSCF_Line = "revPBE NSCF Line" revPBE_NSCF_Uniform = "revPBE NSCF Uniform" revPBE_Optic = "revPBE Optic" revPBE_PADE_DFPT = "revPBE+PADE DFPT" revPBE_PADE_DFPT_Dielectric = "revPBE+PADE DFPT Dielectric" revPBE_PADE_Deformation = "revPBE+PADE Deformation" revPBE_PADE_Dielectric = "revPBE+PADE Dielectric" revPBE_PADE_Molecular_Dynamics = "revPBE+PADE Molecular Dynamics" revPBE_PADE_NMR_Electric_Field_Gradient = "revPBE+PADE NMR Electric Field Gradient" revPBE_PADE_NMR_Nuclear_Shielding = "revPBE+PADE NMR Nuclear Shielding" revPBE_PADE_NSCF_Line = "revPBE+PADE NSCF Line" revPBE_PADE_NSCF_Uniform = "revPBE+PADE NSCF Uniform" revPBE_PADE_Optic = "revPBE+PADE Optic" revPBE_PADE_SCF_Line = "revPBE+PADE SCF Line" revPBE_PADE_Static = "revPBE+PADE Static" revPBE_PADE_Structure_Optimization = "revPBE+PADE Structure Optimization" revPBE_PADE_U_DFPT = "revPBE+PADE+U DFPT" revPBE_PADE_U_DFPT_Dielectric = "revPBE+PADE+U DFPT Dielectric" revPBE_PADE_U_Deformation = "revPBE+PADE+U Deformation" revPBE_PADE_U_Dielectric = "revPBE+PADE+U Dielectric" revPBE_PADE_U_Molecular_Dynamics = "revPBE+PADE+U Molecular Dynamics" revPBE_PADE_U_NMR_Electric_Field_Gradient = ( "revPBE+PADE+U NMR Electric Field Gradient" ) revPBE_PADE_U_NMR_Nuclear_Shielding = "revPBE+PADE+U NMR Nuclear Shielding" revPBE_PADE_U_NSCF_Line = "revPBE+PADE+U NSCF Line" revPBE_PADE_U_NSCF_Uniform = "revPBE+PADE+U NSCF Uniform" revPBE_PADE_U_Optic = "revPBE+PADE+U Optic" revPBE_PADE_U_SCF_Line = "revPBE+PADE+U SCF Line" revPBE_PADE_U_Static = "revPBE+PADE+U Static" revPBE_PADE_U_Structure_Optimization = "revPBE+PADE+U Structure Optimization" revPBE_PADE_U_Unrecognized = "revPBE+PADE+U Unrecognized" revPBE_PADE_Unrecognized = "revPBE+PADE Unrecognized" revPBE_SCF_Line = "revPBE SCF Line" revPBE_Static = "revPBE Static" revPBE_Structure_Optimization = "revPBE Structure Optimization" revPBE_U_DFPT = "revPBE+U DFPT" revPBE_U_DFPT_Dielectric = "revPBE+U DFPT Dielectric" revPBE_U_Deformation = "revPBE+U Deformation" revPBE_U_Dielectric = "revPBE+U Dielectric" revPBE_U_Molecular_Dynamics = "revPBE+U Molecular Dynamics" revPBE_U_NMR_Electric_Field_Gradient = "revPBE+U NMR Electric Field Gradient" revPBE_U_NMR_Nuclear_Shielding = "revPBE+U NMR Nuclear Shielding" revPBE_U_NSCF_Line = "revPBE+U NSCF Line" revPBE_U_NSCF_Uniform = "revPBE+U NSCF Uniform" revPBE_U_Optic = "revPBE+U Optic" revPBE_U_SCF_Line = "revPBE+U SCF Line" revPBE_U_Static = "revPBE+U Static" revPBE_U_Structure_Optimization = "revPBE+U Structure Optimization" revPBE_U_Unrecognized = "revPBE+U Unrecognized" revPBE_Unrecognized = "revPBE Unrecognized" revPBE_vdW_DFPT = "revPBE-vdW DFPT" revPBE_vdW_DFPT_Dielectric = "revPBE-vdW DFPT Dielectric" revPBE_vdW_Deformation = "revPBE-vdW Deformation" revPBE_vdW_Dielectric = "revPBE-vdW Dielectric" revPBE_vdW_Molecular_Dynamics = "revPBE-vdW Molecular Dynamics" revPBE_vdW_NMR_Electric_Field_Gradient = "revPBE-vdW NMR Electric Field Gradient" revPBE_vdW_NMR_Nuclear_Shielding = "revPBE-vdW NMR Nuclear Shielding" revPBE_vdW_NSCF_Line = "revPBE-vdW NSCF Line" revPBE_vdW_NSCF_Uniform = "revPBE-vdW NSCF Uniform" revPBE_vdW_Optic = "revPBE-vdW Optic" revPBE_vdW_SCF_Line = "revPBE-vdW SCF Line" revPBE_vdW_Static = "revPBE-vdW Static" revPBE_vdW_Structure_Optimization = "revPBE-vdW Structure Optimization" revPBE_vdW_U_DFPT = "revPBE-vdW+U DFPT" revPBE_vdW_U_DFPT_Dielectric = "revPBE-vdW+U DFPT Dielectric" revPBE_vdW_U_Deformation = "revPBE-vdW+U Deformation" revPBE_vdW_U_Dielectric = "revPBE-vdW+U Dielectric" revPBE_vdW_U_Molecular_Dynamics = "revPBE-vdW+U Molecular Dynamics" revPBE_vdW_U_NMR_Electric_Field_Gradient = ( "revPBE-vdW+U NMR Electric Field Gradient" ) revPBE_vdW_U_NMR_Nuclear_Shielding = "revPBE-vdW+U NMR Nuclear Shielding" revPBE_vdW_U_NSCF_Line = "revPBE-vdW+U NSCF Line" revPBE_vdW_U_NSCF_Uniform = "revPBE-vdW+U NSCF Uniform" revPBE_vdW_U_Optic = "revPBE-vdW+U Optic" revPBE_vdW_U_SCF_Line = "revPBE-vdW+U SCF Line" revPBE_vdW_U_Static = "revPBE-vdW+U Static" revPBE_vdW_U_Structure_Optimization = "revPBE-vdW+U Structure Optimization" revPBE_vdW_U_Unrecognized = "revPBE-vdW+U Unrecognized" revPBE_vdW_Unrecognized = "revPBE-vdW Unrecognized" rev_vdW_DF2_DFPT = "rev-vdW-DF2 DFPT" rev_vdW_DF2_DFPT_Dielectric = "rev-vdW-DF2 DFPT Dielectric" rev_vdW_DF2_Deformation = "rev-vdW-DF2 Deformation" rev_vdW_DF2_Dielectric = "rev-vdW-DF2 Dielectric" rev_vdW_DF2_Molecular_Dynamics = "rev-vdW-DF2 Molecular Dynamics" rev_vdW_DF2_NMR_Electric_Field_Gradient = "rev-vdW-DF2 NMR Electric Field Gradient" rev_vdW_DF2_NMR_Nuclear_Shielding = "rev-vdW-DF2 NMR Nuclear Shielding" rev_vdW_DF2_NSCF_Line = "rev-vdW-DF2 NSCF Line" rev_vdW_DF2_NSCF_Uniform = "rev-vdW-DF2 NSCF Uniform" rev_vdW_DF2_Optic = "rev-vdW-DF2 Optic" rev_vdW_DF2_SCF_Line = "rev-vdW-DF2 SCF Line" rev_vdW_DF2_Static = "rev-vdW-DF2 Static" rev_vdW_DF2_Structure_Optimization = "rev-vdW-DF2 Structure Optimization" rev_vdW_DF2_U_DFPT = "rev-vdW-DF2+U DFPT" rev_vdW_DF2_U_DFPT_Dielectric = "rev-vdW-DF2+U DFPT Dielectric" rev_vdW_DF2_U_Deformation = "rev-vdW-DF2+U Deformation" rev_vdW_DF2_U_Dielectric = "rev-vdW-DF2+U Dielectric" rev_vdW_DF2_U_Molecular_Dynamics = "rev-vdW-DF2+U Molecular Dynamics" rev_vdW_DF2_U_NMR_Electric_Field_Gradient = ( "rev-vdW-DF2+U NMR Electric Field Gradient" ) rev_vdW_DF2_U_NMR_Nuclear_Shielding = "rev-vdW-DF2+U NMR Nuclear Shielding" rev_vdW_DF2_U_NSCF_Line = "rev-vdW-DF2+U NSCF Line" rev_vdW_DF2_U_NSCF_Uniform = "rev-vdW-DF2+U NSCF Uniform" rev_vdW_DF2_U_Optic = "rev-vdW-DF2+U Optic" rev_vdW_DF2_U_SCF_Line = "rev-vdW-DF2+U SCF Line" rev_vdW_DF2_U_Static = "rev-vdW-DF2+U Static" rev_vdW_DF2_U_Structure_Optimization = "rev-vdW-DF2+U Structure Optimization" rev_vdW_DF2_U_Unrecognized = "rev-vdW-DF2+U Unrecognized" rev_vdW_DF2_Unrecognized = "rev-vdW-DF2 Unrecognized" vdW_DF2_DFPT = "vdW-DF2 DFPT" vdW_DF2_DFPT_Dielectric = "vdW-DF2 DFPT Dielectric" vdW_DF2_Deformation = "vdW-DF2 Deformation" vdW_DF2_Dielectric = "vdW-DF2 Dielectric" vdW_DF2_Molecular_Dynamics = "vdW-DF2 Molecular Dynamics" vdW_DF2_NMR_Electric_Field_Gradient = "vdW-DF2 NMR Electric Field Gradient" vdW_DF2_NMR_Nuclear_Shielding = "vdW-DF2 NMR Nuclear Shielding" vdW_DF2_NSCF_Line = "vdW-DF2 NSCF Line" vdW_DF2_NSCF_Uniform = "vdW-DF2 NSCF Uniform" vdW_DF2_Optic = "vdW-DF2 Optic" vdW_DF2_SCF_Line = "vdW-DF2 SCF Line" vdW_DF2_Static = "vdW-DF2 Static" vdW_DF2_Structure_Optimization = "vdW-DF2 Structure Optimization" vdW_DF2_U_DFPT = "vdW-DF2+U DFPT" vdW_DF2_U_DFPT_Dielectric = "vdW-DF2+U DFPT Dielectric" vdW_DF2_U_Deformation = "vdW-DF2+U Deformation" vdW_DF2_U_Dielectric = "vdW-DF2+U Dielectric" vdW_DF2_U_Molecular_Dynamics = "vdW-DF2+U Molecular Dynamics" vdW_DF2_U_NMR_Electric_Field_Gradient = "vdW-DF2+U NMR Electric Field Gradient" vdW_DF2_U_NMR_Nuclear_Shielding = "vdW-DF2+U NMR Nuclear Shielding" vdW_DF2_U_NSCF_Line = "vdW-DF2+U NSCF Line" vdW_DF2_U_NSCF_Uniform = "vdW-DF2+U NSCF Uniform" vdW_DF2_U_Optic = "vdW-DF2+U Optic" vdW_DF2_U_SCF_Line = "vdW-DF2+U SCF Line" vdW_DF2_U_Static = "vdW-DF2+U Static" vdW_DF2_U_Structure_Optimization = "vdW-DF2+U Structure Optimization" vdW_DF2_U_Unrecognized = "vdW-DF2+U Unrecognized" vdW_DF2_Unrecognized = "vdW-DF2 Unrecognized" ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8290114 emmet_core-0.87.2/emmet/core/vasp/calc_types/utils.py0000644000175100017510000001223515240717720022255 0ustar00runnerrunner"""Module to define various calculation types as Enums for VASP.""" from __future__ import annotations from pathlib import Path from typing import TYPE_CHECKING from monty.serialization import loadfn import numpy as np from emmet.core.io.pymatgen import Kpoints from emmet.core.vasp.calc_types.enums import CalcType, RunType, TaskType if TYPE_CHECKING: from typing import Any _RUN_TYPE_DATA = loadfn( str(Path(__file__).parent.joinpath("calc_types.yaml").resolve()) )["RUN_TYPES"] __all__ = ["run_type", "task_type", "calc_type"] def run_type(parameters: dict[str, Any]) -> RunType: """ Determine run type from the VASP parameters dict. This is adapted from pymatgen to be far less unstable Args: parameters: Dictionary of VASP parameters from vasprun.xml. Returns: The run type. Warning: VASP mangles the LDAU* fields in the parameters field. If you're not using the TaskDocument.run_type, copy over LDAU* fields from the incar rather than trust parameters. """ is_hubbard = "+U" if parameters.get("LDAU", False) else "" def _variant_equal(v1, v2) -> bool: """Check two strings equal.""" if isinstance(v1, str) and isinstance(v2, str): return v1.strip().upper() == v2.strip().upper() return v1 == v2 # This is to force an order of evaluation for functional_class in ["GW", "HF", "VDW", "METAGGA", "GGA"]: for special_type, params in _RUN_TYPE_DATA[functional_class].items(): if all( _variant_equal(parameters.get(param, None), value) for param, value in params.items() ): return RunType(f"{special_type}{is_hubbard}") return RunType(f"LDA{is_hubbard}") def task_type(inputs: dict[str, Any]) -> TaskType: """ Determine task type from vasp inputs. Args: inputs: inputs dict with an incar, kpoints, potcar, and poscar dictionaries. Returns: The task type. """ calc_type = [] incar = inputs.get("incar", {}) kpts = inputs.get("kpoints") or {} # kpoints can be None, then want a dict if isinstance(kpts, Kpoints): kpts = kpts.as_dict() if incar.get("ICHARG", 0) > 10: try: kpt_labels = kpts.get("labels") or [] # Don't change below: `None.__ne__` is `NotImplemented` num_kpt_labels = len([lbl for lbl in kpt_labels if lbl is not None]) except Exception as e: raise Exception("Couldn't identify total number of kpt labels") from e if num_kpt_labels > 0 or kpts.get("nkpoints", 0) > 0: # calcs_reversed kpoints are read from vasprun.xml, which # removes k-points labels --> check that nkpoints > 0 # orig_inputs are read from KPOINTS and retain # user-input labels --> check num_kpt_labels > 0 calc_type.append("NSCF Line") else: calc_type.append("NSCF Uniform") elif len([x for x in kpts.get("labels") or [] if x is not None]) > 0: calc_type.append("NSCF Line") elif incar.get("LEPSILON", False): if incar.get("IBRION", 0) > 6: calc_type.append("DFPT") calc_type.append("Dielectric") elif incar.get("IBRION", 0) > 6: calc_type.append("DFPT") elif incar.get("LCHIMAG", False): calc_type.append("NMR Nuclear Shielding") elif incar.get("LEFG", False): calc_type.append("NMR Electric Field Gradient") elif incar.get("NSW", 1) == 0: if incar.get("LOPTICS", False) is True and incar.get("ALGO", None) == "Exact": calc_type.append("Optic") elif incar.get("ALGO", "").upper() == "CHI": calc_type.append("Optic") elif np.any(np.abs(kpts.get("kpts_weights") or [1e20]) < 1e-6) and ( incar.get("METAGGA") is not None or incar.get("LHFCALC", False) ): # Static meta-GGA or hybrid calculation with zero-weighted k-points # Highly-likely to be a self-consistent line-mode calculation calc_type.append("SCF Line") else: calc_type.append("Static") elif incar.get("LOPTICS", False) is True or incar.get("ALGO", "").upper() == "CHI": calc_type.append("Optic") elif incar.get("ISIF", 2) == 3 and incar.get("IBRION", 0) > 0: calc_type.append("Structure Optimization") elif incar.get("ISIF", 3) == 2 and incar.get("IBRION", 0) > 0: calc_type.append("Deformation") elif incar.get("IBRION", 1) == 0: calc_type.append("Molecular Dynamics") elif incar.get("ALGO", "Normal").lower() == "gw0": calc_type.append("NSCF Uniform") if len(calc_type) == 0: return TaskType("Unrecognized") return TaskType(" ".join(calc_type)) def calc_type( inputs: dict[str, Any], parameters: dict[str, Any], ) -> CalcType: """ Determine the calc type. Args: inputs: inputs dict with an incar, kpoints, potcar, and poscar dictionaries. parameters: Dictionary of VASP parameters from vasprun.xml. Returns: The calc type. """ rt = run_type(parameters).value tt = task_type(inputs).value return CalcType(f"{rt} {tt}") ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8290114 emmet_core-0.87.2/emmet/core/vasp/calculation.py0000644000175100017510000016472415240717720021300 0ustar00runnerrunner"""Core definitions of a VASP calculation documents.""" from __future__ import annotations import logging import os from copy import deepcopy from datetime import datetime from functools import cached_property from hashlib import md5 from pathlib import Path from typing import TYPE_CHECKING, Annotated, Any, Type import numpy as np import orjson from monty.io import zopen from pydantic import BaseModel, BeforeValidator, ConfigDict, Field, model_validator from typing_extensions import NotRequired, TypedDict from emmet.core.band_theory import ElectronicBS, ElectronicDos from emmet.core.io.pymatgen import ( BSVasprun, ChargemolAnalysis, CompleteDos, Kpoints, Locpot, OrbitalType, Oszicar, Outcar, Poscar, ) from emmet.core.io.pymatgen import Potcar as VaspPotcar from emmet.core.io.pymatgen import ( PotcarSingle, Structure, Vasprun, VolumetricData, bader_analysis_from_path, ) from emmet.core.math import ListMatrix3D, Matrix3D, Vector3D, Vector6D from emmet.core.settings import EmmetSettings from emmet.core.trajectory import RelaxTrajectory, Trajectory from emmet.core.types.enums import StoreTrajectoryOption, TaskState, VaspObject from emmet.core.types.pymatgen_types.bader import BaderAnalysis from emmet.core.types.pymatgen_types.kpoints_adapter import KpointsType from emmet.core.types.pymatgen_types.lattice_adapter import LatticeType from emmet.core.types.pymatgen_types.outcar_adapter import OutcarType from emmet.core.types.pymatgen_types.structure_adapter import StructureType from emmet.core.types.typing import JsonDictType, _dict_items_zipper from emmet.core.utils import type_override from emmet.core.vasp.calc_types import ( CalcType, RunType, TaskType, calc_type, run_type, task_type, ) from emmet.core.vasp.models import ChgcarLike, ElectronicStep SETTINGS = EmmetSettings() if TYPE_CHECKING: from typing_extensions import Self from emmet.core.io.pymatgen import BandStructure, CompleteDos logger = logging.getLogger(__name__) class Potcar(BaseModel): pot_type: str | None = Field(None, description="Pseudo-potential type, e.g. PAW") functional: str | None = Field( None, description="Functional type use in the calculation." ) symbols: list[str] | None = Field( None, description="List of VASP potcar symbols used in the calculation." ) class CalculationBaseModel(BaseModel): """Wrapper around pydantic BaseModel with extra functionality.""" def get(self, key: Any, default_value: Any | None = None) -> Any: return getattr(self, key, default_value) class TypedStatisticsDict(TypedDict): MEAN: float ABSMEAN: float VAR: float MIN: float MAX: float class TypedPotcarKeywordsDict(TypedDict): header: list[str] data: list[str] class TypedPotcarStatsDict(TypedDict): header: TypedStatisticsDict data: TypedStatisticsDict class TypedPotcarSummaryStatsDict(TypedDict): keywords: NotRequired[TypedPotcarKeywordsDict | None] stats: NotRequired[TypedPotcarStatsDict | None] class PotcarSpec(BaseModel): """Document defining a VASP POTCAR specification.""" titel: str | None = Field(None, description="TITEL field from POTCAR header") # NOTE: The `hash` is no longer used for validating POTCARs. # It is included here for backwards compatibility # However, it is no longer included in `pymatgen-core`. # Thus we must regenerate this ourselves. # Recommend removing eventually. hash: str | None = Field(None, description="md5 hash of POTCAR file") summary_stats: TypedPotcarSummaryStatsDict | None = Field( None, description="summary statistics used to ID POTCARs without hashing", ) def get(self, key, default=None): return getattr(self, key, default) @classmethod def from_potcar_single(cls, potcar_single: PotcarSingle) -> Self: """ Get a PotcarSpec from a PotcarSingle. Parameters ---------- potcar_single A potcar single object. Returns ------- PotcarSpec A potcar spec. """ if (potcar_hash := getattr(potcar_single, "md5_header_hash", None)) is None: potcar_hash = md5(potcar_single.data.encode()).hexdigest() return cls( titel=potcar_single.TITEL, hash=potcar_hash, summary_stats=potcar_single._summary_stats, # type: ignore[arg-type] ) @classmethod def from_potcar(cls, potcar: VaspPotcar) -> list["PotcarSpec"]: """ Get a list of PotcarSpecs from a Potcar. Parameters ---------- potcar A potcar object. Returns ------- list[PotcarSpec] A list of potcar specs. """ return [cls.from_potcar_single(p) for p in potcar] @classmethod def from_file(cls, file_path: str | Path) -> list["PotcarSpec"]: """ Get a list of PotcarSpecs from a Potcar. Parameters ---------- file_path : str or Path of the POTCAR Returns ------- list[PotcarSpec] A list of potcar specs. """ if ".spec" in str(file_path): with zopen(file_path, "rt") as psf: try: # POTCAR spec is a model-dumped list of PotcarSpec return [cls(**ps) for ps in orjson.loads(psf.read())] except orjson.JSONDecodeError: # POTCAR spec is a pymatgen-style newline-delimited list of symbols return [cls(titel=symb) for symb in psf.read().splitlines()] # type: ignore[arg-type,call-arg] return cls.from_potcar(VaspPotcar.from_file(str(file_path))) @type_override({"incar": str, "parameters": str}) class CalculationInput(CalculationBaseModel): """Document defining VASP calculation inputs. Note that the following fields were formerly top-level fields on InputDoc, but are now properties of `CalculationInput`: pseudo_potentials (Potcar) : summary of the POTCARs used in the calculation xc_override (str) : the exchange-correlation functional used if not the one specified by POTCAR is_lasph (bool) : how the calculation set LASPH (aspherical corrections) magnetic_moments (list of floats) : on-site magnetic moments """ incar: JsonDictType = Field( None, description="INCAR parameters for the calculation" ) kpoints: KpointsType | None = Field(None, description="KPOINTS for the calculation") nkpoints: int | None = Field(None, description="Total number of k-points") potcar_spec: list[PotcarSpec] | None = Field( None, description="Title and hash of POTCAR files used in the calculation" ) potcar: list[str] | None = Field( None, description="The symbols of the POTCARs used in the calculation." ) potcar_type: list[str] | None = Field( None, description="List of POTCAR functional types." ) parameters: JsonDictType = Field(None, description="Parameters from vasprun") lattice_rec: LatticeType | None = Field( None, description="Reciprocal lattice of the structure" ) structure: StructureType | None = Field( None, description="Input structure for the calculation" ) is_hubbard: bool = Field( default=False, description="Is this a Hubbard +U calculation" ) hubbards: Annotated[ dict[str, float] | None, BeforeValidator(_dict_items_zipper) ] = Field(None, description="The hubbard parameters used") @model_validator(mode="before") @classmethod def clean_inputs(cls, config: Any) -> Any: """Ensure whitespace in parameters and Kpoints are serialized. NOTE: ------ A change in VASP introduced whitespace into some parameters, for example `PE` was observed in VASP 6.4.3. This will lead to an incorrect return value from RunType. This validator will ensure that any already-parsed documents are fixed. """ if (kpts := config.get("kpoints")) and isinstance(kpts, dict): config["kpoints"] = Kpoints.from_dict(kpts) # Issue: older tasks don't always contain `input.potcar` # and `input.potcar_spec` # Newer tasks contain `input.potcar`, `input.potcar_spec`, # and `orig_inputs.potcar_spec` # orig_inputs.potcar used to be what is now potcar_spec # try to coerce back here, but not guaranteed if (pcar := config.get("potcar")) and not config.get("potcar_spec"): if isinstance(pcar, dict): if pcar.get("@class") == "Potcar": try: # If POTCAR library is available pcar = VaspPotcar.from_dict(pcar) except ValueError: # No POTCAR library available config["potcar"] = pcar.get("symbols") config["potcar_type"] = [pcar.get("functional")] else: pcar = Potcar(**pcar) if isinstance(pcar, VaspPotcar): config["potcar_spec"] = PotcarSpec.from_potcar(pcar) config.pop("potcar") elif isinstance(pcar, Potcar): config["potcar_type"] = [pcar.pot_type] if pcar.pot_type else None config["potcar"] = pcar.symbols else: try: pspec = [PotcarSpec(**x) for x in pcar] config["potcar_spec"] = pspec config.pop("potcar") except Exception: if not isinstance(pcar, list) or not all( isinstance(x, str) for x in pcar ): config.pop("potcar") if (pspec := config.get("potcar_spec")) and config.get("potcar") is None: config["potcar"] = [] for spec in pspec: if isinstance(spec, PotcarSpec) and spec.titel: titel = spec.titel elif isinstance(spec, dict) and spec.get("titel"): titel = spec["titel"] else: continue symbs = titel.split() if len(symbs) >= 2: symb = symbs[1] else: symb = symbs[0] config["potcar"].append(symb) return config def model_post_init(self, context: Any, /) -> None: # populate legacy hubbards data if ( ( self.is_hubbard or (self.incar or {}).get("LDAU") or (self.parameters or {}).get("LDAU") ) and not self.hubbards and self.potcar ): # Basically the same functionality as in pymatgen Vasprun symbols = [symb.split("_", 1)[0] for symb in self.potcar] input_params = { k: v for inc_k in ( "incar", "parameters", ) for k, v in (getattr(self, inc_k) or {}).items() } u = input_params.get("LDAUU", []) j = input_params.get("LDAUJ", []) if len(j) != len(u): j = [0] * len(u) if len(u) == len(symbols): self.hubbards = { symbols[idx]: u[idx] - j[idx] for idx in range(len(symbols)) } @cached_property def poscar(self) -> Poscar | None: """Return pymatgen object representing the POSCAR file.""" if self.structure: return Poscar(self.structure) return None @property def is_lasph(self) -> bool | None: """Report if the calculation was run with aspherical corrections. If `self.parameters` is populated, returns the value of `LASPH` from vasprun.xml, or its default value if not set (`False`). If `self.parameters` isn't populated (vasprun.xml wasn't parsed), returns `None`. """ if self.parameters: return self.parameters.get("LASPH", False) return None @property def pseudo_potentials(self) -> Potcar | None: """Summarize the pseudo-potentials used.""" if not self.potcar_type: return None if len(potcar_meta := self.potcar_type[0].split("_")) == 2: pot_type, func = potcar_meta elif len(potcar_meta) == 1: pot_type = potcar_meta[0] func = "LDA" return Potcar(pot_type=pot_type, functional=func, symbols=self.potcar) @property def xc_override(self) -> str | None: """Report the exchange-correlation functional used.""" xc = None if self.incar: xc = self.incar.get("GGA") or self.incar.get("METAGGA") return xc.upper() if isinstance(xc, str) else xc @property def magnetic_moments(self) -> list[float] | None: """Report initial magnetic moments assigned to each atom.""" return (self.parameters or self.incar or {}).get("MAGMOM", None) @classmethod def from_vasprun( cls, vasprun: Vasprun, potcar_spec: list[PotcarSpec] | None = None ) -> Self: """ Create a VASP input document from a Vasprun object. Parameters ---------- vasprun A vasprun object. potcar_spec : list of dict If specified, the POTCAR spec to override that of vasprun.xml Returns ------- CalculationInput The input document. """ kpoints_dict = vasprun.kpoints.as_dict() kpoints_dict["actual_kpoints"] = [ {"abc": list(k), "weight": w} for k, w in zip(vasprun.actual_kpoints, vasprun.actual_kpoints_weights) ] parameters = dict(vasprun.parameters).copy() incar = dict(vasprun.incar) if metagga := incar.get("METAGGA"): # Per issue #960, the METAGGA tag is populated in the # INCAR field of vasprun.xml, and not parameters parameters.update({"METAGGA": metagga}) return cls( # type: ignore[call-arg] structure=vasprun.initial_structure, incar=incar, kpoints=Kpoints.from_dict(kpoints_dict), nkpoints=len(kpoints_dict["actual_kpoints"]), potcar_spec=potcar_spec or [PotcarSpec(**ps) for ps in vasprun.potcar_spec], potcar_type=[s.split()[0] for s in vasprun.potcar_symbols], parameters=parameters, lattice_rec=vasprun.initial_structure.lattice.reciprocal_lattice, is_hubbard=vasprun.is_hubbard, hubbards=vasprun.hubbards, ) class RunStatistics(BaseModel): """Summary of the run statistics for a VASP calculation.""" average_memory: float = Field(0, description="The average memory used in kb") max_memory: float = Field(0, description="The maximum memory used in kb") elapsed_time: float = Field(0, description="The real time elapsed in seconds") system_time: float = Field(0, description="The system CPU time in seconds") user_time: float = Field( 0, description="The user CPU time spent by VASP in seconds" ) total_time: float = Field(0, description="The total CPU time for this calculation") cores: int = Field(0, description="The number of cores used by VASP") @classmethod def from_outcar(cls, outcar: Outcar) -> Self: """ Create a run statistics document from an Outcar object. Parameters ---------- outcar An Outcar object. Returns ------- RunStatistics The run statistics. """ # rename these statistics mapping = { "Average memory used (kb)": "average_memory", "Maximum memory used (kb)": "max_memory", "Elapsed time (sec)": "elapsed_time", "System time (sec)": "system_time", "User time (sec)": "user_time", "Total CPU time used (sec)": "total_time", "cores": "cores", } run_stats: dict[str, int | float] = {} for k, v in mapping.items(): stat = outcar.run_stats.get(k) or 0 try: stat = float(stat) except ValueError: # sometimes the statistics are misformatted stat = 0 run_stats[v] = stat return cls(**run_stats) # type: ignore[arg-type] class FrequencyDependentDielectric(BaseModel): """Frequency-dependent dielectric data.""" real: list[list[float]] | None = Field( None, description="Real part of the frequency dependent dielectric constant, given at" " each energy as 6 components according to XX, YY, ZZ, XY, YZ, ZX", ) imaginary: list[list[float]] | None = Field( None, description="Imaginary part of the frequency dependent dielectric constant, " "given at each energy as 6 components according to XX, YY, ZZ, XY, " "YZ, ZX", ) energy: list[float] | None = Field( None, description="Energies at which the real and imaginary parts of the dielectric" "constant are given", ) @model_validator(mode="before") def migrate_legacy_fields(cls, data: Any) -> Any: if "imag" in data: data["imaginary"] = data["imag"] return data @classmethod def from_vasprun(cls, vasprun: Vasprun) -> Self: """ Create a frequency-dependent dielectric calculation document from a vasprun. Parameters ---------- vasprun : Vasprun A vasprun object. Returns ------- FrequencyDependentDielectric A frequency-dependent dielectric document. """ energy, real, imag = vasprun.dielectric return cls(real=real, imaginary=imag, energy=energy) class ElectronPhononDisplacedStructures(BaseModel): """Document defining electron phonon displaced structures.""" temperatures: list[float] | None = Field( None, description="The temperatures at which the electron phonon displacements " "were generated.", ) structures: list[StructureType] | None = Field( None, description="The displaced structures corresponding to each temperature." ) class IonicStep(BaseModel): # type: ignore """Document defining the information at each ionic step.""" e_fr_energy: float | None = Field(None, description="The free energy.") e_wo_entrp: float | None = Field(None, description="The energy without entropy.") e_0_energy: float | None = Field(None, description="The internal energy.") forces: list[Vector3D] | None = Field(None, description="The forces on each atom.") stress: Matrix3D | None = Field(None, description="The stress on the lattice.") electronic_steps: list[ElectronicStep] | None = Field( None, description="The electronic convergence steps." ) num_electronic_steps: int | None = Field( None, description="The number of electronic steps needed to reach convergence." ) structure: StructureType | None = Field( None, description="The structure at this step." ) model_config = ConfigDict(extra="allow") @model_validator(mode="after") def set_elec_step_count(self): if self.electronic_steps is not None: self.num_electronic_steps = len(self.electronic_steps) return self def _deser_dos_properties( dos_properties: list[Any] | dict[str, Any] | None, ) -> dict[str, dict[str, dict[str, float]]] | None: if dos_properties: if isinstance(dos_properties, list): dos_properties = { element: { orbital: {key: value for key, value in property} for orbital, property in properties } for element, properties in dos_properties } elif isinstance(dos_properties, dict) and isinstance( next(iter(dos_properties.values())), list ): dos_properties = { element: { orbital: {key: value for key, value in property} for orbital, property in properties } for element, properties in dos_properties.items() } return dos_properties # type: ignore[return-value] class DielectricProperties(BaseModel): """Store electronic response properties. Note the units and tensor ranks: - Dielectric tensors are dimensionless (no units apply), and are 3x3 - Piezoelectric tensors are in C(oulomb)/m^2, and are 3x6 - Strain tensors, for each atom, are in eV/Å, and are 3x6 - Born charges, for each atom, are in units of the elementary charge, and are 3x3 For both Born charges and strain, the tensors are listed for each site in the structure. Thus one expects a list of 3x3 and 3x6 tensors, respectively. """ born_charges: list[Matrix3D] | None = Field( None, description=( "Born effective charges for each site in the structure " "(units of elementary charge e)." ), ) dielectric_ionic_tensor: Matrix3D | None = Field( None, description="Ionic contribution to the dielectric tensor (dimensionless)." ) dielectric_tensor: Matrix3D | None = Field( None, description="Overall dielectric tensor (dimensionless)." ) internal_strain_tensor: list[tuple[Vector6D, Vector6D, Vector6D]] | None = Field( None, description="The internal strain tensor (eV/Å)." ) piezo_ionic_tensor: tuple[Vector6D, Vector6D, Vector6D] | None = Field( None, description="Ionic contribution to the piezoelectric tensor (C/m^2)." ) piezo_tensor: tuple[Vector6D, Vector6D, Vector6D] | None = Field( None, description="Overall piezoelectric tensor (C/m^2)." ) @classmethod def from_outcar(cls, outcar: Outcar | dict[str, Any]) -> Self: """Parse data from the OUTCAR. Parameters ----------- outcar : Outcar or its as_dict representation """ aliases = {"born_charges": "born"} config = { k: ( getattr(outcar, aliases.get(k, k), None) if isinstance(outcar, Outcar) else outcar.get(aliases.get(k, k), None) ) for k in cls.model_fields } return cls( **{ k: v.tolist() if isinstance(v, np.ndarray) else v for k, v in config.items() } ) class CoreCalculationOutput(BaseModel): """Document defining core VASP calculation outputs.""" bandgap: float | None = Field( None, description="The band gap from the calculation in eV" ) cbm: float | None = Field( None, description="The conduction band minimum in eV (if system is not metallic)", ) density: float | None = Field( None, description="Density of final structure in units of g/cc." ) dielectric_properties: DielectricProperties | None = Field( None, description="Dielectric, piezoelectric, and other electromechanical properties.", ) direct_gap: float | None = Field( None, description="Direct band gap in eV (if system is not metallic)" ) dos_properties: Annotated[ dict[str, dict[str, dict[str, float]]] | None, BeforeValidator(_deser_dos_properties), ] = Field( None, description="Element- and orbital-projected band properties (in eV) for the " "DOS. All properties are with respect to the Fermi level.", ) efermi: float | None = Field( None, description="The Fermi level from the calculation in eV" ) energy: float | None = Field( None, description="The final total DFT energy for the calculation" ) energy_per_atom: float | None = Field( None, description="The final DFT energy per atom for the calculation" ) epsilon_ionic: ListMatrix3D | None = Field( None, description="The ionic part of the dielectric constant" ) epsilon_static: ListMatrix3D | None = Field( None, description="The high-frequency dielectric constant" ) epsilon_static_wolfe: ListMatrix3D | None = Field( None, description="The high-frequency dielectric constant w/o local field effects", ) frequency_dependent_dielectric: FrequencyDependentDielectric | None = Field( None, description="Frequency-dependent dielectric information from an LOPTICS " "calculation", ) is_gap_direct: bool | None = Field( None, description="Whether the band gap is direct" ) is_metal: bool | None = Field(None, description="Whether the system is metallic") locpot: dict[str, list[float]] | None = Field( None, description="Average of the local potential along the crystal axes" ) mag_density: float | None = Field( None, description="The magnetization density, defined as total_mag/volume " "(units of A^-3)", ) optical_absorption_coeff: list[float] | None = Field( None, description="Optical absorption coefficient in cm^-1" ) outcar: OutcarType | None = Field( None, description="Information extracted from the OUTCAR file" ) structure: StructureType | None = Field( None, description="The final structure from the calculation" ) transition: str | None = Field( None, description="Band gap transition given by CBM and VBM k-points" ) vbm: float | None = Field( None, description="The valence band maximum in eV (if system is not metallic)" ) class CalculationOutput(CoreCalculationOutput): """Wrapper for CoreCalculationOutput for parsing and storing larger fields.""" elph_displaced_structures: ElectronPhononDisplacedStructures | None = Field( None, description="Electron-phonon displaced structures, generated by setting " "PHON_LMC = True.", ) force_constants: list[list[Matrix3D]] | None = Field( None, description="Force constants between every pair of atoms in the structure" ) ionic_steps: list[IonicStep] | None = Field( None, description="Energy, forces, structure, etc. for each ionic step" ) normalmode_eigenvals: list[float] | None = Field( None, description="Normal mode eigenvalues of phonon modes at Gamma. " "Note the unit changed between VASP 5 and 6.", ) normalmode_eigenvecs: list[list[Vector3D]] | None = Field( None, description="Normal mode eigenvectors of phonon modes at Gamma" ) normalmode_frequencies: list[float] | None = Field( None, description="Frequencies in THz of the normal modes at Gamma" ) num_electronic_steps: list[int] | None = Field( None, description="The number of electronic steps in each ionic step." ) run_stats: RunStatistics | None = Field( None, description="Summary of runtime statistics for this calculation" ) @classmethod def from_vasp_outputs( cls, vasprun: Vasprun, outcar: Outcar | None, contcar: Poscar | None, locpot: Locpot | None = None, elph_poscars: list[Path] | None = None, store_trajectory: StoreTrajectoryOption | str = StoreTrajectoryOption.NO, ) -> Self: """ Create a VASP output document from VASP outputs. Parameters ---------- vasprun A Vasprun object. outcar An Outcar object. contcar A Poscar object. locpot A Locpot object. elph_poscars Path to displaced electron-phonon coupling POSCAR files generated using ``PHON_LMC = True``. store_trajectory Whether to store ionic steps as a pymatgen Trajectory object. Different value tune the amount of data from the ionic_steps stored in the Trajectory. If not NO, the `ionic_steps` field is left as None. Returns ------- The VASP calculation output document. """ try: bandstructure = vasprun.get_band_structure(efermi="smart") bandgap_info = bandstructure.get_band_gap() electronic_output = dict( efermi=bandstructure.efermi, vbm=bandstructure.get_vbm()["energy"], cbm=bandstructure.get_cbm()["energy"], bandgap=bandgap_info["energy"], is_gap_direct=bandgap_info["direct"], is_metal=bandstructure.is_metal(), direct_gap=bandstructure.get_direct_band_gap(), transition=bandgap_info["transition"], ) except Exception: logger.warning("Error in parsing bandstructure") if vasprun.incar["IBRION"] == 1: logger.warning("VASP doesn't properly output efermi for IBRION == 1") electronic_output = {} freq_dependent_diel: FrequencyDependentDielectric | None = None try: freq_dependent_diel = FrequencyDependentDielectric.from_vasprun(vasprun) except KeyError: pass locpot_avg = None if locpot: locpot_avg = { str(i): locpot.get_average_along_axis(i).tolist() for i in range(3) } # parse force constants phonon_output = {} if hasattr(vasprun, "force_constants"): # convert eigenvalues to frequency eigs = -vasprun.normalmode_eigenvals frequencies = np.sqrt(np.abs(eigs)) * np.sign(eigs) # convert to THz in VASP 5 and lower; VASP 6 uses THz internally major_version = int(vasprun.vasp_version.split(".")[0]) if major_version < 6: frequencies *= 15.633302 phonon_output = dict( force_constants=vasprun.force_constants.tolist(), normalmode_frequencies=frequencies.tolist(), normalmode_eigenvals=vasprun.normalmode_eigenvals.tolist(), normalmode_eigenvecs=vasprun.normalmode_eigenvecs.tolist(), ) if outcar and contcar: outcar_dict = outcar.as_dict() # use structure from CONTCAR as it is written to # greater precision than in the vasprun # but still need to copy the charge over structure = contcar.structure structure._charge = vasprun.final_structure._charge mag_density = ( outcar.total_mag / structure.volume if outcar.total_mag else None ) if len(outcar.magnetization) != 0: # patch calculated magnetic moments into final structure magmoms = [m["tot"] for m in outcar.magnetization] structure.add_site_property("magmom", magmoms) else: logger.warning( "No OUTCAR/CONTCAR available, some information will be missing from TaskDoc." ) outcar_dict = {} structure = vasprun.final_structure mag_density = None # Parse DOS properties dosprop_dict = ( _get_band_props(vasprun.complete_dos, structure) if hasattr(vasprun, "complete_dos") and vasprun.parameters["LORBIT"] >= 11 else {} ) elph_structures: ElectronPhononDisplacedStructures | None = None if elph_poscars is not None: elph_structures_dct: dict[str, list[Any]] = { "temperatures": [], "structures": [], } for elph_poscar in elph_poscars: temp = str(elph_poscar.name).replace("POSCAR.T=", "").replace(".gz", "") elph_structures_dct["temperatures"].append(temp) elph_structures_dct["structures"].append( Structure.from_file(elph_poscar) ) elph_structures = ElectronPhononDisplacedStructures(**elph_structures_dct) store_trajectory = StoreTrajectoryOption(store_trajectory) ionic_steps = ( vasprun.ionic_steps if store_trajectory == StoreTrajectoryOption.NO else None ) num_elec_steps = None if ionic_steps is not None: num_elec_steps = [ len(ionic_step.get("electronic_steps", []) or []) for ionic_step in ionic_steps ] # The `epsilon_*` attrs of vasprun are now numpy arrays # and need to be explicitly checked if they are empty epsilons: dict[str, np.ndarray | list | None] = {} for attr in ("epsilon_static", "epsilon_static_wolfe", "epsilon_ionic"): if (v := getattr(vasprun, attr, None)) is not None and len(v) > 0: epsilons[attr] = v else: epsilons[attr] = None return cls( structure=structure, energy=vasprun.final_energy, energy_per_atom=vasprun.final_energy / len(structure), mag_density=mag_density, frequency_dependent_dielectric=freq_dependent_diel, elph_displaced_structures=elph_structures, dos_properties=dosprop_dict, ionic_steps=( [IonicStep(**step) for step in ionic_steps] if ionic_steps else None ), num_electronic_steps=num_elec_steps, locpot=locpot_avg, outcar=outcar_dict, run_stats=RunStatistics.from_outcar(outcar) if outcar else None, dielectric_properties=( DielectricProperties.from_outcar(outcar) if outcar else None ), **epsilons, # type: ignore[arg-type] **electronic_output, **phonon_output, ) @type_override({"ddec6": str}) class Calculation(CalculationBaseModel): """Full VASP calculation inputs and outputs.""" dir_name: str | None = Field( None, description="The directory for this VASP calculation" ) vasp_version: str | None = Field( None, description="VASP version used to perform the calculation" ) has_vasp_completed: Annotated[ TaskState | None, BeforeValidator(lambda v: TaskState(v) if v is not None else None), ] = Field(None, description="Whether VASP completed the calculation successfully") input: CalculationInput | None = Field( None, description="VASP input settings for the calculation" ) output: CalculationOutput | None = Field( None, description="The VASP calculation output" ) completed_at: str | None = Field( None, description="Timestamp for when the calculation was completed" ) task_name: str | None = Field( None, description="Name of task given by custodian (e.g., relax1, relax2)" ) output_file_paths: Annotated[ dict[str, str] | None, BeforeValidator(_dict_items_zipper) ] = Field( None, description="Paths of the VASP output files " "associated with this calculation", ) bader: BaderAnalysis | None = Field( None, description="Output from bader charge analysis" ) ddec6: JsonDictType = Field(None, description="Output from DDEC6 charge analysis") run_type: RunType | None = Field( None, description="Calculation run type (e.g., HF, HSE06, PBE)" ) task_type: TaskType | None = Field( None, description="Calculation task type (e.g., Structure Optimization)." ) calc_type: CalcType | None = Field( None, description="Return calculation type (run type + task_type)." ) @classmethod def from_vasp_files( cls, dir_name: Path | str, task_name: str, vasprun_file: Path | str, outcar_file: Path | str, contcar_file: Path | str, volumetric_files: list[Path] | None = None, elph_poscars: list[Path] | None = None, oszicar_file: Path | str | None = None, potcar_spec_file: Path | str | None = None, parse_dos: str | bool = False, parse_bandstructure: str | bool = False, average_locpot: bool = True, run_bader: bool = False, run_ddec6: bool | str = False, strip_bandstructure_projections: bool = False, strip_dos_projections: bool = False, store_volumetric_data: tuple[str] | None = None, store_trajectory: StoreTrajectoryOption | str = StoreTrajectoryOption.NO, vasprun_kwargs: dict | None = None, use_emmet_models: bool = SETTINGS.USE_EMMET_MODELS, ) -> tuple[ "Calculation", dict[VaspObject, ChgcarLike] | dict[VaspObject, VolumetricData] ]: """ Create a VASP calculation document from a directory and file paths. Parameters ---------- dir_name The directory containing the calculation outputs. task_name The task name. vasprun_file Path to the vasprun.xml file. outcar_file Path to the OUTCAR file. contcar_file Path to the CONTCAR file volumetric_files Path to volumetric files. elph_poscars Path to displaced electron-phonon coupling POSCAR files generated using ``PHON_LMC = True`` oszicar_file Path to the OSZICAR file potcar_spec_file : Path | str | None = None Path to a POTCAR.spec file. Used in rehydration of a calculation from archived data, where the original POTCAR is not available. parse_dos Whether to parse the DOS. Can be: - "auto": Only parse DOS if there are no ionic steps (NSW = 0). - True: Always parse DOS. - False: Never parse DOS. parse_bandstructure How to parse the bandstructure. Can be: - "auto": Parse the bandstructure with projections for NSCF calculations and decide automatically if it's line or uniform mode. - "line": Parse the bandstructure as a line mode calculation with projections - True: Parse the bandstructure as a uniform calculation with projections . - False: Parse the band structure without projects and just store vbm, cbm, band_gap, is_metal and efermi rather than the full band structure object. average_locpot Whether to store the average of the LOCPOT along the crystal axes. run_bader : bool = False Whether to run bader on the charge density. run_ddec6 : bool or str = False Whether to run DDEC6 on the charge density. If a string, it's interpreted as the path to the atomic densities directory. Can also be set via the DDEC6_ATOMIC_DENSITIES_DIR environment variable. The files are available at https://sourceforge.net/projects/ddec/files. strip_dos_projections Whether to strip the element and site projections from the density of states. This can help reduce the size of DOS objects in systems with many atoms. strip_bandstructure_projections Whether to strip the element and site projections from the band structure. This can help reduce the size of DOS objects in systems with many atoms. store_volumetric_data Which volumetric files to store. store_trajectory Whether to store the ionic steps in a pymatgen Trajectory object and the amount of data to store from the ionic_steps. Can be: - FULL: Store the Trajectory. All the properties from the ionic_steps are stored in the frame_properties except for the Structure, to avoid redundancy. - PARTIAL: Store the Trajectory. All the properties from the ionic_steps are stored in the frame_properties except from Structure and ElectronicStep. - NO: Trajectory is not Stored. If not NO, :obj:'.CalculationOutput.ionic_steps' is set to None to reduce duplicating information. vasprun_kwargs Additional keyword arguments that will be passed to the Vasprun init. use_emmet_models : bool = True Whether to store VASP objects as emmet-core models (True, default) or as pymatgen models (False) Returns ------- Calculation A VASP calculation document. """ dir_name = Path(dir_name) vasprun_file = dir_name / vasprun_file outcar_file = dir_name / outcar_file contcar_file = dir_name / contcar_file vasprun_kwargs = vasprun_kwargs if vasprun_kwargs else {} volumetric_files = [dir_name / v for v in (volumetric_files or [])] vasprun = Vasprun(vasprun_file, **vasprun_kwargs) outcar = Outcar(outcar_file) if ( os.path.getsize(contcar_file) == 0 and vasprun.parameters.get("NELM", 60) == 1 ): contcar = Poscar(vasprun.final_structure) else: contcar = Poscar.from_file(contcar_file) completed_at = str(datetime.fromtimestamp(Path(vasprun_file).stat().st_mtime)) output_file_paths = _get_output_file_paths(volumetric_files) vasp_objects = _get_volumetric_data( output_file_paths, store_volumetric_data, use_emmet_models ) if (dos := _parse_dos(parse_dos, vasprun, use_emmet_models)) is not None: if strip_dos_projections: setattr( dos, "projected_densities" if use_emmet_models else "pdos", None ) vasp_objects[VaspObject.DOS] = dos # type: ignore bandstructure = _parse_bandstructure( parse_bandstructure, vasprun, use_emmet_models ) if bandstructure is not None: if strip_bandstructure_projections: bandstructure.projections = None vasp_objects[VaspObject.BANDSTRUCTURE] = bandstructure # type: ignore bader = None if run_bader and VaspObject.CHGCAR in output_file_paths: suffix = "" if task_name == "standard" else f".{task_name}" bader = bader_analysis_from_path(str(dir_name), suffix=suffix) ddec6 = None if run_ddec6 and VaspObject.CHGCAR in output_file_paths: densities_path = run_ddec6 if isinstance(run_ddec6, (str, Path)) else None ddec6 = ChargemolAnalysis( path=dir_name, atomic_densities_path=densities_path ).summary locpot: Locpot | None = None if average_locpot: if VaspObject.LOCPOT in vasp_objects: if use_emmet_models: locpot = vasp_objects[VaspObject.LOCPOT].to_pmg(pmg_cls=Locpot) # type: ignore else: locpot = vasp_objects[VaspObject.LOCPOT] # type: ignore elif VaspObject.LOCPOT in output_file_paths: locpot_file = output_file_paths[VaspObject.LOCPOT] # type: ignore locpot = Locpot.from_file(locpot_file) potcar_spec: list[PotcarSpec] | None = None if potcar_spec_file: potcar_spec = PotcarSpec.from_file(dir_name / potcar_spec_file) input_doc = CalculationInput.from_vasprun(vasprun, potcar_spec=potcar_spec) this_task_type = task_type(input_doc.model_dump()) store_trajectory = StoreTrajectoryOption(store_trajectory) output_doc = CalculationOutput.from_vasp_outputs( vasprun, outcar, contcar, locpot=locpot, elph_poscars=elph_poscars, store_trajectory=store_trajectory, ) if store_trajectory != StoreTrajectoryOption.NO: temperatures: list[float] | None = None if oszicar_file: try: oszicar = Oszicar(dir_name / oszicar_file) _temperatures: list[float | None] = [ osz_is.get("T") for osz_is in oszicar.ionic_steps ] if all(t is not None for t in _temperatures): temperatures = _temperatures # type: ignore[assignment] except ValueError: # there can be errors in parsing the floats from OSZICAR pass traj_class = ( Trajectory if this_task_type == TaskType.Molecular_Dynamics else RelaxTrajectory ) vasp_objects[VaspObject.TRAJECTORY] = traj_class.from_vasprun( # type: ignore[index] vasprun, store_electronic_steps=(store_trajectory == StoreTrajectoryOption.FULL), temperature=temperatures, ) if not use_emmet_models: vasp_objects[VaspObject.TRAJECTORY] = vasp_objects[ # type: ignore[union-attr] VaspObject.TRAJECTORY ].to_pmg() # MD run if vasprun.parameters.get("IBRION", -1) == 0: if vasprun.parameters.get("NSW", 0) == vasprun.md_n_steps: has_vasp_completed = TaskState.SUCCESS else: has_vasp_completed = TaskState.FAILED # others else: has_vasp_completed = ( TaskState.SUCCESS if vasprun.converged else TaskState.FAILED ) return ( cls( dir_name=str(dir_name), task_name=task_name, vasp_version=vasprun.vasp_version, has_vasp_completed=has_vasp_completed, completed_at=completed_at, input=input_doc, output=output_doc, output_file_paths={ k.name.lower(): v for k, v in output_file_paths.items() }, bader=bader, ddec6=ddec6, run_type=run_type(input_doc.parameters), task_type=this_task_type, calc_type=calc_type(input_doc.model_dump(), input_doc.parameters), ), vasp_objects, ) @classmethod def from_vasprun( cls, path: Path | str, task_name: str = "Unknown vapsrun.xml", vasprun_kwargs: dict | None = None, ) -> Self: """ Create a VASP calculation document from a directory and file paths. Parameters ---------- path Path to the vasprun.xml file. task_name The task name. vasprun_kwargs Additional keyword arguments that will be passed to the Vasprun init. Returns ------- Calculation A VASP calculation document. """ path = Path(path) vasprun_kwargs = vasprun_kwargs if vasprun_kwargs else {} vasprun = Vasprun(path, **vasprun_kwargs) completed_at = str(datetime.fromtimestamp(path.stat().st_mtime)) input_doc = CalculationInput.from_vasprun(vasprun) output_doc = CalculationOutput.from_vasp_outputs( vasprun, outcar=None, contcar=None, ) # MD run if vasprun.parameters.get("IBRION", -1) == 0: if vasprun.parameters.get("NSW", 0) == vasprun.nionic_steps: has_vasp_completed = TaskState.SUCCESS else: has_vasp_completed = TaskState.FAILED # others else: has_vasp_completed = ( TaskState.SUCCESS if vasprun.converged else TaskState.FAILED ) return cls( # type: ignore[call-arg] dir_name=str(path.resolve().parent), task_name=task_name, vasp_version=vasprun.vasp_version, has_vasp_completed=has_vasp_completed, completed_at=completed_at, input=input_doc, output=output_doc, output_file_paths={}, run_type=run_type(input_doc.parameters), task_type=task_type(input_doc.model_dump()), calc_type=calc_type(input_doc.model_dump(), input_doc.parameters), ) def _get_output_file_paths(volumetric_files: list[Path]) -> dict[VaspObject, str]: """ Get the output file paths for VASP output files from the list of volumetric files. Parameters ---------- volumetric_files A list of volumetric files associated with the calculation. Returns ------- dict[VaspObject, str] A mapping between the VASP object type and the file path. """ return { vasp_object: str(volumetric_file) for vasp_object in VaspObject for volumetric_file in volumetric_files if vasp_object.name in str(volumetric_file) } def _get_volumetric_data( output_file_paths: dict[VaspObject, str], store_volumetric_data: tuple[str] | None, use_emmet_models: bool, ) -> dict[VaspObject, ChgcarLike] | dict[VaspObject, VolumetricData]: """ Load volumetric data files from a directory. Parameters ---------- output_file_paths A dictionary mapping the data type to absolute file paths. store_volumetric_data The volumetric data files to load. E.g., `("chgcar", "locpot")`. Provided as a list of strings note you can use either the keys or the values available in the `VaspObject` enum (e.g., "locpot" or "LOCPOT") are both valid. Whether to store VASP objects as emmet-core models (True, default) or as pymatgen models (False) Returns ------- dict[VaspObject, ChgcarLike] or dict[VaspObject, VolumetricData] A dictionary mapping the VASP object data type (`VaspObject.LOCPOT`, `VaspObject.CHGCAR`, etc) to the volumetric data object. """ from emmet.core.io.pymatgen import Chgcar if store_volumetric_data is None or len(store_volumetric_data) == 0: return {} volumetric_data: dict[VaspObject, ChgcarLike] | dict[VaspObject, VolumetricData] = ( {} ) for file_type, file in output_file_paths.items(): if ( file_type.name not in store_volumetric_data and file_type.value not in store_volumetric_data ): continue try: # assume volumetric data is all in CHGCAR format pmg_cls = Locpot if file_type == VaspObject.LOCPOT else Chgcar vobj = pmg_cls.from_file(str(file)) volumetric_data[file_type] = ( ChgcarLike.from_pmg(vobj) if use_emmet_models else vobj ) except Exception as exc: raise ValueError(f"Failed to parse {file_type} at {file}:\n{exc}.") return volumetric_data def _parse_dos( parse_mode: str | bool, vasprun: Vasprun, use_emmet_models: bool, ) -> ElectronicDos | CompleteDos | None: """Parse DOS. See Calculation.from_vasp_files for supported arguments.""" nsw = vasprun.incar.get("NSW", 0) if parse_mode is True or (parse_mode == "auto" and nsw < 1): if use_emmet_models: return ElectronicDos.from_pmg(vasprun.complete_dos) return vasprun.complete_dos return None def _parse_bandstructure( parse_mode: str | bool, vasprun: Vasprun, use_emmet_models: bool, ) -> ElectronicBS | BandStructure | None: """Parse band structure. See Calculation.from_vasp_files for supported arguments.""" vasprun_file = vasprun.filename bs: ElectronicBS | BandStructure | None = None # only save the bandstructure if not moving ions if parse_mode == "auto" and vasprun.incar.get("NSW", 0) <= 1: if vasprun.incar.get("ICHARG", 0) > 10: # NSCF calculation bs_vrun = BSVasprun(vasprun_file, parse_projected_eigen=True) try: # try parsing line mode bs = bs_vrun.get_band_structure(line_mode=True, efermi="smart") except Exception: # treat as a regular calculation bs = bs_vrun.get_band_structure(efermi="smart") else: # Not a NSCF calculation bs_vrun = BSVasprun(vasprun_file, parse_projected_eigen=False) bs = bs_vrun.get_band_structure(efermi="smart") elif parse_mode: # legacy line/True behavior for bandstructure_mode bs_vrun = BSVasprun(vasprun_file, parse_projected_eigen=True) bs = bs_vrun.get_band_structure(line_mode=parse_mode == "line", efermi="smart") if bs and use_emmet_models: return ElectronicBS.from_pmg(bs) return bs def _get_band_props( complete_dos: CompleteDos, structure: Structure ) -> dict[str, dict[str, dict[str, float]]]: """ Calculate band properties from a CompleteDos object and Structure. Parameters ---------- complete_dos A CompleteDos object. structure a pymatgen Structure object. Returns ------- dict A dictionary of element and orbital-projected DOS properties. """ dosprop_dict: dict[str, dict[str, dict[str, float]]] = {} if not complete_dos.pdos: # It's possible to have a CompleteDos object with only structure info and no projected DOS info return dosprop_dict for el in structure.composition.elements: el_name = str(el.name) dosprop_dict[el_name] = {} for orb_type in [ OrbitalType(x) for x in range(OrbitalType[el.block].value + 1) # type: ignore[misc] ]: try: dosprop_dict[el_name][str(orb_type)] = { "filling": complete_dos.get_band_filling( band=orb_type, elements=[el] ), "center": complete_dos.get_band_center( band=orb_type, elements=[el] ), "bandwidth": complete_dos.get_band_width( band=orb_type, elements=[el] ), "skewness": complete_dos.get_band_skewness( band=orb_type, elements=[el] ), "kurtosis": complete_dos.get_band_kurtosis( band=orb_type, elements=[el] ), "upper_edge": complete_dos.get_upper_band_edge( band=orb_type, elements=[el] ), } except KeyError: # No projected DOS available for that particular element + orbital character continue return dosprop_dict def _calculation_to_trajectory_dict( calc: Calculation, traj_class: RelaxTrajectory | Trajectory = RelaxTrajectory, ) -> tuple[dict[str, list[Any]], RunType, TaskType, CalcType, float | None]: """Convert a single VASP calculation to Trajectory._from_dict compatible dict. Parameters ----------- calc (emmet.core.vasp.calculation.Calculation) traj_class : RelaxTrajectory The trajectory class used in parsing ionic step properties to save. Returns ----------- dict, RunType, TaskType, CalcType, float | None """ ct: CalcType | None = None rt: RunType | None = None tt: TaskType | None = None time_step: float | None = None ionic_step_props = ( set(traj_class.model_fields["ionic_step_properties"].default) .difference( { "energy", "magmoms", } ) .intersection(set(IonicStep.model_fields)) ) # refresh calc, run, and task type if possible if calc.input: vis = calc.input.model_dump() padded_params = { **(calc.input.parameters or {}), **(calc.input.incar or {}), } ct = calc_type(vis, padded_params) rt = run_type(padded_params) tt = task_type(vis) time_step = ( padded_params.get("POTIM") if padded_params.get("IBRION", -1) == 0 else None ) props: dict[str, list] = {} if calc.output: remap = {"energy": "e_0_energy"} ionic_steps = calc.output.ionic_steps or [] props.update( { k: [getattr(ionic_step, remap.get(k, k)) for ionic_step in ionic_steps] for k in {"structure", "energy", *ionic_step_props} } ) return props, rt, tt, ct, time_step def get_trajectories_from_calculations( calculations: list[Calculation], separate: bool = True, traj_class: Type[RelaxTrajectory] = RelaxTrajectory, **kwargs, ) -> list[RelaxTrajectory]: """ Create trajectories from a list of Calculation Objects. Includes an option to join trajectories with the same `calc_type`. Note that if no input is provided in the calculation, the calculation is split off into its own trajectory. By default, splits every calculation into a separate `Trajectory`. Parameters ----------- task_doc : emmet.core.TaskDoc separate : bool = True by default Whether to split all calculations into separate Trajectory objects, or to join them if their calc types are identical. traj_class : RelaxTrajectory Class to use in deserializing the trajectory data. Defaults to RelaxTrajectory, which contains just enough fields for a relaxation trajectory. Could be used to return a full Trajectory if MD data is desired. kwargs Other kwargs to pass to Trajectory Returns ----------- list of Trajectory """ trajs: list[RelaxTrajectory] = [] props: dict[str, list[Any]] = {} old_meta: dict[str, Any] = { k: None for k in ("run_type", "task_type", "calc_type", "time_step") } new_meta = deepcopy(old_meta) for icr, cr in enumerate(calculations): ( new_props, new_meta["run_type"], new_meta["task_type"], new_meta["calc_type"], new_meta["time_step"], ) = _calculation_to_trajectory_dict(cr, traj_class=traj_class) if ( separate or old_meta["calc_type"] != new_meta["calc_type"] or new_meta["calc_type"] is None or icr == 0 ): # Either CalcType changed or no calculation input was provided # or this is the first calculation in `calcs_reversed` # Append existing trajectory to list of trajectories, and restart if icr > 0: trajs.append(traj_class._from_dict(props, **old_meta, **kwargs)) # type: ignore[arg-type] props = deepcopy(new_props) else: for k, new_vals in new_props.items(): props[k].extend(new_vals) for k, v in new_meta.items(): old_meta[k] = v # create final trajectory trajs.append(traj_class._from_dict(props, **old_meta, **kwargs)) # type: ignore[arg-type] return trajs ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8290114 emmet_core-0.87.2/emmet/core/vasp/material.py0000644000175100017510000003011215240717720020557 0ustar00runnerrunner"""Core definition of a Materials Document""" from typing import Mapping, Self from pydantic import BaseModel, Field from emmet.core.io.pymatgen import SpacegroupAnalyzer, oxide_type, StructureMatcher from emmet.core.base import EmmetMeta from emmet.core.material import MaterialsDoc as CoreMaterialsDoc from emmet.core.material import PropertyOrigin from emmet.core.mpid import AlphaID from emmet.core.settings import EmmetSettings from emmet.core.tasks import ValidationTaskDoc from emmet.core.types.pymatgen_types.computed_entries_adapter import ( ComputedStructureEntryType, ) from emmet.core.types.typing import IdentifierType from emmet.core.utils import utcnow from emmet.core.vasp.calc_types import CalcType, RunType, TaskType SETTINGS = EmmetSettings() class BlessedCalcs(BaseModel, populate_by_name=True): GGA: ComputedStructureEntryType | None = Field(None) GGA_U: ComputedStructureEntryType | None = Field(None, alias="GGA+U") PBESol: ComputedStructureEntryType | None = Field(None, alias="PBEsol") SCAN: ComputedStructureEntryType | None = Field(None) R2SCAN: ComputedStructureEntryType | None = Field(None, alias="r2SCAN") HSE: ComputedStructureEntryType | None = Field(None, alias="HSE06") class MaterialsDoc(CoreMaterialsDoc): calc_types: Mapping[IdentifierType, CalcType] | None = Field( # type: ignore None, description="Calculation types for all the calculations that make up this material", ) task_types: Mapping[IdentifierType, TaskType] | None = Field( None, description="Task types for all the calculations that make up this material", ) run_types: Mapping[IdentifierType, RunType] | None = Field( None, description="Run types for all the calculations that make up this material", ) origins: list[PropertyOrigin] | None = Field( None, description="Struct array for tracking the provenance of properties" ) entries: BlessedCalcs | None = Field( None, description="Dictionary for tracking entries for VASP calculations" ) @classmethod def from_tasks( cls, task_group: list[ValidationTaskDoc], structure_quality_scores: dict[ str, int ] = SETTINGS.VASP_STRUCTURE_QUALITY_SCORES, use_statics: bool = SETTINGS.VASP_USE_STATICS, commercial_license: bool = True, ) -> Self: """ Converts a group of tasks into one material Args: task_group: List of task document structure_quality_scores: quality scores for various calculation types use_statics: Use statics to define a material commercial_license: Whether the data should be licensed with BY-C (otherwise BY-NC). """ if len(task_group) == 0: raise Exception("Must have more than one task in the group.") # Metadata last_updated = max(task.last_updated for task in task_group) created_at = min(task.completed_at for task in task_group) task_ids = list({task.task_id for task in task_group}) deprecated_tasks = {task.task_id for task in task_group if not task.is_valid} run_types = {task.task_id: task.run_type for task in task_group} task_types = {task.task_id: task.task_type for task in task_group} calc_types = {task.task_id: task.calc_type for task in task_group} structure_optimizations = [ task for task in task_group if task.task_type == TaskType.Structure_Optimization # type: ignore ] statics = [task for task in task_group if task.task_type == TaskType.Static] # type: ignore structure_calcs = ( structure_optimizations + statics if use_statics else structure_optimizations ) validity_check = [doc for doc in structure_calcs if doc.is_valid] if not validity_check: raise ValueError("Group must contain at least one valid task") # Material ID possible_mat_ids = [task.task_id for task in structure_optimizations] if use_statics: possible_mat_ids += [task.task_id for task in statics] material_id = AlphaID(min(possible_mat_ids), prefix="mp") # Always prefer a static over a structure opt structure_task_quality_scores = {"Structure Optimization": 1, "Static": 2} def _structure_eval(task: ValidationTaskDoc): """ Helper function to order structures optimization and statics calcs by - Functional Type - Spin polarization - Special Tags - Energy """ task_run_type = task.run_type _SPECIAL_TAGS = ["LASPH", "ISPIN"] special_tags = sum( ( task.input.parameters.get(tag, False) if task.input.parameters else False ) for tag in _SPECIAL_TAGS ) return ( -1 * int(task.is_valid), -1 * structure_quality_scores.get(task_run_type.value, 0), -1 * structure_task_quality_scores.get(task.task_type.value, 0), -1 * special_tags, task.output.energy_per_atom, ) best_structure_calc = sorted(structure_calcs, key=_structure_eval)[0] structure = best_structure_calc.output.structure # Initial Structures initial_structures = [task.input.structure for task in task_group] sm = StructureMatcher( ltol=0.1, stol=0.1, angle_tol=0.1, scale=False, attempt_supercell=False ) initial_structures = [ group[0] for group in sm.group_structures(initial_structures) ] # Deprecated deprecated = all(task.task_id in deprecated_tasks for task in structure_calcs) deprecated = deprecated or best_structure_calc.task_id in deprecated_tasks # Origins origins = [ PropertyOrigin( name="structure", task_id=best_structure_calc.task_id, last_updated=best_structure_calc.last_updated, ) ] # Entries # **current materials docs must contain at last one GGA or GGA+U entry # Always prefer a static over a structure opt entry_task_quality_scores = {"Structure Optimization": 1, "Static": 2} def _entry_eval(task: ValidationTaskDoc): """ Helper function to order entries and statics calcs by - Spin polarization - Special Tags - Energy """ _SPECIAL_TAGS = ["LASPH", "ISPIN"] special_tags = sum( ( task.input.parameters.get(tag, False) if task.input.parameters else False ) for tag in _SPECIAL_TAGS ) return ( -1 * int(task.is_valid), -1 * entry_task_quality_scores.get(task.task_type.value, 0), -1 * special_tags, task.output.energy_per_atom, ) # Entries # **current materials docs must contain at last one GGA or GGA+U entry entries = {} all_run_types = set(run_types.values()) for rt in all_run_types: relevant_calcs = sorted( [doc for doc in structure_calcs if doc.run_type == rt and doc.is_valid], key=_entry_eval, ) if relevant_calcs: best_task_doc = relevant_calcs[0] entry = { "@class": "ComputedStructureEntry", "@module": "pymatgen.entries.computed_entries", "composition": best_task_doc.output.structure.composition, "correction": 0.0, "data": { "aspherical": best_task_doc.input.parameters.get( "LASPH", False ), "last_updated": str(utcnow()), "oxide_type": oxide_type(best_task_doc.output.structure), "material_id": material_id, "task_id": best_task_doc.task_id, }, "energy": best_task_doc.output.energy, "entry_id": f"{material_id}-{rt}", "parameters": { "hubbards": best_task_doc.input.hubbards, "is_hubbard": best_task_doc.input.is_hubbard, "potcar_spec": ( [dict(d) for d in best_task_doc.input.potcar_spec] if best_task_doc.input.potcar_spec else [] ), "run_type": str(best_task_doc.run_type), }, "structure": best_task_doc.output.structure, } entries[rt] = entry if not any( run_type in entries for run_type in (RunType.GGA, RunType.GGA_U, RunType.r2SCAN) ): raise ValueError( "Individual material entry must contain at least one GGA, GGA+U, or r2SCAN calculation" ) # Builder meta and license builder_meta = EmmetMeta(license="BY-C" if commercial_license else "BY-NC") # type: ignore[call-arg] return cls.from_structure( meta_structure=structure, material_id=material_id, last_updated=last_updated, created_at=created_at, task_ids=task_ids, calc_types=calc_types, run_types=run_types, task_types=task_types, initial_structures=initial_structures, deprecated=deprecated, deprecated_tasks=deprecated_tasks, origins=origins, entries=entries, builder_meta=builder_meta, ) @classmethod def construct_deprecated_material( cls, task_group: list[ValidationTaskDoc], commercial_license: bool = True, ) -> "MaterialsDoc": """ Converts a group of tasks into a deprecated material Args: task_group: List of task document commercial_license: Whether the data should be licensed with BY-C (otherwise BY-NC). """ if len(task_group) == 0: raise Exception("Must have more than one task in the group.") # Metadata last_updated = max(task.last_updated for task in task_group) created_at = min(task.completed_at for task in task_group) task_ids = list({task.task_id for task in task_group}) deprecated_tasks = {task.task_id for task in task_group} run_types = {task.task_id: task.run_type for task in task_group} task_types = {task.task_id: task.task_type for task in task_group} calc_types = {task.task_id: task.calc_type for task in task_group} # Material ID material_id = min([task.task_id for task in task_group]) # Choose any random structure for metadata structure = SpacegroupAnalyzer( task_group[0].output.structure, symprec=0.1 ).get_conventional_standard_structure() origins = [ PropertyOrigin( name="structure", task_id=task_group[0].task_id, last_updated=task_group[0].last_updated, ) ] # Deprecated deprecated = True # Builder meta and license builder_meta = EmmetMeta(license="BY-C" if commercial_license else "BY-NC") # type: ignore[call-arg] return cls.from_structure( meta_structure=structure, material_id=material_id, last_updated=last_updated, created_at=created_at, task_ids=task_ids, calc_types=calc_types, run_types=run_types, task_types=task_types, deprecated=deprecated, deprecated_tasks=deprecated_tasks, builder_meta=builder_meta, origins=origins, ) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8290114 emmet_core-0.87.2/emmet/core/vasp/models.py0000644000175100017510000001524015240717720020251 0ustar00runnerrunner"""Common models and types needed for VASP data.""" from __future__ import annotations from typing import TYPE_CHECKING import numpy as np from pydantic import BaseModel, Field from emmet.core.io.pymatgen import ( Structure, BaseVolumetricData as PmgVolumetricData, Poscar, ) from emmet.core.types.enums import ValueEnum from emmet.core.types.pymatgen_types.structure_adapter import StructureType if TYPE_CHECKING: from collections.abc import Callable from typing_extensions import Self class ElectronicStep(BaseModel): """Document defining the information at each electronic step. Note, not all the information will be available at every step. """ alphaZ: float | None = Field(None, description="The alpha Z term.") ewald: float | None = Field(None, description="The ewald energy.") hartreedc: float | None = Field(None, description="Negative Hartree energy.") XCdc: float | None = Field(None, description="Negative exchange energy.") pawpsdc: float | None = Field( None, description="Negative potential energy with exchange-correlation energy." ) pawaedc: float | None = Field(None, description="The PAW double counting term.") eentropy: float | None = Field(None, description="The entropy (T * S).") bandstr: float | None = Field( None, description="The band energy (from eigenvalues)." ) atom: float | None = Field(None, description="The atomic energy.") e_fr_energy: float | None = Field(None, description="The free energy.") e_wo_entrp: float | None = Field(None, description="The energy without entropy.") e_0_energy: float | None = Field(None, description="The internal energy.") class VolumetricLabel(ValueEnum): TOTAL = "total" DIFF = "diff" # NB: we only need these if we have noncolinear calculations # DIFF_X = "diff_x" # DIFF_Y = "diff_y" # DIFF_Z = "diff_z" class AugChargeData(BaseModel): label: str | None = Field( None, description="The label written by VASP for the augmenation charge data." ) data: list[float] | None = Field(None, description="The augmentation charges.") class ChgcarLike(BaseModel): """Model for VASP CHGCAR-like data. Includes CHGCAR, CHG, LOCPOT, ELFCAR, AECCAR0, AECCAR1, AECCAR2, POT. All volumetric data is flattened upon storage in the model from a rank-3 tensor. C-order is used throughout. """ identifier: str | None = Field( None, description="The identifier of this calculation." ) labels: list[VolumetricLabel] | None = Field( None, description="The spin resolution of the volumetric data." ) data: list[list[float] | None] | None = Field( description="The primary volumetric data." ) data_rank: list[tuple[int, int, int]] | None = Field( None, description="The original shape of the" ) data_aug: list[list[AugChargeData] | None] | None = Field( None, description="The augmentation charge volumetric data." ) structure: StructureType | None = Field( None, description="The structure associated with the volumetric data." ) @staticmethod def parse_augmentation_charge_data( aug_data: dict[str, list[str]], ) -> dict[VolumetricLabel, list[AugChargeData]]: aug_data_arr: dict[VolumetricLabel, list[AugChargeData]] = {} for k, unfmt_data in aug_data.items(): if isinstance(unfmt_data, dict): aug_data_arr[VolumetricLabel(k)] = [ AugChargeData(label=str(atom_label), data=atom_data) for atom_label, atom_data in unfmt_data.items() ] continue if not isinstance(unfmt_data, list | np.ndarray) or not any( line.strip() for line in unfmt_data ): continue parse_meta = True num_vals = -1 aug_data_arr[VolumetricLabel(k)] = [] atom_data: list[float] = [] atom_label: str | None = None for row in unfmt_data: data = row.replace("\n", "").split() if parse_meta: if not data[0].isalpha(): # pymatgen sometimes puts extra lines here because they # exist in a CHGCAR but have no clear meaning. # probably needs a fix in pymatgen continue label = " ".join([x for x in data[:-1] if x.isalpha()]) atom_label = label atom_data = [] num_vals = int(data[-1]) parse_meta = False else: atom_data.extend([float(x) for x in data]) if len(atom_data) >= num_vals: parse_meta = True aug_data_arr[VolumetricLabel(k)].append( AugChargeData(label=atom_label, data=atom_data) ) return aug_data_arr @classmethod def from_pmg(cls, vd: PmgVolumetricData, **kwargs) -> Self: """Convert generic pymatgen volumetric data to an archive format.""" labels = [VolumetricLabel(k) for k in vd.data] data_aug = None if aug_data := cls.parse_augmentation_charge_data(vd.data_aug): # type: ignore[arg-type] data_aug = [aug_data.get(vlab) for vlab in labels] return cls( # type: ignore[call-arg] labels=labels, data=[vd.data[vlab].flatten(order="C") for vlab in labels], # type: ignore[misc] data_rank=[vd.data[vlab].shape for vlab in labels], # type: ignore[misc] data_aug=data_aug, structure=( vd.structure if isinstance(vd.structure, Structure) else Structure.from_dict(vd.structure.as_dict()) ), **kwargs, ) def to_pmg(self, pmg_cls: Callable = PmgVolumetricData) -> PmgVolumetricData: """Convert to a pymatgen VolumetricData-derived object.""" data_aug: dict[str, np.ndarray] | None = None if self.data_aug: data_aug = { vol_label.value: np.array(self.data_aug[i]) for i, vol_label in enumerate(self.labels or []) if self.data_aug[i] } return pmg_cls( Poscar(self.structure), # type: ignore[arg-type] { vol_label.value: np.array(self.data[i]).reshape( # type: ignore[index] self.data_rank[i], order="C" # type: ignore[index] ) for i, vol_label in enumerate(self.labels or []) }, data_aug=data_aug, ) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8290114 emmet_core-0.87.2/emmet/core/vasp/task_valid.py0000644000175100017510000001434715240717720021116 0ustar00runnerrunner"""Core definition of a VASP Task Document""" from __future__ import annotations from typing import Any from pydantic import BaseModel, Field from emmet.core.io.pymatgen import oxide_type, ComputedEntry, ComputedStructureEntry from emmet.core.math import Matrix3D, Vector3D from emmet.core.structure import StructureMetadata from emmet.core.task import BaseTaskDocument from emmet.core.types.enums import TaskState from emmet.core.types.pymatgen_types.structure_adapter import StructureType from emmet.core.utils import arrow_incompatible from emmet.core.vasp.calc_types import RunType, calc_type, run_type, task_type @arrow_incompatible class InputSummary(BaseModel): """ Summary of inputs for a VASP calculation """ structure: StructureType | None = Field( None, description="The input structure object" ) parameters: dict = Field( {}, description="Input parameters from VASPRUN for the last calculation in the series", ) pseudo_potentials: dict = Field( {}, description="Summary of the pseudopotentials used in this calculation" ) potcar_spec: list[dict] = Field( [{}], description="Potcar specification as a title and hash" ) is_hubbard: bool = Field(False, description="Is this a Hubbard +U calculation.") hubbards: dict = Field({}, description="The hubbard parameters used.") class OutputSummary(BaseModel): """ Summary of the outputs for a VASP calculation """ structure: StructureType | None = Field( None, description="The output structure object" ) energy: float | None = Field( None, description="The final total DFT energy for the last calculation" ) energy_per_atom: float | None = Field( None, description="The final DFT energy per atom for the last calculation" ) bandgap: float | None = Field( None, description="The DFT bandgap for the last calculation" ) forces: list[Vector3D] | None = Field( None, description="Forces on atoms from the last calculation" ) stress: Matrix3D | None = Field( None, description="Stress on the unitcell from the last calculation" ) @arrow_incompatible class RunStatistics(BaseModel): """ Summary of the Run statistics for a VASP calculation """ average_memory: float | None = Field( None, description="The average memory used in kb" ) max_memory: float | None = Field(None, description="The maximum memory used in kb") elapsed_time: float | None = Field( None, description="The real time elapsed in seconds" ) system_time: float | None = Field( None, description="The system CPU time in seconds" ) user_time: float | None = Field( None, description="The user CPU time spent by VASP in seconds" ) total_time: float | None = Field( None, description="The total CPU time for this calculation" ) cores: int | str | None = Field( None, description="The number of cores used by VASP (some clusters print `mpi-ranks` here)", ) @arrow_incompatible class TaskDocument(BaseTaskDocument, StructureMetadata): """ Definition of VASP Task Document """ calc_code: str = "VASP" run_stats: dict[str, RunStatistics] = Field( {}, description="Summary of runtime statistics for each calculation in this task", ) is_valid: bool = Field( True, description="Whether this task document passed validation or not" ) input: InputSummary = Field(default=InputSummary(**{})) output: OutputSummary = Field(default=OutputSummary(**{})) state: TaskState | None = Field(None, description="State of this calculation") orig_inputs: dict[str, Any] = Field( {}, description="Summary of the original VASP inputs" ) calcs_reversed: list[dict] = Field( [], description="The 'raw' calculation docs used to assembled this task" ) tags: list[str] | None = Field( None, description="Metadata tags for this task document" ) warnings: list[str] | None = Field( None, description="Any warnings related to this property" ) @property def run_type(self) -> RunType: params = self.calcs_reversed[0].get("input", {}).get("parameters", {}) incar = self.calcs_reversed[0].get("input", {}).get("incar", {}) return run_type({**params, **incar}) @property def task_type(self): return task_type(self.orig_inputs) @property def calc_type(self): inputs = ( self.calcs_reversed[0].get("input", {}) if len(self.calcs_reversed) > 0 else self.orig_inputs ) params = self.calcs_reversed[0].get("input", {}).get("parameters", {}) incar = self.calcs_reversed[0].get("input", {}).get("incar", {}) return calc_type(inputs, {**params, **incar}) @property def _entry_dict(self): """Dict representation of the TaskDocument's ComputedEntry.""" return { "correction": 0.0, "entry_id": self.task_id, "composition": getattr(self.output.structure, "composition", None), "energy": self.output.energy, "parameters": { "potcar_spec": self.input.potcar_spec, "is_hubbard": self.input.is_hubbard, "hubbards": self.input.hubbards, # This is done to be compatible with MontyEncoder for the ComputedEntry "run_type": str(self.run_type), }, "data": { "oxide_type": ( oxide_type(self.output.structure) if self.output.structure else None ), "aspherical": self.input.parameters.get("LASPH", True), "last_updated": self.last_updated, }, } @property def entry(self) -> ComputedEntry: """Turns a Task Doc into a ComputedEntry""" return ComputedEntry.from_dict(self._entry_dict) @property def structure_entry(self) -> ComputedStructureEntry: """Turns a Task Doc into a ComputedStructureEntry""" return ComputedStructureEntry.from_dict( { **self._entry_dict, "structure": self.output.structure, } ) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8290114 emmet_core-0.87.2/emmet/core/vasp/utils.py0000644000175100017510000002256615240717720020137 0ustar00runnerrunner"""Define utilities needed for parsing VASP calculations.""" from __future__ import annotations import logging import os from collections import defaultdict from pathlib import Path from typing import TYPE_CHECKING from pydantic import BaseModel, Field, model_validator from emmet.core.utils import get_hash_blocked if TYPE_CHECKING: from typing import Any from emmet.core.types.typing import FSPathType logger = logging.getLogger(__name__) TASK_NAMES = {"precondition"} | {f"relax{i+1}" for i in range(9)} class FileMetadata(BaseModel): """ Lightweight model to enable validation on files via MD5. """ name: str = Field( description="Name of the VASP file without suffixes (e.g., INCAR)" ) path: Path = Field(description="Path to the VASP file") hash: str | None = Field( description="Hash of the file (computed only when requested)", default=None, ) @model_validator(mode="before") def coerce_path(cls, v: Any) -> Any: """Only coerce to Path. No existence check here.""" if "path" in v: path = v["path"] if not isinstance(path, Path): path = Path(path) v["path"] = path return v def compute_hash(self) -> str | None: """Compute the hash of the file.""" if self.validate_path_exists(): try: self.hash = get_hash_blocked(self.path) except Exception: self.hash = None return self.hash def validate_path_exists(self): if not self.path.exists(): raise ValueError(f"Path does not exist: {self.path}") if not self.path.is_file(): raise ValueError(f"Path is not a file: {self.path}") return True def __hash__(self): return hash(self.path) def __eq__(self, other): if not isinstance(other, FileMetadata): return NotImplemented return self.path == other.path @property def calc_suffix(self) -> str: """Get any calculation-related suffixes, e.g., relax1.""" suffix: str | None = None suffixes = self.path.suffixes for i in range(len(suffixes)): if (s := suffixes[-1 - i].split(".")[-1]) in TASK_NAMES: suffix = s break return suffix or "standard" class CalculationLocator(BaseModel): """Object to represent calculation directory with possible file suffixes.""" path: Path = Field(description="The path to the calculation directory") modifier: str | None = Field( description="Optional modifier for the calculation", default=None ) def __hash__(self) -> int: # Resolve path to handle different representations of same path return hash((self.path.resolve(), self.modifier)) # You might not need custom __eq__ in Pydantic v2 # But if you keep it, consider path resolution: def __eq__(self, other: Any) -> bool: if not isinstance(other, CalculationLocator): return False return ( self.path.resolve() == other.path.resolve() and self.modifier == other.modifier ) VASP_INPUT_FILES = [ "INCAR", "KPOINTS", "KPOINTS_OPT", "POSCAR", "POTCAR", "POTCAR.spec", "vaspin.h5", ] VASP_ELECTRONIC_STRUCTURE = [ "EIGENVAL", "DOSCAR", ] VASP_VOLUMETRIC_FILES = ( [ "CHGCAR", "CHG", ] + [f"AECCAR{i}" for i in range(3)] + [ "ELFCAR", "LOCPOT", "POT", ] ) VASP_OUTPUT_FILES = [ "CONTCAR", "IBZKPT", "OSZICAR", "OUTCAR", "PCDAT", "PROCAR", "REPORT", "vasprun.xml", "vaspout.h5", "XDATCAR", ] VASP_RAW_DATA_ORG = { "input": VASP_INPUT_FILES.copy(), "output": VASP_OUTPUT_FILES.copy(), "volumetric": VASP_VOLUMETRIC_FILES.copy(), "electronic_structure": VASP_ELECTRONIC_STRUCTURE.copy(), "workflow": ["FW.json", "custodian.json", "transformations.json"], } REQUIRED_VASP_FILES = {"INCAR", "POSCAR", "POTCAR", "CONTCAR", "OUTCAR", "vasprun.xml"} _vasp_files = set() for v in VASP_RAW_DATA_ORG.values(): _vasp_files.update(v) VASP_RAW_DATA_ORG["input"].extend([f"{f}.orig" for f in VASP_INPUT_FILES]) def discover_vasp_files( target_dir: FSPathType, ) -> dict[str, list[FileMetadata]]: """ Scan a target directory and identify VASP files. Parameters ----------- target_dir : FSPathType Returns ----------- List of FileMetadata for the identified files. """ head_dir = Path(target_dir) vasp_files: list[FileMetadata] = [] with os.scandir(head_dir) as scan_dir: for p in scan_dir: # Check that at least one VASP file matches the file name if p.is_file() and any(f for f in _vasp_files if f in p.name): vasp_files.append(FileMetadata(name=p.name, path=Path(p.path))) by_suffix = defaultdict(list) for file_meta in vasp_files: by_suffix[file_meta.calc_suffix].append(file_meta) return dict(by_suffix) # type: ignore[arg-type] def discover_and_sort_vasp_files( target_dir: FSPathType, ) -> dict[str, dict[str, Path | list[Path]]]: """ Find and sort VASP files from a directory for TaskDoc. Parameters ----------- target_dir : FSPathType Returns ----------- dict of str (categories) to list of FileMetadata (list of VASP files in that category) """ by_type: defaultdict[str, dict[str, Path | list[Path]]] = defaultdict(dict) for calc_suffix, files in discover_vasp_files(target_dir).items(): for _f in files: f = _f.name.lower() file_path = _f.path.resolve() for k in ("vasprun", "contcar", "outcar", "potcar.spec", "oszicar"): if k in f: by_type[calc_suffix][f"{k.replace('.','_')}_file"] = file_path break else: # NB: the POT file needs the extra `"potcar" not in f` check to ensure that # POTCARs are not classed as volumetric files. if any( vf.lower() in f and "potcar" not in f for vf in VASP_VOLUMETRIC_FILES ): if "volumetric_files" not in by_type[_f.calc_suffix]: by_type[calc_suffix]["volumetric_files"] = [] by_type[calc_suffix]["volumetric_files"].append(file_path) # type: ignore[union-attr] elif "poscar.t=" in f: if "elph_poscars" not in by_type[calc_suffix]: by_type[_f.calc_suffix]["elph_poscars"] = [] by_type[calc_suffix]["elph_poscars"].append(file_path) # type: ignore[union-attr] return dict(by_type) def recursive_discover_vasp_files( target_dir: FSPathType, only_valid: bool = False, max_depth: int | None = None, ) -> dict[CalculationLocator, list[FileMetadata]]: """ Recursively scan a target directory and identify VASP files. Parameters ----------- target_dir : FSPathType only_valid : bool = False (default) Whether to only include directories which have the required minimum number of input and output files for parsing. max_depth : non-negative int or None (default) If an int, the maximum depth with which directories are scanned for VASP files. For example, if max_depth == 1, this would only search `target_dir` and any immediate sub-directories in `target_dir`. Returns ----------- dict of Path to list of FileMetadata identified as VASP files. """ head_dir = Path(target_dir).resolve() if max_depth and (max_depth < 0 or not isinstance(max_depth, int)): raise ValueError( "The maximum path depth should be a non-negative integer, " "with zero indicating that only the current directory should " "be searched." ) def _path_depth_check(tpath: FSPathType) -> bool: if max_depth and (tp := Path(tpath).resolve()) != head_dir: for depth, parent in enumerate(tp.parents): if parent == head_dir: break return depth + 1 <= max_depth return True def _recursive_discover_vasp_files( tdir: FSPathType, paths: dict[CalculationLocator, list[FileMetadata]] ) -> None: if Path(tdir).is_dir() and _path_depth_check(tdir): with os.scandir(tdir) as scan_dir: for p in scan_dir: _recursive_discover_vasp_files(p, paths) if tpaths_by_suffix := discover_vasp_files(tdir): for calc_suffix, tpaths in tpaths_by_suffix.items(): # Check if minimum number of VASP files are present # TODO: update with vaspout.h5 parsing if only_valid and any( not any(f in file.name for file in tpaths) for f in REQUIRED_VASP_FILES ): # Incomplete calculation input/output continue paths[ CalculationLocator( path=Path(tdir).resolve(), modifier=calc_suffix ) ] = tpaths vasp_files: dict[CalculationLocator, list[FileMetadata]] = {} _recursive_discover_vasp_files(target_dir, vasp_files) return vasp_files ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8292716 emmet_core-0.87.2/emmet/core/vasp/validation.py0000644000175100017510000001264215240717720021123 0ustar00runnerrunner"""Current MP tools to validate VASP calculations.""" from __future__ import annotations from typing import TYPE_CHECKING from pydantic import Field from emmet.core.io.pymatgen import ( REQUIRED_VASP_FILES, VaspValidator, Incar, LightOutcar, LightVasprun, PotcarSummaryStats, VaspFiles, VaspInputSafe, ) from emmet.core.base import EmmetBaseModel from emmet.core.types.typing import DateTimeType, IdentifierType from emmet.core.utils import arrow_incompatible from emmet.core.vasp.calc_types.enums import CalcType, RunType from emmet.core.vasp.calculation import Calculation from emmet.core.vasp.task_valid import TaskDocument from emmet.core.vasp.utils import FileMetadata, discover_vasp_files if TYPE_CHECKING: from pathlib import Path from typing_extensions import Self from emmet.core.tasks import TaskDoc @arrow_incompatible class ValidationDoc(EmmetBaseModel, VaspValidator): """ Validation document for a VASP calculation """ task_id: IdentifierType | None = Field( None, description="The task_id for this validation document" ) last_updated: DateTimeType = Field( description="The most recent time when this document was updated.", ) nelements: int | None = Field(None, description="Number of elements.") symmetry_number: int | None = Field( None, title="Space Group Number", description="The spacegroup number for the lattice.", ) run_type: RunType | None = Field( None, description="The run type of the calculation" ) calc_type: CalcType | None = Field(None, description="The calculation type.") @classmethod def from_file_metadata(cls, file_meta: list[FileMetadata], **kwargs) -> Self: """Validate files from a list of their metadata.""" vasp_file_paths: dict[str, Path] = {} for f in file_meta: if ( len(matched_files := [vf for vf in REQUIRED_VASP_FILES if vf in f.name]) > 0 ): vasp_file_paths[matched_files[0].lower().split(".")[0]] = f.path return cls.from_vasp_input(vasp_file_paths=vasp_file_paths, **kwargs) @staticmethod def task_doc_to_vasp_files(task_doc: TaskDoc | TaskDocument) -> VaspFiles: """Convert an emmet.core TaskDoc or legacy TaskDocument to VaspFiles.""" if isinstance(task_doc, TaskDocument): final_calc = Calculation(**task_doc.calcs_reversed[0]) else: final_calc = task_doc.calcs_reversed[0] potcar_stats = None if final_calc.input.potcar_spec: potcar_stats = [ PotcarSummaryStats( titel=ps.titel, keywords=ps.summary_stats["keywords"] if ps.summary_stats else None, stats=ps.summary_stats["stats"] if ps.summary_stats else None, lexch=( "pe" if final_calc.input.potcar_type[0] == "PAW_PBE" else "ca" ), ) for ps in final_calc.input.potcar_spec ] # Issue with legacy data: VASP version can include date info - remove here vasp_version = None if len(split_vasp_ver := final_calc.vasp_version.split(".")) > 0: vasp_version = ".".join(split_vasp_ver[: min(3, len(split_vasp_ver))]) return VaspFiles( user_input=VaspInputSafe( # type: ignore[call-arg] incar=Incar(final_calc.input.incar), kpoints=final_calc.input.kpoints, structure=final_calc.input.structure, potcar=potcar_stats, ), outcar=LightOutcar( **{ k: final_calc.output.outcar.get(k) for k in ("drift", "magnetization") } ), vasprun=LightVasprun( # type: ignore[call-arg] vasp_version=vasp_version, # type: ignore[arg-type] ionic_steps=[ ionic_step.model_dump() for ionic_step in final_calc.output.ionic_steps ], final_energy=task_doc.output.energy, final_structure=task_doc.output.structure, kpoints=final_calc.input.kpoints, parameters=final_calc.input.parameters, bandgap=final_calc.output.bandgap, ), ) @classmethod def from_task_doc(cls, task_doc: TaskDoc | TaskDocument, **kwargs) -> Self: """Validate a VASP calculation represented by an emmet.core TaskDoc/ument.""" vasp_files = cls.task_doc_to_vasp_files(task_doc) for k in ("run_type", "calc_type"): if not kwargs.get(k): kwargs[k] = getattr(task_doc, k, None) if not kwargs.get("symmetry_number") and task_doc.symmetry: kwargs["symmetry_number"] = task_doc.symmetry.number return cls.from_vasp_input(vasp_files=vasp_files, **kwargs) @classmethod def from_directory(cls, dir_name: str | Path, **kwargs) -> Self: """Override parent model to use file discovery method.""" vasp_files = discover_vasp_files(dir_name) # NB: this will pick "standard" over "relax*" if present, # and will select the last "relax*" if those are the only # types present final_calc_name = sorted(vasp_files)[-1] return cls.from_file_metadata(vasp_files[final_calc_name], **kwargs) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8292716 emmet_core-0.87.2/emmet/core/vasp/validation_legacy.py0000644000175100017510000004352115240717720022447 0ustar00runnerrunner"""Legacy MP tools to validate VASP calculations.""" from __future__ import annotations from typing import TYPE_CHECKING, Self import numpy as np from pydantic import BaseModel, Field, ImportString from emmet.core.io.pymatgen import Structure, Kpoints, VaspInputSet from emmet.core.base import EmmetBaseModel from emmet.core.settings import EmmetSettings from emmet.core.tasks import CoreTaskDoc, TaskDoc from emmet.core.types.enums import DeprecationMessage from emmet.core.types.typing import DateTimeType, IdentifierType from emmet.core.vasp.calc_types.enums import CalcType, TaskType from emmet.core.vasp.calculation import CoreCalculationOutput from emmet.core.vasp.task_valid import TaskDocument if TYPE_CHECKING: from collections.abc import Sequence SETTINGS = EmmetSettings() class ValidationDataDict(BaseModel): encut_ratio: float | None = Field(None) max_gradient: float | None = Field(None) kspacing_delta: float | None = Field(None) kpts_ratio: float | None = Field(None) class ValidationDoc(EmmetBaseModel, extra="allow"): """ Validation document for a VASP calculation """ task_id: IdentifierType = Field( ..., description="The task_id for this validation document" ) valid: bool = Field(False, description="Whether this task is valid or not") last_updated: DateTimeType = Field( description="Last updated date for this document", ) reasons: list[DeprecationMessage | str] | None = Field( None, description="List of deprecation tags detailing why this task isn't valid" ) warnings: list[str] = Field( [], description="List of potential warnings about this calculation" ) data: ValidationDataDict | None = Field( None, description="Dictioary of data used to perform validation." " Useful for post-mortem analysis", ) @classmethod def from_task_doc( cls, task_doc: CoreTaskDoc | TaskDoc | TaskDocument, kpts_tolerance: float | None = None, kspacing_tolerance: float = SETTINGS.VASP_KSPACING_TOLERANCE, input_sets: dict[str, ImportString] = SETTINGS.VASP_DEFAULT_INPUT_SETS, LDAU_fields: list[str] = SETTINGS.VASP_CHECKED_LDAU_FIELDS, max_allowed_scf_gradient: float = SETTINGS.VASP_MAX_SCF_GRADIENT, max_magmoms: dict[str, float] = SETTINGS.VASP_MAX_MAGMOM, potcar_stats: dict[CalcType, dict[str, str]] | None = None, bad_tags: list[str] | None = None, ) -> Self: """ Determines if a calculation is valid based on expected input parameters from a pymatgen inputset Args: task_doc: the task document to process kpts_tolerance: the tolerance to allow kpts to lag behind the input set settings kspacing_tolerance: the tolerance to allow kspacing to lag behind the input set settings input_sets: a dictionary of task_types -> pymatgen input set for validation pseudo_dir: directory of pseudopotential directory to ensure correct hashes LDAU_fields: LDAU fields to check for consistency max_allowed_scf_gradient: maximum uphill gradient allowed for SCF steps after the initial equillibriation period potcar_stats: Dictionary of potcar stat data. Mapping is calculation type -> potcar symbol -> hash value. bad_tags: List of tags for calculations to deprecate """ if not kpts_tolerance: kpts_tolerance = ( 0.4 if task_doc.tags and "mp_production_old" in task_doc.tags else 0.9 ) bandgap = task_doc.output.bandgap calc_type = task_doc.calc_type task_type = task_doc.task_type run_type = task_doc.run_type chemsys = task_doc.chemsys if isinstance(task_doc, (TaskDoc, TaskDocument)): inputs = task_doc.orig_inputs calcs_reversed = [ calc if not hasattr(calc, "model_dump") else calc.model_dump() for calc in task_doc.calcs_reversed ] if calcs_reversed[0].get("input", {}).get("structure", None): structure = calcs_reversed[0]["input"]["structure"] else: structure = task_doc.input.structure or task_doc.output.structure else: inputs = task_doc.input calcs_reversed = None structure = task_doc.input.structure or task_doc.output.structure if isinstance(structure, dict): structure = Structure.from_dict(structure) reasons = [] data = {} # type: ignore warnings: list[str] = [] if str(calc_type) in input_sets: try: valid_input_set = _get_input_set( run_type, task_type, calc_type, structure, input_sets, bandgap ) except (TypeError, KeyError, ValueError): reasons.append(DeprecationMessage.SET) valid_input_set = None try: assert valid_input_set is not None assert valid_input_set.structure is not None # Sometimes spglib can't determine space group with the default # `symprec` and `angle_tolerance`. In these cases, # `Structure.get_space_group_info()` fails valid_input_set.structure.get_space_group_info() except Exception: reasons.append(DeprecationMessage.SYMMETRY) valid_input_set = None if valid_input_set: # Checking POTCAR summary_stats if a directory is supplied if potcar_stats: if _potcar_stats_check(task_doc, potcar_stats): if task_type in [ TaskType.NSCF_Line, TaskType.NSCF_Uniform, TaskType.DFPT_Dielectric, TaskType.Dielectric, ]: warnings.append(DeprecationMessage.POTCAR.__doc__) # type: ignore else: reasons.append(DeprecationMessage.POTCAR) # Checking K-Points # Calculations that use KSPACING will not have a .kpoints attr if task_type != TaskType.NSCF_Line: # Not validating k-point data for line-mode calculations as constructing # the k-path is too costly for the builder and the uniform input set is used. if valid_input_set.kpoints is not None: if _kpoint_check( valid_input_set, inputs, calcs_reversed, data, kpts_tolerance, ): reasons.append(DeprecationMessage.KPTS) else: # warnings _kspacing_warnings( valid_input_set, inputs, data, warnings, kspacing_tolerance ) # warn, but don't invalidate if wrong ISMEAR valid_ismear = valid_input_set.incar.get("ISMEAR", 1) incar = inputs.get("incar", {}) curr_ismear = incar.get("ISMEAR", 1) if curr_ismear != valid_ismear: warnings.append( f"Inappropriate smearing settings. Set to {curr_ismear}," f" but should be {valid_ismear}" ) # Checking ENCUT encut = incar.get("ENCUT") valid_encut = valid_input_set.incar["ENCUT"] data["encut_ratio"] = float(encut) / valid_encut # type: ignore if data["encut_ratio"] < 1: reasons.append(DeprecationMessage.ENCUT) # U-value checks if _u_value_checks(task_doc, valid_input_set, warnings): reasons.append(DeprecationMessage.LDAU) if calcs_reversed: # Check the max upwards SCF step if _scf_upward_check( calcs_reversed, inputs, data, max_allowed_scf_gradient, warnings ): reasons.append(DeprecationMessage.MAX_SCF) # Check for Am and Po elements. These currently do not have proper elemental entries # and will not get treated properly by the thermo builder. if chemsys and ("Am" in chemsys or "Po" in chemsys): reasons.append(DeprecationMessage.MANUAL) # Check for magmom anomalies for specific elements if _magmom_check( calcs_reversed or task_doc.output, structure, max_magmoms=max_magmoms, ): reasons.append(DeprecationMessage.MAG) else: if "Unrecognized" in str(calc_type): reasons.append(DeprecationMessage.UNKNOWN) else: reasons.append(DeprecationMessage.SET) if len(list(set(task_doc.tags or []).intersection(bad_tags or []))) > 0: warnings.append(f"Manual Deprecation by tags: {bad_tags}") reasons.append(DeprecationMessage.MANUAL) doc = ValidationDoc( # type: ignore[call-arg] task_id=task_doc.task_id, valid=len(reasons) == 0, reasons=reasons, data=data, warnings=warnings, ) return doc def _get_input_set( run_type, task_type, calc_type, structure, input_sets, bandgap ) -> VaspInputSet: # Ensure inputsets get proper additional input values if "SCAN" in run_type.value: valid_input_set = input_sets[str(calc_type)]( structure, bandgap=bandgap ) # type: ignore elif task_type == TaskType.NSCF_Uniform or task_type == TaskType.NSCF_Line: # Constructing the k-path for line-mode calculations is too costly, so # the uniform input set is used instead and k-points are not checked. valid_input_set = input_sets[str(calc_type)](structure, mode="uniform") elif task_type == TaskType.NMR_Electric_Field_Gradient: valid_input_set = input_sets[str(calc_type)](structure, mode="efg") else: valid_input_set = input_sets[str(calc_type)](structure) return valid_input_set def _scf_upward_check(calcs_reversed, inputs, data, max_allowed_scf_gradient, warnings): skip = abs(inputs.get("incar", {}).get("NELMDL", -5)) - 1 energies = [ d["e_fr_energy"] for d in calcs_reversed[0]["output"]["ionic_steps"][-1]["electronic_steps"] ] if len(energies) > skip: max_gradient = np.max(np.gradient(energies)[skip:]) data["max_gradient"] = max_gradient if max_gradient > max_allowed_scf_gradient: return True else: warnings.append( "Not enough electronic steps to compute valid gradient" " and compare with max SCF gradient tolerance" ) return False def _u_value_checks(task_doc, valid_input_set, warnings): # NOTE: Reverting to old method of just using input.hubbards which is wrong in many instances input_hubbards = {} if task_doc.input.hubbards is None else task_doc.input.hubbards if valid_input_set.incar.get("LDAU", False) or len(input_hubbards) > 0: # Assemble required input_set LDAU params into dictionary input_set_hubbards = dict( zip( valid_input_set.poscar.site_symbols, valid_input_set.incar.get("LDAUU", []), ) ) all_elements = list(set(input_set_hubbards.keys()) | set(input_hubbards.keys())) diff_ldau_params = { el: (input_set_hubbards.get(el, 0), input_hubbards.get(el, 0)) for el in all_elements if not np.allclose(input_set_hubbards.get(el, 0), input_hubbards.get(el, 0)) } if len(diff_ldau_params) > 0: warnings.extend( [ f"U-value for {el} should be {good} but was {bad}" for el, (good, bad) in diff_ldau_params.items() ] ) return True return False def _kpoint_check(input_set, inputs, calcs_reversed, data, kpts_tolerance): """ Checks to make sure the total number of kpoints is correct """ valid_num_kpts = input_set.kpoints.num_kpts or np.prod(input_set.kpoints.kpts[0]) if calcs_reversed: input_dict = calcs_reversed[0].get("input", {}) if not input_dict: input_dict = inputs else: input_dict = inputs kpoints = input_dict.get("kpoints", {}) if isinstance(kpoints, Kpoints): kpoints = kpoints.as_dict() elif kpoints is None: kpoints = {} num_kpts = kpoints.get("nkpoints", 0) or np.prod(kpoints.get("kpoints", [1, 1, 1])) data["kpts_ratio"] = num_kpts / valid_num_kpts return data["kpts_ratio"] < kpts_tolerance def _kspacing_warnings(input_set, inputs, data, warnings, kspacing_tolerance): """ Issues warnings based on KSPACING values """ valid_kspacing = input_set.incar.get("KSPACING", 0) if kspacing := inputs.get("incar", {}).get("KSPACING"): data["kspacing_delta"] = kspacing - valid_kspacing # larger KSPACING means fewer k-points if data["kspacing_delta"] > kspacing_tolerance: warnings.append( f"KSPACING is greater than input set: {data['kspacing_delta']}" f" lower than {kspacing_tolerance} " ) elif data["kspacing_delta"] < kspacing_tolerance: warnings.append( f"KSPACING is lower than input set: {data['kspacing_delta']}" f" lower than {kspacing_tolerance} " ) def _potcar_stats_check( task_doc, potcar_stats: dict, exclude_keys: Sequence[str] | None = ["sha256", "copyr"], ): """ Checks to make sure the POTCAR summary stats is equal to the correct value from the pymatgen input set. """ data_tol = 1.0e-6 excl: set[str] = set([k.lower() for k in (exclude_keys or [])]) try: potcar_details = task_doc.calcs_reversed[0].model_dump()["input"]["potcar_spec"] except AttributeError: potcar_details = task_doc.input.model_dump()["potcar_spec"] except KeyError: # Assume it is an old calculation without potcar_spec data and treat it as passing POTCAR hash check return False use_legacy_hash_check = False if any(entry.get("summary_stats", None) is None for entry in potcar_details): # potcar_spec doesn't include summary_stats kwarg needed to check potcars # fall back to header hash checking use_legacy_hash_check = True all_match = True for entry in potcar_details: if not entry["titel"]: all_match = False break symbol = entry["titel"].split(" ")[1] ref_summ_stats = potcar_stats[str(task_doc.calc_type)].get(symbol, None) if not ref_summ_stats: # Symbol differs from reference set - deprecate all_match = False break if use_legacy_hash_check: all_match = any( all( entry[key] == ref_stat[key] for key in ( "hash", "titel", ) ) for ref_stat in ref_summ_stats ) else: entry_keys = { key: set([k.lower() for k in entry["summary_stats"]["keywords"][key]]) - excl for key in ["header", "data"] } all_match = False for ref_stat in ref_summ_stats: ref_keys = { key: set([k.lower() for k in ref_stat["keywords"][key]]) - excl for key in ["header", "data"] } key_match = all(entry_keys[k] == v for k, v in ref_keys.items()) data_match = False if key_match: data_match = all( abs( ref_stat["stats"][key][stat] - entry["summary_stats"]["stats"][key][stat] ) < data_tol for stat in ["MEAN", "ABSMEAN", "VAR", "MIN", "MAX"] for key in ["header", "data"] ) all_match = key_match and data_match if all_match: # Found at least one match to reference POTCAR summary stats, # that suffices for the check break if not all_match: break return not all_match def _magmom_check( calc: list | CoreCalculationOutput, structure: Structure, max_magmoms: dict[str, float], ): """ Checks for maximum magnetization values for specific elements. Returns True if the maximum absolute value outlined below is exceded for the associated element. """ if isinstance(calc, CoreCalculationOutput): outcar = calc.outcar else: outcar = calc[0]["output"]["outcar"] if outcar and (mag_info := outcar.get("magnetization", [])): return any( abs(mag_info[isite].get("tot", 0.0)) > abs(max_magmoms.get(site.label, np.inf)) for isite, site in enumerate(structure) ) return False def _get_unsorted_symbol_set(structure: Structure): """ Have to build structure_symbol set manually to ensure we get the right order since pymatgen sorts its symbol_set list. """ return list( { str(sp): 1 for site in structure for sp, v in site.species.items() if v != 0 }.keys() ) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8292716 emmet_core-0.87.2/emmet/core/xas.py0000644000175100017510000003211515240717720016610 0ustar00runnerrunnerfrom __future__ import annotations import warnings from itertools import groupby from functools import cached_property from typing import TYPE_CHECKING, overload import numpy as np from pydantic import Field from emmet.core.io.pymatgen import ( XAS, site_weighted_spectrum, Element, SpacegroupAnalyzer, ) from emmet.core.feff.task import TaskDocument from emmet.core.spectrum import SpectrumDoc from emmet.core.types.enums import ValueEnum, XasEdge, XasType from emmet.core.types.pymatgen_types.element_adapter import ElementType from emmet.core.types.pymatgen_types.xas_adapter import XASType from emmet.core.mpid import AlphaID from emmet.core.types.typing import ( ID_PADLEN, IdentifierType, validate_compound_identifier, ) if TYPE_CHECKING: from typing import Any, Literal from emmet.core.types.typing import CompoundIDType Type = ValueEnum("Type", [(e.name, e.value) for e in XasType]) # type: ignore[call-arg] """Type is deprecated and will be removed - migrate to XasType.""" Edge = ValueEnum("Edge", [(e.name, e.value) for e in XasEdge]) # type: ignore[call-arg] """Edge is deprecated and will be removed - migrate to XasEdge.""" @overload def validate_xas_spectrum_id( idx: str, as_components: Literal[True] = True ) -> CompoundIDType: ... @overload def validate_xas_spectrum_id( idx: str, as_components: Literal[False] = False ) -> str: ... def validate_xas_spectrum_id( idx: str, as_components: bool = False ) -> str | CompoundIDType: """Validate an XAS spectrum identifier.""" return validate_compound_identifier( idx, suffixes=(XasType, Element, XasEdge), separator="-", use_prefix=False, as_components=as_components, ) def format_spectrum_id( spectrum_id: "Any", legacy: bool, prefix: str = "mp", padlen: int = ID_PADLEN, ) -> str | None: """Render an XAS spectrum id in either legacy or new alpha form. Spectrum ids are composite identifiers built at runtime from a task id plus three typed suffix components (spectrum type, absorbing element, edge), all joined with ``-``. Their shape convention follows the same prefix-dropping rule as task ids — the leading id portion is prefixed in legacy form and bare in alpha form: - Legacy form: ``mp----`` (e.g. ``mp-779827-XANES-O-K``). - New alpha form: ``---`` (e.g. ``aaabsjpj-XANES-O-K``) with **no** ``mp-`` prefix. Parsing delegates to :func:`validate_xas_spectrum_id` (and through it to :func:`validate_compound_identifier`), so the suffix shape stays in lockstep with the canonical XAS spectrum_id schema. Args: spectrum_id: A spectrum id in either form. May be the bare composite string, or anything :func:`validate_xas_spectrum_id` accepts. legacy: If True, returns the legacy ``mp--...`` form. If False, returns the bare-alpha-prefixed ``-...`` form. prefix: The id prefix used in the legacy form. Defaults to ``"mp"``. padlen: The minimum identifier length on the alpha-form output. Defaults to 8. Returns: The formatted string. If ``spectrum_id`` is None or empty, it is returned unchanged. If parsing fails, the input is coerced to a string and returned unchanged (defensive: this helper never raises from a display path). Examples: >>> format_spectrum_id("mp-779827-XANES-O-K", legacy=False) 'aaabsjpj-XANES-O-K' >>> format_spectrum_id("aaabsjpj-XANES-O-K", legacy=True) 'mp-779827-XANES-O-K' """ # Two-step guard avoids triggering ``MPID.__eq__`` (which raises # ValueError on ``MPID(...) == ""``) when ``spectrum_id`` is an # MPID/AlphaID subclass instance rather than a plain string. if spectrum_id is None: return spectrum_id if isinstance(spectrum_id, str) and not spectrum_id: return spectrum_id try: components = validate_xas_spectrum_id(str(spectrum_id), as_components=True) except (ValueError, TypeError, IndexError): return str(spectrum_id) identifier = components["identifier"] suffix = components["suffix"] separator = components["separator"] suffix_str = separator.join(s.value for s in suffix) if legacy: base = f"{prefix}-{int(identifier)}" else: # Alpha display: bare padded identifier with no prefix, matching # the convention `validate_xas_spectrum_id(..., use_prefix=False)` # uses when emitting the canonical alpha form server-side. base = str(AlphaID(int(identifier), padlen=padlen, prefix=None)) return f"{base}{separator}{suffix_str}" class XASDoc(SpectrumDoc): """ Document describing a XAS Spectrum. """ spectrum_name: str = "XAS" spectrum: XASType | None = Field( None, description="The XAS spectrum for this calculation." ) absorbing_element: ElementType = Field(..., description="Absoring element.") spectrum_type: XasType = Field(..., description="XAS spectrum type.") edge: XasEdge = Field( ..., title="Absorption Edge", description="The interaction edge for XAS." ) @cached_property def spectrum_id(self) -> str: """Return legacy-style spectrum_id in AlphaID format.""" if not self.task_id: raise ValueError("Cannot determine `spectrum_id` without a `task_id`.") return validate_xas_spectrum_id( "-".join( [ self.task_id.string, self.spectrum_type.value, self.absorbing_element.value, self.edge.value, ] ), as_components=False, ) @classmethod def from_spectrum( cls, xas_spectrum: XAS, material_id: IdentifierType | None = None, **kwargs, ): spectrum_type = XasType(xas_spectrum.spectrum_type) edge = XasEdge(xas_spectrum.edge) return super().from_structure( meta_structure=xas_spectrum.structure, material_id=material_id, spectrum=xas_spectrum, edge=edge, spectrum_type=spectrum_type, absorbing_element=xas_spectrum.absorbing_element, **kwargs, ) @classmethod def from_task_docs( cls, all_tasks: list[TaskDocument], material_id: IdentifierType | None = None, num_samples: int = 200, ) -> list["XASDoc"]: """ Converts a set of FEFF Task Documents into XASDocs by merging XANES + EXAFS into XAFS spectra first and then merging along equivalent elements to get element averaged spectra Args: all_tasks: FEFF Task documents that have matching structure material_id: The material ID for the generated XASDocs num_samples: number of sampled points for site-weighted averaging """ all_spectra: list[XAS] = [] averaged_spectra: list[XAS] = [] # This is a hack using extra attributes within this function to carry some extra information # without generating new objects for task in all_tasks: spectrum = task.xas_spectrum spectrum.last_updated = task.last_updated spectrum.task_ids = [task.task_id] all_spectra.append(spectrum) # Pre sort by keys to remove needing to sort in the group by stage all_spectra = sorted( all_spectra, key=lambda x: ( x.absorbing_index, x.edge, x.spectrum_type, -1 * x.last_updated, ), ) # Generate Merged Spectra # Dictionary of all site to spectra mapping sites_to_spectra = { index: list(group) for index, group in groupby( all_spectra, key=lambda x: x.absorbing_index, ) } # perform spectra merging for site, spectra in sites_to_spectra.items(): type_to_spectra = { index: list(group) for index, group in groupby( spectra, key=lambda x: (x.edge, x.spectrum_type), ) } # Make K-edge XAFS spectra by merging XANES + EXAFS if ("K", "XANES") in type_to_spectra and ("K", "EXAFS") in type_to_spectra: xanes = type_to_spectra[("K", "XANES")][-1] exafs = type_to_spectra[("K", "EXAFS")][-1] try: total_spectrum = xanes.stitch(exafs, mode="XAFS") total_spectrum.absorbing_index = site total_spectrum.task_ids = xanes.task_ids + exafs.task_ids # type: ignore[attr-defined] all_spectra.append(total_spectrum) except ValueError as e: warnings.warn(f"Warning during spectral merging in XASDoC: {e}") # Make L2,3 XANES spectra by merging L2 and L3 spectra if ("L2", "XANES") in type_to_spectra and ( "L3", "XANES", ) in type_to_spectra: l2 = type_to_spectra[("L2", "XANES")][-1] l3 = type_to_spectra[("L3", "XANES")][-1] try: total_spectrum = l2.stitch(l3, mode="L23") total_spectrum.absorbing_index = site total_spectrum.task_ids = l2.task_ids + l3.task_ids # type: ignore[attr-defined] all_spectra.append(total_spectrum) except ValueError as e: warnings.warn(f"Warning during spectral merging in XASDoC: {e}") # We don't have L2,3 EXAFS yet so don't have any merging # Site-weighted averaging spectra_to_average = [ list(group) for _, group in groupby( sorted( all_spectra, key=lambda x: (x.absorbing_element, x.edge, x.spectrum_type), ), key=lambda x: (x.absorbing_element, x.edge, x.spectrum_type), ) ] for relevant_spectra in spectra_to_average: if len(relevant_spectra) > 0 and not _is_missing_sites(relevant_spectra): if len(relevant_spectra) > 1: try: avg_spectrum = site_weighted_spectrum( relevant_spectra, num_samples=num_samples ) avg_spectrum.task_ids = [ # type: ignore[attr-defined] id for spectrum in relevant_spectra for id in spectrum.task_ids ] avg_spectrum.last_updated = max( # type: ignore[attr-defined, type-var] [spectrum.last_updated for spectrum in relevant_spectra] ) averaged_spectra.append(avg_spectrum) except ValueError as e: warnings.warn( f"Warning during site-weighted averaging in XASDoC: {e}" ) else: averaged_spectra.append(relevant_spectra[0]) spectra_docs = [] for spectrum in averaged_spectra: doc = XASDoc.from_spectrum( xas_spectrum=spectrum, material_id=material_id, task_ids=spectrum.task_ids, last_updated=spectrum.last_updated, ) spectra_docs.append(doc) return spectra_docs def _is_missing_sites(spectra: list[XAS]): """ Determines if the collection of spectra are missing any indicies for the given element """ structure = spectra[0].structure element = spectra[0].absorbing_element.symbol # Find missing symmeterically inequivalent sites symm_sites = SymmSites(structure) absorption_indicies = {spectrum.absorbing_index for spectrum in spectra} missing_site_spectra_indicies = ( set(structure.indices_from_symbol(element)) - absorption_indicies ) for site_index in absorption_indicies: missing_site_spectra_indicies -= set( symm_sites.get_equivalent_site_indices(site_index) ) return len(missing_site_spectra_indicies) != 0 class SymmSites: """ Wrapper to get equivalent site indicies from SpacegroupAnalyzer """ def __init__(self, structure): self.structure = structure sa = SpacegroupAnalyzer(self.structure) symm_data = sa.get_symmetry_dataset() # equivalency mapping for the structure # i'th site in the input structure equivalent to eq_atoms[i]'th site self.eq_atoms = symm_data["equivalent_atoms"] def get_equivalent_site_indices(self, i): """ Site indices in the structure that are equivalent to the given site i. """ rv = np.argwhere(self.eq_atoms == self.eq_atoms[i]).squeeze().tolist() if isinstance(rv, int): rv = [rv] return rv ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8292716 emmet_core-0.87.2/emmet/core/xrd.py0000644000175100017510000000705715240717720016621 0ustar00runnerrunnerfrom __future__ import annotations from typing import TYPE_CHECKING import numpy as np from pydantic import Field, model_validator from emmet.core.io.pymatgen import WAVELENGTHS, XRDCalculator, Structure, Element from emmet.core.spectrum import SpectrumDoc from emmet.core.types.enums import ValueEnum if TYPE_CHECKING: from emmet.core.types.typing import IdentifierType from emmet.core.types.pymatgen_types.diffraction_pattern_adapter import ( DiffractionPatternType, ) from emmet.core.types.pymatgen_types.element_adapter import ElementType class Edge(ValueEnum): K_Alpha = "Ka" K_Alpha1 = "Ka1" K_Alpha2 = "Ka2" K_Beta = "Kb" K_Beta1 = "Kb1" K_Beta2 = "Kb2" class XRDDoc(SpectrumDoc): """ Document describing a XRD Diffraction Pattern """ spectrum_name: str = "XRD" spectrum: DiffractionPatternType # type: ignore[valid-type] min_two_theta: float max_two_theta: float wavelength: float = Field(..., description="Wavelength for the diffraction source.") target: ElementType | None = Field( None, description="Target element for the diffraction source." ) edge: Edge | None = Field( None, description="Atomic edge for the diffraction source." ) @model_validator(mode="before") @classmethod def get_target_and_edge(cls, values: dict): # Only do this if neither target not edge is defined if "target" not in values and "edge" not in values: try: pymatgen_wavelength = next( k for k, v in WAVELENGTHS.items() if np.allclose(values["wavelength"], v) ) values["target"] = pymatgen_wavelength[:2] values["edge"] = pymatgen_wavelength[2:] except Exception: return values return values @classmethod def from_structure( # type: ignore[override] cls, spectrum_id: str, structure: Structure, wavelength: float, material_id: IdentifierType | None = None, min_two_theta=0, max_two_theta=180, symprec=0.1, **kwargs, ) -> "XRDDoc": calc = XRDCalculator(wavelength=wavelength, symprec=symprec) pattern = calc.get_pattern( structure, two_theta_range=(min_two_theta, max_two_theta) ) return super().from_structure( material_id=material_id, spectrum_id=spectrum_id, meta_structure=structure, spectrum=pattern, wavelength=wavelength, min_two_theta=min_two_theta, max_two_theta=max_two_theta, **kwargs, ) @classmethod def from_target( cls, structure: Structure, target: Element, edge: Edge, material_id: IdentifierType | None = None, min_two_theta=0, max_two_theta=180, symprec=0.1, **kwargs, ) -> "XRDDoc": if f"{target}{edge}" not in WAVELENGTHS: raise ValueError(f"{target}{edge} not in pymatgen wavelengths dictionary") wavelength = WAVELENGTHS[f"{target}{edge}"] spectrum_id = f"{material_id}-{target}{edge}" return cls.from_structure( material_id=material_id, spectrum_id=spectrum_id, structure=structure, wavelength=wavelength, target=target, edge=edge, min_two_theta=min_two_theta, max_two_theta=max_two_theta, symprec=symprec, **kwargs, ) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011028.9860497 emmet_core-0.87.2/emmet_core.egg-info/0000755000175100017510000000000015240717725017220 5ustar00runnerrunner././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011028.9062207 emmet_core-0.87.2/emmet_core.egg-info/PKG-INFO0000644000175100017510000000401615240717725020316 0ustar00runnerrunnerMetadata-Version: 2.4 Name: emmet-core Version: 0.87.2 Summary: Core Emmet Library Author-email: The Materials Project License-Expression: BSD-3-Clause-LBNL Project-URL: Homepage, https://github.com/materialsproject/emmet Project-URL: Documentation, https://materialsproject.github.io/emmet/ Requires-Python: >=3.11 Requires-Dist: pymatgen>=2024.6.10 Requires-Dist: monty>=2024.2.2 Requires-Dist: pydantic>=2.12 Requires-Dist: pydantic-settings>=2.0 Requires-Dist: pymatgen-io-validation>=0.1.1 Requires-Dist: pybtex~=0.24 Requires-Dist: typing-extensions>=3.7 Requires-Dist: blake3 Requires-Dist: inflect Requires-Dist: pubchempy Provides-Extra: all Requires-Dist: seekpath>=2.0.1; extra == "all" Requires-Dist: pymatgen-analysis-defects>=2024.7.18; extra == "all" Requires-Dist: pymatgen-analysis-diffusion>=2024.7.15; extra == "all" Requires-Dist: pymatgen-core<2026.7.27; extra == "all" Requires-Dist: pymatgen-analysis-alloys>=0.0.6; extra == "all" Requires-Dist: pyarrow; extra == "all" Requires-Dist: pymongo; extra == "all" Requires-Dist: pycodcif; extra == "all" Requires-Dist: lobsterpy; extra == "all" Requires-Dist: phonopy>=3.0.0; extra == "all" Provides-Extra: test Requires-Dist: pre-commit; extra == "test" Requires-Dist: pytest; extra == "test" Requires-Dist: pytest-cov; extra == "test" Requires-Dist: pytest-xdist; extra == "test" Requires-Dist: pycodestyle; extra == "test" Requires-Dist: pydocstyle; extra == "test" Requires-Dist: flake8; extra == "test" Requires-Dist: mypy; extra == "test" Requires-Dist: mypy-extensions; extra == "test" Requires-Dist: types-setuptools; extra == "test" Requires-Dist: types-requests; extra == "test" Requires-Dist: wincertstore; extra == "test" Requires-Dist: custodian; extra == "test" Provides-Extra: docs Requires-Dist: sphinx<9.0.0; extra == "docs" Requires-Dist: sphinx-rtd-theme>=3.0.2; extra == "docs" Requires-Dist: sphinx-markdown-builder>=0.6.8; extra == "docs" Requires-Dist: sphinx-autodoc-typehints; extra == "docs" Requires-Dist: myst-parser; extra == "docs" ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011028.9395669 emmet_core-0.87.2/emmet_core.egg-info/SOURCES.txt0000644000175100017510000002202215240717725021102 0ustar00runnerrunnerpyproject.toml dev_scripts/gen_api_field_test.py dev_scripts/generate_enums.py dev_scripts/generate_identifier_exclude_list.py emmet/core/__init__.py emmet/core/_forbidden_alpha_id.py emmet/core/_general_store.py emmet/core/_messages.py emmet/core/_user_settings.py emmet/core/absorption.py emmet/core/alloys.py emmet/core/arrow.py emmet/core/band_theory.py emmet/core/base.py emmet/core/bonds.py emmet/core/charge_density.py emmet/core/chemenv.py emmet/core/corrected_entries.py emmet/core/defect.py emmet/core/disorder.py emmet/core/dois.py emmet/core/elasticity.py emmet/core/elasticity_legacy.py emmet/core/electrode.py emmet/core/electronic_structure.py emmet/core/eos.py emmet/core/fermi.py emmet/core/find_structure.py emmet/core/formula_autocomplete.py emmet/core/grain_boundary.py emmet/core/lobster.py emmet/core/magnetism.py emmet/core/material.py emmet/core/material_property.py emmet/core/math.py emmet/core/ml.py emmet/core/molecules_jcesr.py emmet/core/mpcomplete.py emmet/core/mpid.py emmet/core/mpid_ext.py emmet/core/neb.py emmet/core/optimade.py emmet/core/oxidation_states.py emmet/core/phonon.py emmet/core/polar.py emmet/core/provenance.py emmet/core/py.typed emmet/core/qc_tasks.py emmet/core/robocrys.py emmet/core/settings.py emmet/core/similarity.py emmet/core/spectrum.py emmet/core/structure.py emmet/core/structure_group.py emmet/core/substrates.py emmet/core/summary.py emmet/core/surface_properties.py emmet/core/symmetry.py emmet/core/task.py emmet/core/tasks.py emmet/core/testing_utils.py emmet/core/thermo.py emmet/core/trajectory.py emmet/core/utils.py emmet/core/xas.py emmet/core/xrd.py emmet/core/connectors/__init__.py emmet/core/connectors/analysis.py emmet/core/connectors/icsd/__init__.py emmet/core/connectors/icsd/client.py emmet/core/connectors/icsd/enums.py emmet/core/connectors/icsd/schemas.py emmet/core/connectors/icsd/settings.py emmet/core/featurization/__init__.py emmet/core/featurization/featurizers.py emmet/core/featurization/robocrys/__init__.py emmet/core/featurization/robocrys/adapter.py emmet/core/featurization/robocrys/util.py emmet/core/featurization/robocrys/condense/__init__.py emmet/core/featurization/robocrys/condense/component.py emmet/core/featurization/robocrys/condense/condenser.py emmet/core/featurization/robocrys/condense/fingerprint.py emmet/core/featurization/robocrys/condense/formula_aliases.json.gz emmet/core/featurization/robocrys/condense/mineral.py emmet/core/featurization/robocrys/condense/mineral_db.json.gz emmet/core/featurization/robocrys/condense/molecule.py emmet/core/featurization/robocrys/condense/molecule_db.json.gz emmet/core/featurization/robocrys/condense/site.py emmet/core/featurization/robocrys/describe/__init__.py emmet/core/featurization/robocrys/describe/adapter.py emmet/core/featurization/robocrys/describe/describer.py emmet/core/feff/__init__.py emmet/core/feff/task.py emmet/core/io/__init__.py emmet/core/io/pymatgen.py emmet/core/mobility/__init__.py emmet/core/mobility/migrationgraph.py emmet/core/molecules/__init__.py emmet/core/molecules/atomic.py emmet/core/molecules/bonds.py emmet/core/molecules/electric.py emmet/core/molecules/metal_binding.py emmet/core/molecules/molecule_property.py emmet/core/molecules/orbitals.py emmet/core/molecules/redox.py emmet/core/molecules/summary.py emmet/core/molecules/thermo.py emmet/core/molecules/trajectory.py emmet/core/molecules/vibration.py emmet/core/openff/__init__.py emmet/core/openff/tasks.py emmet/core/openmm/__init__.py emmet/core/openmm/calculations.py emmet/core/openmm/tasks.py emmet/core/qchem/__init__.py emmet/core/qchem/calculation.py emmet/core/qchem/molecule.py emmet/core/qchem/task.py emmet/core/qchem/calc_types/__init__.py emmet/core/qchem/calc_types/calc_types.py emmet/core/qchem/calc_types/calc_types.yaml emmet/core/qchem/calc_types/em_utils.py emmet/core/qchem/calc_types/enums.py emmet/core/qchem/calc_types/utils.py emmet/core/synthesis/__init__.py emmet/core/synthesis/core.py emmet/core/synthesis/materials.py emmet/core/synthesis/operations.py emmet/core/synthesis/reaction.py emmet/core/types/__init__.py emmet/core/types/electronic_structure.py emmet/core/types/enums.py emmet/core/types/typing.py emmet/core/types/pymatgen_types/__init__.py emmet/core/types/pymatgen_types/alloy_adapter.py emmet/core/types/pymatgen_types/bader.py emmet/core/types/pymatgen_types/balanced_reaction_adapter.py emmet/core/types/pymatgen_types/bandstructure_symm_line_adapter.py emmet/core/types/pymatgen_types/compatibility_adapter.py emmet/core/types/pymatgen_types/composition_adapter.py emmet/core/types/pymatgen_types/computed_entries_adapter.py emmet/core/types/pymatgen_types/defect_adapter.py emmet/core/types/pymatgen_types/diffraction_pattern_adapter.py emmet/core/types/pymatgen_types/dos_adapter.py emmet/core/types/pymatgen_types/electrode_adapter.py emmet/core/types/pymatgen_types/element_adapter.py emmet/core/types/pymatgen_types/grain_boundary_adapter.py emmet/core/types/pymatgen_types/ir_dielectric_tensor_adapter.py 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emmet/core/vasp/utils.py emmet/core/vasp/validation.py emmet/core/vasp/validation_legacy.py emmet/core/vasp/calc_types/__init__.py emmet/core/vasp/calc_types/calc_types.yaml emmet/core/vasp/calc_types/enums.py emmet/core/vasp/calc_types/utils.py emmet_core.egg-info/PKG-INFO emmet_core.egg-info/SOURCES.txt emmet_core.egg-info/dependency_links.txt emmet_core.egg-info/requires.txt emmet_core.egg-info/scm_file_list.json emmet_core.egg-info/scm_version.json emmet_core.egg-info/top_level.txt requirements/deployment.txt requirements/ubuntu-latest_py3.11.txt requirements/ubuntu-latest_py3.11_extras.txt requirements/ubuntu-latest_py3.12.txt requirements/ubuntu-latest_py3.12_extras.txt requirements/ubuntu-latest_py3.13.txt requirements/ubuntu-latest_py3.13_extras.txt requirements/ubuntu-latest_py3.14.txt requirements/ubuntu-latest_py3.14_extras.txt tests/__init__.py tests/conftest.py tests/conftest_qchem.py tests/test_absorption.py tests/test_arrow.py tests/test_band_theory.py tests/test_calculation.py tests/test_chemenv.py tests/test_defects.py tests/test_elasticity.py tests/test_electrodes.py tests/test_electronic_structure.py tests/test_lobster.py tests/test_magnetism.py tests/test_ml.py tests/test_model_fields.py tests/test_molecule_metadata.py tests/test_mpid.py tests/test_neb.py tests/test_optimade.py tests/test_oxidation_states.py tests/test_phonon.py tests/test_polar.py tests/test_provenance.py tests/test_qc_task.py tests/test_robocrys.py tests/test_settings.py tests/test_similarity.py tests/test_structure_group.py tests/test_structure_metadata.py tests/test_symmetry.py tests/test_task.py tests/test_thermo.py tests/test_trajectory.py tests/test_utils.py tests/test_xas.py tests/test_xrd.py tests/featurization/__init__.py tests/featurization/test_featurizers.py tests/featurization/robocrys/__init__.py tests/featurization/robocrys/conftest.py tests/featurization/robocrys/test_adapter.py tests/featurization/robocrys/test_util.py 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tests/qchem/__init__.py tests/qchem/test_calc_types.py tests/qchem/test_molecules.py tests/qchem/test_qchem.py tests/types/test_enums.py tests/types/test_typing.py tests/vasp/conftest.py tests/vasp/test_calc_types.py tests/vasp/test_materials.py tests/vasp/test_utils.py tests/vasp/test_vasp.py././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011028.9072614 emmet_core-0.87.2/emmet_core.egg-info/dependency_links.txt0000644000175100017510000000000115240717725023266 0ustar00runnerrunner ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011028.9084203 emmet_core-0.87.2/emmet_core.egg-info/requires.txt0000644000175100017510000000117715240717725021626 0ustar00runnerrunnerpymatgen>=2024.6.10 monty>=2024.2.2 pydantic>=2.12 pydantic-settings>=2.0 pymatgen-io-validation>=0.1.1 pybtex~=0.24 typing-extensions>=3.7 blake3 inflect pubchempy [all] seekpath>=2.0.1 pymatgen-analysis-defects>=2024.7.18 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"emmet-core" dynamic = ["version"] requires-python = ">=3.11" description="Core Emmet Library" license = "BSD-3-Clause-LBNL" authors = [ {name = "The Materials Project", email="feedback@materialsproject.org"}, ] dependencies = [ "pymatgen>=2024.6.10", "monty>=2024.2.2", "pydantic>=2.12", "pydantic-settings>=2.0", "pymatgen-io-validation>=0.1.1", "pybtex~=0.24", "typing-extensions>=3.7", "blake3", "inflect", "pubchempy", ] [project.urls] Homepage = "https://github.com/materialsproject/emmet" Documentation = "https://materialsproject.github.io/emmet/" [project.optional-dependencies] all = [ "seekpath>=2.0.1", "pymatgen-analysis-defects>=2024.7.18", "pymatgen-analysis-diffusion>=2024.7.15", "pymatgen-core<2026.7.27", # pymatgen-analysis-diffusion behind on deprecated functions "pymatgen-analysis-alloys>=0.0.6", "pyarrow", "pymongo", "pycodcif", "lobsterpy", "phonopy>=3.0.0", ] test = [ "pre-commit", "pytest", "pytest-cov", "pytest-xdist", "pycodestyle", "pydocstyle", "flake8", "mypy", "mypy-extensions", "types-setuptools", "types-requests", "wincertstore", "custodian", ] docs = [ "sphinx<9.0.0", "sphinx-rtd-theme>=3.0.2", "sphinx-markdown-builder>=0.6.8", "sphinx-autodoc-typehints", "myst-parser", ] ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011028.9875283 emmet_core-0.87.2/requirements/0000755000175100017510000000000015240717725016132 5ustar00runnerrunner././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8292716 emmet_core-0.87.2/requirements/deployment.txt0000644000175100017510000000642215240717720021052 0ustar00runnerrunner# # This file is autogenerated by pip-compile with Python 3.11 # by the following command: # # pip-compile --output-file=emmet/emmet-core/requirements/deployment.txt emmet/emmet-core/pyproject.toml python/requirements.txt # annotated-types==0.8.0 # via pydantic bibtexparser==1.4.4 # via pymatgen-core blake3==1.0.9 # via emmet-core (emmet/emmet-core/pyproject.toml) certifi==2026.7.22 # via requests charset-normalizer==3.5.1 # via requests contourpy==1.3.3 # via matplotlib cycler==0.12.1 # via matplotlib fonttools==4.63.0 # via matplotlib idna==3.18 # via requests inflect==7.5.0 # via emmet-core (emmet/emmet-core/pyproject.toml) joblib==1.5.3 # via pymatgen-core kiwisolver==1.5.0 # via matplotlib latexcodec==3.0.1 # via pybtex lxml==6.1.1 # via pymatgen-core matplotlib==3.11.1 # via # -r python/requirements.txt # pymatgen-core monty==2026.7.16 # via # emmet-core (emmet/emmet-core/pyproject.toml) # pymatgen-core more-itertools==11.1.0 # via inflect mpmath==1.3.0 # via sympy narwhals==2.24.0 # via plotly networkx==3.6.1 # via pymatgen-core numpy==2.4.6 # via # -r python/requirements.txt # contourpy # matplotlib # monty # pandas # pymatgen-core # pymatgen-io-validation # scipy # spglib orjson==3.12.0 # via pymatgen-core packaging==26.3 # via # matplotlib # plotly palettable==3.3.3 # via pymatgen-core pandas==3.0.5 # via # -r python/requirements.txt # pymatgen-core pillow==12.3.0 # via matplotlib plotly==6.9.0 # via pymatgen-core pubchempy==1.0.5 # via emmet-core (emmet/emmet-core/pyproject.toml) pybtex==0.26.1 # via emmet-core (emmet/emmet-core/pyproject.toml) pydantic==2.13.4 # via # emmet-core (emmet/emmet-core/pyproject.toml) # pydantic-settings # pymatgen-io-validation pydantic-core==2.46.4 # via pydantic pydantic-settings==2.15.0 # via # emmet-core (emmet/emmet-core/pyproject.toml) # pymatgen-io-validation pymatgen==2026.5.4 # via emmet-core (emmet/emmet-core/pyproject.toml) pymatgen-core==2026.8.13 # via # pymatgen # pymatgen-io-validation pymatgen-io-validation==0.1.4 # via emmet-core (emmet/emmet-core/pyproject.toml) pyparsing==3.3.2 # via # bibtexparser # matplotlib python-dateutil==2.9.0.post0 # via # matplotlib # pandas python-dotenv==1.2.3 # via pydantic-settings pyyaml==6.0.3 # via pybtex requests==2.34.2 # via # pymatgen-core # pymatgen-io-validation ruamel-yaml==0.19.1 # via monty scipy==1.17.1 # via # -r python/requirements.txt # pymatgen-core six==1.17.0 # via python-dateutil spglib==2.7.0 # via pymatgen-core sympy==1.14.0 # via pymatgen-core tabulate==0.10.0 # via pymatgen-core tqdm==4.70.0 # via pymatgen-core typeguard==4.6.0 # via inflect typing-extensions==4.16.0 # via # blake3 # emmet-core (emmet/emmet-core/pyproject.toml) # pydantic # pydantic-core # spglib # typeguard # typing-inspection typing-inspection==0.4.4 # via # pydantic # pydantic-settings uncertainties==3.2.3 # via pymatgen-core urllib3==2.7.0 # via requests ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8301334 emmet_core-0.87.2/requirements/ubuntu-latest_py3.11.txt0000644000175100017510000000560515240717720022523 0ustar00runnerrunner# # This file is autogenerated by pip-compile with Python 3.11 # by the following command: # # pip-compile --output-file=requirements/ubuntu-latest_py3.11.txt # annotated-types==0.8.0 # via pydantic bibtexparser==1.4.4 # via pymatgen-core blake3==1.0.9 # via emmet-core (pyproject.toml) certifi==2026.7.22 # via requests charset-normalizer==3.5.1 # via requests contourpy==1.3.3 # via matplotlib cycler==0.12.1 # via matplotlib fonttools==4.63.0 # via matplotlib idna==3.18 # via requests inflect==7.5.0 # via emmet-core (pyproject.toml) joblib==1.5.3 # via pymatgen-core kiwisolver==1.5.0 # via matplotlib latexcodec==3.0.1 # via pybtex lxml==6.1.1 # via pymatgen-core matplotlib==3.11.1 # via pymatgen-core monty==2026.7.16 # via # emmet-core (pyproject.toml) # pymatgen-core more-itertools==11.1.0 # via inflect mpmath==1.3.0 # via sympy narwhals==2.24.0 # via plotly networkx==3.6.1 # via pymatgen-core numpy==2.4.6 # via # contourpy # matplotlib # monty # pandas # pymatgen-core # pymatgen-io-validation # scipy # spglib orjson==3.12.0 # via pymatgen-core packaging==26.3 # via # matplotlib # plotly palettable==3.3.3 # via pymatgen-core pandas==3.0.5 # via pymatgen-core pillow==12.3.0 # via matplotlib plotly==6.9.0 # via pymatgen-core pubchempy==1.0.5 # via emmet-core (pyproject.toml) pybtex==0.26.1 # via emmet-core (pyproject.toml) pydantic==2.13.4 # via # emmet-core (pyproject.toml) # pydantic-settings # pymatgen-io-validation pydantic-core==2.46.4 # via pydantic pydantic-settings==2.15.0 # via # emmet-core (pyproject.toml) # pymatgen-io-validation pymatgen==2026.5.4 # via emmet-core (pyproject.toml) pymatgen-core==2026.8.13 # via # pymatgen # pymatgen-io-validation pymatgen-io-validation==0.1.4 # via emmet-core (pyproject.toml) pyparsing==3.3.2 # via # bibtexparser # matplotlib python-dateutil==2.9.0.post0 # via # matplotlib # pandas python-dotenv==1.2.3 # via pydantic-settings pyyaml==6.0.3 # via pybtex requests==2.34.2 # via # pymatgen-core # pymatgen-io-validation ruamel-yaml==0.19.1 # via monty scipy==1.17.1 # via pymatgen-core six==1.17.0 # via python-dateutil spglib==2.7.0 # via pymatgen-core sympy==1.14.0 # via pymatgen-core tabulate==0.10.0 # via pymatgen-core tqdm==4.70.0 # via pymatgen-core typeguard==4.6.0 # via inflect typing-extensions==4.16.0 # via # blake3 # emmet-core (pyproject.toml) # pydantic # pydantic-core # spglib # typeguard # typing-inspection typing-inspection==0.4.4 # via # pydantic # pydantic-settings uncertainties==3.2.3 # via pymatgen-core urllib3==2.7.0 # via requests ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8301334 emmet_core-0.87.2/requirements/ubuntu-latest_py3.11_extras.txt0000644000175100017510000001763715240717720024121 0ustar00runnerrunner# # This file is autogenerated by pip-compile with Python 3.11 # by the following command: # # pip-compile --all-extras --output-file=requirements/ubuntu-latest_py3.11_extras.txt # alabaster==1.0.0 # via sphinx annotated-types==0.8.0 # via pydantic ase==3.29.0 # via pymatgen-analysis-diffusion ast-serialize==0.8.0 # via mypy babel==2.18.0 # via sphinx bibtexparser==1.4.4 # via pymatgen-core blake3==1.0.9 # via emmet-core (pyproject.toml) certifi==2026.7.22 # via requests cfgv==3.5.0 # via pre-commit charset-normalizer==3.5.1 # via requests contourpy==1.3.3 # via matplotlib coverage[toml]==7.15.4 # via pytest-cov custodian==2025.12.14 # via emmet-core (pyproject.toml) cycler==0.12.1 # via matplotlib distlib==0.4.3 # via virtualenv dnspython==2.8.0 # via pymongo docutils==0.21.2 # via # myst-parser # sphinx # sphinx-markdown-builder # sphinx-rtd-theme execnet==2.1.2 # via pytest-xdist filelock==3.32.3 # via # python-discovery # virtualenv flake8==7.3.0 # via emmet-core (pyproject.toml) fonttools==4.63.0 # via matplotlib h5py==3.16.0 # via phonopy identify==2.6.19 # via pre-commit idna==3.18 # via requests imageio==2.37.4 # via scikit-image imagesize==2.0.0 # via sphinx inflect==7.5.0 # via emmet-core (pyproject.toml) iniconfig==2.3.0 # via pytest jinja2==3.1.6 # via # myst-parser # sphinx joblib==1.5.3 # via # pymatgen-analysis-diffusion # pymatgen-core kiwisolver==1.5.0 # via matplotlib latexcodec==3.0.1 # via pybtex lazy-loader==0.5 # via scikit-image librt==0.15.0 # via mypy lobsterpy==0.6.1 # via emmet-core (pyproject.toml) lxml==6.1.1 # via pymatgen-core markdown-it-py==4.2.0 # via # mdit-py-plugins # myst-parser markupsafe==3.0.3 # via jinja2 matplotlib==3.11.1 # via # ase # phonopy # pymatgen-core # seaborn mccabe==0.7.0 # via flake8 mdit-py-plugins==0.6.1 # via myst-parser mdurl==0.1.2 # via markdown-it-py monty==2026.7.16 # via # custodian # emmet-core (pyproject.toml) # pymatgen-core more-itertools==11.1.0 # via inflect mp-pyrho==0.5.1 # via pymatgen-analysis-defects mpmath==1.3.0 # via sympy mypy==2.3.1 # via emmet-core (pyproject.toml) mypy-extensions==1.1.0 # via # emmet-core (pyproject.toml) # mypy myst-parser==5.1.0 # via emmet-core (pyproject.toml) narwhals==2.24.0 # via plotly networkx==3.6.1 # via # pymatgen-core # scikit-image nodeenv==1.10.0 # via pre-commit numpy==2.4.6 # via # ase # contourpy # h5py # imageio # lobsterpy # matplotlib # monty # pandas # phonopy # pymatgen-analysis-defects # pymatgen-analysis-diffusion # pymatgen-core # pymatgen-io-validation # scikit-image # scipy # seaborn # seekpath # shapely # spglib # symfc # tifffile orjson==3.12.0 # via pymatgen-core packaging==26.3 # via # lazy-loader # matplotlib # plotly # pytest # scikit-image # sphinx palettable==3.3.3 # via pymatgen-core pandas==3.0.5 # via # pymatgen-core # seaborn pathspec==1.1.1 # via mypy phonopy==4.4.0 # via emmet-core (pyproject.toml) phonors==0.3.0 # via phonopy pillow==12.3.0 # via # imageio # matplotlib # scikit-image platformdirs==4.11.3 # via virtualenv plotly==6.9.0 # via pymatgen-core pluggy==1.6.0 # via # pytest # pytest-cov pre-commit==4.6.2 # via emmet-core (pyproject.toml) psutil==7.2.2 # via custodian pubchempy==1.0.5 # via emmet-core (pyproject.toml) pyarrow==25.0.1 # via emmet-core (pyproject.toml) pybtex==0.26.1 # via emmet-core (pyproject.toml) pycodcif==3.0.1 # via emmet-core (pyproject.toml) pycodestyle==2.14.0 # via # emmet-core (pyproject.toml) # flake8 pydantic==2.13.4 # via # emmet-core (pyproject.toml) # pydantic-settings # pymatgen-io-validation pydantic-core==2.46.4 # via pydantic pydantic-settings==2.15.0 # via # emmet-core (pyproject.toml) # pymatgen-io-validation pydocstyle==6.3.0 # via emmet-core (pyproject.toml) pyflakes==3.4.0 # via flake8 pygments==2.21.0 # via # pytest # sphinx pymatgen==2026.5.4 # via # emmet-core (pyproject.toml) # lobsterpy # mp-pyrho # pymatgen-analysis-alloys # pymatgen-analysis-defects # pymatgen-analysis-diffusion pymatgen-analysis-alloys==0.0.9 # via emmet-core (pyproject.toml) pymatgen-analysis-defects==2026.3.20 # via emmet-core (pyproject.toml) pymatgen-analysis-diffusion==2025.11.15 # via emmet-core (pyproject.toml) pymatgen-core==2026.7.16 # via # emmet-core (pyproject.toml) # pymatgen # pymatgen-io-validation pymatgen-io-validation==0.1.4 # via emmet-core (pyproject.toml) pymongo==4.17.0 # via emmet-core (pyproject.toml) pyparsing==3.3.2 # via # bibtexparser # matplotlib pytest==9.1.1 # via # emmet-core (pyproject.toml) # pytest-cov # pytest-xdist pytest-cov==7.1.0 # via emmet-core (pyproject.toml) pytest-xdist==3.8.0 # via emmet-core (pyproject.toml) python-dateutil==2.9.0.post0 # via # matplotlib # pandas python-discovery==1.5.2 # via virtualenv python-dotenv==1.2.3 # via pydantic-settings pyyaml==6.0.3 # via # myst-parser # phonopy # pre-commit # pybtex requests==2.34.2 # via # pymatgen-core # pymatgen-io-validation # sphinx roman-numerals==4.1.0 # via roman-numerals-py roman-numerals-py==4.1.0 # via sphinx ruamel-yaml==0.19.1 # via # custodian # monty scikit-image==0.26.0 # via pymatgen-analysis-defects scipy==1.17.1 # via # ase # pymatgen-core # scikit-image # symfc seaborn==0.13.2 # via pymatgen-analysis-diffusion seekpath==2.2.1 # via emmet-core (pyproject.toml) shapely==2.1.2 # via pymatgen-analysis-alloys six==1.17.0 # via python-dateutil snowballstemmer==3.1.1 # via # pydocstyle # sphinx spglib==2.7.0 # via # phonopy # pymatgen-core # seekpath # symfc sphinx==8.2.3 # via # emmet-core (pyproject.toml) # myst-parser # sphinx-autodoc-typehints # sphinx-markdown-builder # sphinx-rtd-theme # sphinxcontrib-jquery sphinx-autodoc-typehints==3.5.2 # via emmet-core (pyproject.toml) sphinx-markdown-builder==0.6.11 # via emmet-core (pyproject.toml) sphinx-rtd-theme==3.1.0 # via emmet-core (pyproject.toml) sphinxcontrib-applehelp==2.0.0 # via sphinx sphinxcontrib-devhelp==2.0.0 # via sphinx sphinxcontrib-htmlhelp==2.1.0 # via sphinx sphinxcontrib-jquery==4.1 # via sphinx-rtd-theme sphinxcontrib-jsmath==1.0.1 # via sphinx sphinxcontrib-qthelp==2.0.0 # via sphinx sphinxcontrib-serializinghtml==2.0.0 # via sphinx symfc==1.7.3 # via phonopy sympy==1.14.0 # via pymatgen-core tabulate==0.10.0 # via # pymatgen-core # sphinx-markdown-builder tifffile==2026.3.3 # via scikit-image tqdm==4.70.0 # via pymatgen-core typeguard==4.6.0 # via inflect types-requests==2.33.0.20260712 # via emmet-core (pyproject.toml) types-setuptools==84.0.0.20260812 # via emmet-core (pyproject.toml) typing-extensions==4.16.0 # via # ase # blake3 # emmet-core (pyproject.toml) # mypy # pydantic # pydantic-core # spglib # typeguard # typing-inspection typing-inspection==0.4.4 # via # pydantic # pydantic-settings uncertainties==3.2.3 # via pymatgen-core urllib3==2.7.0 # via # requests # types-requests virtualenv==21.7.4 # via pre-commit wincertstore==0.2.1 # via emmet-core (pyproject.toml) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8301334 emmet_core-0.87.2/requirements/ubuntu-latest_py3.12.txt0000644000175100017510000000556615240717720022532 0ustar00runnerrunner# # This file is autogenerated by pip-compile with Python 3.12 # by the following command: # # pip-compile --output-file=requirements/ubuntu-latest_py3.12.txt # annotated-types==0.8.0 # via pydantic bibtexparser==1.4.4 # via pymatgen-core blake3==1.0.9 # via emmet-core (pyproject.toml) certifi==2026.7.22 # via requests charset-normalizer==3.5.1 # via requests contourpy==1.3.3 # via matplotlib cycler==0.12.1 # via matplotlib fonttools==4.63.0 # via matplotlib idna==3.18 # via requests inflect==7.5.0 # via emmet-core (pyproject.toml) joblib==1.5.3 # via pymatgen-core kiwisolver==1.5.0 # via matplotlib latexcodec==3.0.1 # via pybtex lxml==6.1.1 # via pymatgen-core matplotlib==3.11.1 # via pymatgen-core monty==2026.7.16 # via # emmet-core (pyproject.toml) # pymatgen-core more-itertools==11.1.0 # via inflect mpmath==1.3.0 # via sympy narwhals==2.24.0 # via plotly networkx==3.6.1 # via pymatgen-core numpy==2.5.2 # via # contourpy # matplotlib # monty # pandas # pymatgen-core # pymatgen-io-validation # scipy # spglib orjson==3.12.0 # via pymatgen-core packaging==26.3 # via # matplotlib # plotly palettable==3.3.3 # via pymatgen-core pandas==3.0.5 # via pymatgen-core pillow==12.3.0 # via matplotlib plotly==6.9.0 # via pymatgen-core pubchempy==1.0.5 # via emmet-core (pyproject.toml) pybtex==0.26.1 # via emmet-core (pyproject.toml) pydantic==2.13.4 # via # emmet-core (pyproject.toml) # pydantic-settings # pymatgen-io-validation pydantic-core==2.46.4 # via pydantic pydantic-settings==2.15.0 # via # emmet-core (pyproject.toml) # pymatgen-io-validation pymatgen==2026.5.4 # via emmet-core (pyproject.toml) pymatgen-core==2026.8.13 # via # pymatgen # pymatgen-io-validation pymatgen-io-validation==0.1.4 # via emmet-core (pyproject.toml) pyparsing==3.3.2 # via # bibtexparser # matplotlib python-dateutil==2.9.0.post0 # via # matplotlib # pandas python-dotenv==1.2.3 # via pydantic-settings pyyaml==6.0.3 # via pybtex requests==2.34.2 # via # pymatgen-core # pymatgen-io-validation ruamel-yaml==0.19.1 # via monty scipy==1.18.0 # via pymatgen-core six==1.17.0 # via python-dateutil spglib==2.7.0 # via pymatgen-core sympy==1.14.0 # via pymatgen-core tabulate==0.10.0 # via pymatgen-core tqdm==4.70.0 # via pymatgen-core typeguard==4.6.0 # via inflect typing-extensions==4.16.0 # via # emmet-core (pyproject.toml) # pydantic # pydantic-core # spglib # typeguard # typing-inspection typing-inspection==0.4.4 # via # pydantic # pydantic-settings uncertainties==3.2.3 # via pymatgen-core urllib3==2.7.0 # via requests ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8301334 emmet_core-0.87.2/requirements/ubuntu-latest_py3.12_extras.txt0000644000175100017510000001762115240717720024113 0ustar00runnerrunner# # This file is autogenerated by pip-compile with Python 3.12 # by the following command: # # pip-compile --all-extras --output-file=requirements/ubuntu-latest_py3.12_extras.txt # alabaster==1.0.0 # via sphinx annotated-types==0.8.0 # via pydantic ase==3.29.0 # via pymatgen-analysis-diffusion ast-serialize==0.8.0 # via mypy babel==2.18.0 # via sphinx bibtexparser==1.4.4 # via pymatgen-core blake3==1.0.9 # via emmet-core (pyproject.toml) certifi==2026.7.22 # via requests cfgv==3.5.0 # via pre-commit charset-normalizer==3.5.1 # via requests contourpy==1.3.3 # via matplotlib coverage[toml]==7.15.4 # via pytest-cov custodian==2025.12.14 # via emmet-core (pyproject.toml) cycler==0.12.1 # via matplotlib distlib==0.4.3 # via virtualenv dnspython==2.8.0 # via pymongo docutils==0.21.2 # via # myst-parser # sphinx # sphinx-markdown-builder # sphinx-rtd-theme execnet==2.1.2 # via pytest-xdist filelock==3.32.3 # via # python-discovery # virtualenv flake8==7.3.0 # via emmet-core (pyproject.toml) fonttools==4.63.0 # via matplotlib h5py==3.16.0 # via phonopy identify==2.6.19 # via pre-commit idna==3.18 # via requests imageio==2.37.4 # via scikit-image imagesize==2.0.0 # via sphinx inflect==7.5.0 # via emmet-core (pyproject.toml) iniconfig==2.3.0 # via pytest jinja2==3.1.6 # via # myst-parser # sphinx joblib==1.5.3 # via # pymatgen-analysis-diffusion # pymatgen-core kiwisolver==1.5.0 # via matplotlib latexcodec==3.0.1 # via pybtex lazy-loader==0.5 # via scikit-image librt==0.15.0 # via mypy lobsterpy==0.6.1 # via emmet-core (pyproject.toml) lxml==6.1.1 # via pymatgen-core markdown-it-py==4.2.0 # via # mdit-py-plugins # myst-parser markupsafe==3.0.3 # via jinja2 matplotlib==3.11.1 # via # ase # phonopy # pymatgen-core # seaborn mccabe==0.7.0 # via flake8 mdit-py-plugins==0.6.1 # via myst-parser mdurl==0.1.2 # via markdown-it-py monty==2026.7.16 # via # custodian # emmet-core (pyproject.toml) # pymatgen-core more-itertools==11.1.0 # via inflect mp-pyrho==0.5.1 # via pymatgen-analysis-defects mpmath==1.3.0 # via sympy mypy==2.3.1 # via emmet-core (pyproject.toml) mypy-extensions==1.1.0 # via # emmet-core (pyproject.toml) # mypy myst-parser==5.1.0 # via emmet-core (pyproject.toml) narwhals==2.24.0 # via plotly networkx==3.6.1 # via # pymatgen-core # scikit-image nodeenv==1.10.0 # via pre-commit numpy==2.5.2 # via # ase # contourpy # h5py # imageio # lobsterpy # matplotlib # monty # pandas # phonopy # pymatgen-analysis-defects # pymatgen-analysis-diffusion # pymatgen-core # pymatgen-io-validation # scikit-image # scipy # seaborn # seekpath # shapely # spglib # symfc # tifffile orjson==3.12.0 # via pymatgen-core packaging==26.3 # via # lazy-loader # matplotlib # plotly # pytest # scikit-image # sphinx palettable==3.3.3 # via pymatgen-core pandas==3.0.5 # via # pymatgen-core # seaborn pathspec==1.1.1 # via mypy phonopy==4.4.0 # via emmet-core (pyproject.toml) phonors==0.3.0 # via phonopy pillow==12.3.0 # via # imageio # matplotlib # scikit-image platformdirs==4.11.3 # via virtualenv plotly==6.9.0 # via pymatgen-core pluggy==1.6.0 # via # pytest # pytest-cov pre-commit==4.6.2 # via emmet-core (pyproject.toml) psutil==7.2.2 # via custodian pubchempy==1.0.5 # via emmet-core (pyproject.toml) pyarrow==25.0.1 # via emmet-core (pyproject.toml) pybtex==0.26.1 # via emmet-core (pyproject.toml) pycodcif==3.0.1 # via emmet-core (pyproject.toml) pycodestyle==2.14.0 # via # emmet-core (pyproject.toml) # flake8 pydantic==2.13.4 # via # emmet-core (pyproject.toml) # pydantic-settings # pymatgen-io-validation pydantic-core==2.46.4 # via pydantic pydantic-settings==2.15.0 # via # emmet-core (pyproject.toml) # pymatgen-io-validation pydocstyle==6.3.0 # via emmet-core (pyproject.toml) pyflakes==3.4.0 # via flake8 pygments==2.21.0 # via # pytest # sphinx pymatgen==2026.5.4 # via # emmet-core (pyproject.toml) # lobsterpy # mp-pyrho # pymatgen-analysis-alloys # pymatgen-analysis-defects # pymatgen-analysis-diffusion pymatgen-analysis-alloys==0.0.9 # via emmet-core (pyproject.toml) pymatgen-analysis-defects==2026.3.20 # via emmet-core (pyproject.toml) pymatgen-analysis-diffusion==2025.11.15 # via emmet-core (pyproject.toml) pymatgen-core==2026.7.16 # via # emmet-core (pyproject.toml) # pymatgen # pymatgen-io-validation pymatgen-io-validation==0.1.4 # via emmet-core (pyproject.toml) pymongo==4.17.0 # via emmet-core (pyproject.toml) pyparsing==3.3.2 # via # bibtexparser # matplotlib pytest==9.1.1 # via # emmet-core (pyproject.toml) # pytest-cov # pytest-xdist pytest-cov==7.1.0 # via emmet-core (pyproject.toml) pytest-xdist==3.8.0 # via emmet-core (pyproject.toml) python-dateutil==2.9.0.post0 # via # matplotlib # pandas python-discovery==1.5.2 # via virtualenv python-dotenv==1.2.3 # via pydantic-settings pyyaml==6.0.3 # via # myst-parser # phonopy # pre-commit # pybtex requests==2.34.2 # via # pymatgen-core # pymatgen-io-validation # sphinx roman-numerals==4.1.0 # via roman-numerals-py roman-numerals-py==4.1.0 # via sphinx ruamel-yaml==0.19.1 # via # custodian # monty scikit-image==0.26.0 # via pymatgen-analysis-defects scipy==1.18.0 # via # ase # pymatgen-core # scikit-image # symfc seaborn==0.13.2 # via pymatgen-analysis-diffusion seekpath==2.2.1 # via emmet-core (pyproject.toml) shapely==2.1.2 # via pymatgen-analysis-alloys six==1.17.0 # via python-dateutil snowballstemmer==3.1.1 # via # pydocstyle # sphinx spglib==2.7.0 # via # phonopy # pymatgen-core # seekpath # symfc sphinx==8.2.3 # via # emmet-core (pyproject.toml) # myst-parser # sphinx-autodoc-typehints # sphinx-markdown-builder # sphinx-rtd-theme # sphinxcontrib-jquery sphinx-autodoc-typehints==3.5.2 # via emmet-core (pyproject.toml) sphinx-markdown-builder==0.6.11 # via emmet-core (pyproject.toml) sphinx-rtd-theme==3.1.0 # via emmet-core (pyproject.toml) sphinxcontrib-applehelp==2.0.0 # via sphinx sphinxcontrib-devhelp==2.0.0 # via sphinx sphinxcontrib-htmlhelp==2.1.0 # via sphinx sphinxcontrib-jquery==4.1 # via sphinx-rtd-theme sphinxcontrib-jsmath==1.0.1 # via sphinx sphinxcontrib-qthelp==2.0.0 # via sphinx sphinxcontrib-serializinghtml==2.0.0 # via sphinx symfc==1.7.3 # via phonopy sympy==1.14.0 # via pymatgen-core tabulate==0.10.0 # via # pymatgen-core # sphinx-markdown-builder tifffile==2026.8.16 # via scikit-image tqdm==4.70.0 # via pymatgen-core typeguard==4.6.0 # via inflect types-requests==2.33.0.20260712 # via emmet-core (pyproject.toml) types-setuptools==84.0.0.20260812 # via emmet-core (pyproject.toml) typing-extensions==4.16.0 # via # ase # emmet-core (pyproject.toml) # mypy # pydantic # pydantic-core # spglib # typeguard # typing-inspection typing-inspection==0.4.4 # via # pydantic # pydantic-settings uncertainties==3.2.3 # via pymatgen-core urllib3==2.7.0 # via # requests # types-requests virtualenv==21.7.4 # via pre-commit wincertstore==0.2.1 # via emmet-core (pyproject.toml) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8301334 emmet_core-0.87.2/requirements/ubuntu-latest_py3.13.txt0000644000175100017510000000554715240717720022532 0ustar00runnerrunner# # This file is autogenerated by pip-compile with Python 3.13 # by the following command: # # pip-compile --output-file=requirements/ubuntu-latest_py3.13.txt # annotated-types==0.8.0 # via pydantic bibtexparser==1.4.4 # via pymatgen-core blake3==1.0.9 # via emmet-core (pyproject.toml) certifi==2026.7.22 # via requests charset-normalizer==3.5.1 # via requests contourpy==1.3.3 # via matplotlib cycler==0.12.1 # via matplotlib fonttools==4.63.0 # via matplotlib idna==3.18 # via requests inflect==7.5.0 # via emmet-core (pyproject.toml) joblib==1.5.3 # via pymatgen-core kiwisolver==1.5.0 # via matplotlib latexcodec==3.0.1 # via pybtex lxml==6.1.1 # via pymatgen-core matplotlib==3.11.1 # via pymatgen-core monty==2026.7.16 # via # emmet-core (pyproject.toml) # pymatgen-core more-itertools==11.1.0 # via inflect mpmath==1.3.0 # via sympy narwhals==2.24.0 # via plotly networkx==3.6.1 # via pymatgen-core numpy==2.5.2 # via # contourpy # matplotlib # monty # pandas # pymatgen-core # pymatgen-io-validation # scipy # spglib orjson==3.12.0 # via pymatgen-core packaging==26.3 # via # matplotlib # plotly palettable==3.3.3 # via pymatgen-core pandas==3.0.5 # via pymatgen-core pillow==12.3.0 # via matplotlib plotly==6.9.0 # via pymatgen-core pubchempy==1.0.5 # via emmet-core (pyproject.toml) pybtex==0.26.1 # via emmet-core (pyproject.toml) pydantic==2.13.4 # via # emmet-core (pyproject.toml) # pydantic-settings # pymatgen-io-validation pydantic-core==2.46.4 # via pydantic pydantic-settings==2.15.0 # via # emmet-core (pyproject.toml) # pymatgen-io-validation pymatgen==2026.5.4 # via emmet-core (pyproject.toml) pymatgen-core==2026.8.13 # via # pymatgen # pymatgen-io-validation pymatgen-io-validation==0.1.4 # via emmet-core (pyproject.toml) pyparsing==3.3.2 # via # bibtexparser # matplotlib python-dateutil==2.9.0.post0 # via # matplotlib # pandas python-dotenv==1.2.3 # via pydantic-settings pyyaml==6.0.3 # via pybtex requests==2.34.2 # via # pymatgen-core # pymatgen-io-validation ruamel-yaml==0.19.1 # via monty scipy==1.18.0 # via pymatgen-core six==1.17.0 # via python-dateutil spglib==2.7.0 # via pymatgen-core sympy==1.14.0 # via pymatgen-core tabulate==0.10.0 # via pymatgen-core tqdm==4.70.0 # via pymatgen-core typeguard==4.6.0 # via inflect typing-extensions==4.16.0 # via # emmet-core (pyproject.toml) # pydantic # pydantic-core # typeguard # typing-inspection typing-inspection==0.4.4 # via # pydantic # pydantic-settings uncertainties==3.2.3 # via pymatgen-core urllib3==2.7.0 # via requests ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8301334 emmet_core-0.87.2/requirements/ubuntu-latest_py3.13_extras.txt0000644000175100017510000001760215240717720024113 0ustar00runnerrunner# # This file is autogenerated by pip-compile with Python 3.13 # by the following command: # # pip-compile --all-extras --output-file=requirements/ubuntu-latest_py3.13_extras.txt # alabaster==1.0.0 # via sphinx annotated-types==0.8.0 # via pydantic ase==3.29.0 # via pymatgen-analysis-diffusion ast-serialize==0.8.0 # via mypy babel==2.18.0 # via sphinx bibtexparser==1.4.4 # via pymatgen-core blake3==1.0.9 # via emmet-core (pyproject.toml) certifi==2026.7.22 # via requests cfgv==3.5.0 # via pre-commit charset-normalizer==3.5.1 # via requests contourpy==1.3.3 # via matplotlib coverage[toml]==7.15.4 # via pytest-cov custodian==2025.12.14 # via emmet-core (pyproject.toml) cycler==0.12.1 # via matplotlib distlib==0.4.3 # via virtualenv dnspython==2.8.0 # via pymongo docutils==0.21.2 # via # myst-parser # sphinx # sphinx-markdown-builder # sphinx-rtd-theme execnet==2.1.2 # via pytest-xdist filelock==3.32.3 # via # python-discovery # virtualenv flake8==7.3.0 # via emmet-core (pyproject.toml) fonttools==4.63.0 # via matplotlib h5py==3.16.0 # via phonopy identify==2.6.19 # via pre-commit idna==3.18 # via requests imageio==2.37.4 # via scikit-image imagesize==2.0.0 # via sphinx inflect==7.5.0 # via emmet-core (pyproject.toml) iniconfig==2.3.0 # via pytest jinja2==3.1.6 # via # myst-parser # sphinx joblib==1.5.3 # via # pymatgen-analysis-diffusion # pymatgen-core kiwisolver==1.5.0 # via matplotlib latexcodec==3.0.1 # via pybtex lazy-loader==0.5 # via scikit-image librt==0.15.0 # via mypy lobsterpy==0.6.1 # via emmet-core (pyproject.toml) lxml==6.1.1 # via pymatgen-core markdown-it-py==4.2.0 # via # mdit-py-plugins # myst-parser markupsafe==3.0.3 # via jinja2 matplotlib==3.11.1 # via # ase # phonopy # pymatgen-core # seaborn mccabe==0.7.0 # via flake8 mdit-py-plugins==0.6.1 # via myst-parser mdurl==0.1.2 # via markdown-it-py monty==2026.7.16 # via # custodian # emmet-core (pyproject.toml) # pymatgen-core more-itertools==11.1.0 # via inflect mp-pyrho==0.5.1 # via pymatgen-analysis-defects mpmath==1.3.0 # via sympy mypy==2.3.1 # via emmet-core (pyproject.toml) mypy-extensions==1.1.0 # via # emmet-core (pyproject.toml) # mypy myst-parser==5.1.0 # via emmet-core (pyproject.toml) narwhals==2.24.0 # via plotly networkx==3.6.1 # via # pymatgen-core # scikit-image nodeenv==1.10.0 # via pre-commit numpy==2.5.2 # via # ase # contourpy # h5py # imageio # lobsterpy # matplotlib # monty # pandas # phonopy # pymatgen-analysis-defects # pymatgen-analysis-diffusion # pymatgen-core # pymatgen-io-validation # scikit-image # scipy # seaborn # seekpath # shapely # spglib # symfc # tifffile orjson==3.12.0 # via pymatgen-core packaging==26.3 # via # lazy-loader # matplotlib # plotly # pytest # scikit-image # sphinx palettable==3.3.3 # via pymatgen-core pandas==3.0.5 # via # pymatgen-core # seaborn pathspec==1.1.1 # via mypy phonopy==4.4.0 # via emmet-core (pyproject.toml) phonors==0.3.0 # via phonopy pillow==12.3.0 # via # imageio # matplotlib # scikit-image platformdirs==4.11.3 # via virtualenv plotly==6.9.0 # via pymatgen-core pluggy==1.6.0 # via # pytest # pytest-cov pre-commit==4.6.2 # via emmet-core (pyproject.toml) psutil==7.2.2 # via custodian pubchempy==1.0.5 # via emmet-core (pyproject.toml) pyarrow==25.0.1 # via emmet-core (pyproject.toml) pybtex==0.26.1 # via emmet-core (pyproject.toml) pycodcif==3.0.1 # via emmet-core (pyproject.toml) pycodestyle==2.14.0 # via # emmet-core (pyproject.toml) # flake8 pydantic==2.13.4 # via # emmet-core (pyproject.toml) # pydantic-settings # pymatgen-io-validation pydantic-core==2.46.4 # via pydantic pydantic-settings==2.15.0 # via # emmet-core (pyproject.toml) # pymatgen-io-validation pydocstyle==6.3.0 # via emmet-core (pyproject.toml) pyflakes==3.4.0 # via flake8 pygments==2.21.0 # via # pytest # sphinx pymatgen==2026.5.4 # via # emmet-core (pyproject.toml) # lobsterpy # mp-pyrho # pymatgen-analysis-alloys # pymatgen-analysis-defects # pymatgen-analysis-diffusion pymatgen-analysis-alloys==0.0.9 # via emmet-core (pyproject.toml) pymatgen-analysis-defects==2026.3.20 # via emmet-core (pyproject.toml) pymatgen-analysis-diffusion==2025.11.15 # via emmet-core (pyproject.toml) pymatgen-core==2026.7.16 # via # emmet-core (pyproject.toml) # pymatgen # pymatgen-io-validation pymatgen-io-validation==0.1.4 # via emmet-core (pyproject.toml) pymongo==4.17.0 # via emmet-core (pyproject.toml) pyparsing==3.3.2 # via # bibtexparser # matplotlib pytest==9.1.1 # via # emmet-core (pyproject.toml) # pytest-cov # pytest-xdist pytest-cov==7.1.0 # via emmet-core (pyproject.toml) pytest-xdist==3.8.0 # via emmet-core (pyproject.toml) python-dateutil==2.9.0.post0 # via # matplotlib # pandas python-discovery==1.5.2 # via virtualenv python-dotenv==1.2.3 # via pydantic-settings pyyaml==6.0.3 # via # myst-parser # phonopy # pre-commit # pybtex requests==2.34.2 # via # pymatgen-core # pymatgen-io-validation # sphinx roman-numerals==4.1.0 # via roman-numerals-py roman-numerals-py==4.1.0 # via sphinx ruamel-yaml==0.19.1 # via # custodian # monty scikit-image==0.26.0 # via pymatgen-analysis-defects scipy==1.18.0 # via # ase # pymatgen-core # scikit-image # symfc seaborn==0.13.2 # via pymatgen-analysis-diffusion seekpath==2.2.1 # via emmet-core (pyproject.toml) shapely==2.1.2 # via pymatgen-analysis-alloys six==1.17.0 # via python-dateutil snowballstemmer==3.1.1 # via # pydocstyle # sphinx spglib==2.7.0 # via # phonopy # pymatgen-core # seekpath # symfc sphinx==8.2.3 # via # emmet-core (pyproject.toml) # myst-parser # sphinx-autodoc-typehints # sphinx-markdown-builder # sphinx-rtd-theme # sphinxcontrib-jquery sphinx-autodoc-typehints==3.5.2 # via emmet-core (pyproject.toml) sphinx-markdown-builder==0.6.11 # via emmet-core (pyproject.toml) sphinx-rtd-theme==3.1.0 # via emmet-core (pyproject.toml) sphinxcontrib-applehelp==2.0.0 # via sphinx sphinxcontrib-devhelp==2.0.0 # via sphinx sphinxcontrib-htmlhelp==2.1.0 # via sphinx sphinxcontrib-jquery==4.1 # via sphinx-rtd-theme sphinxcontrib-jsmath==1.0.1 # via sphinx sphinxcontrib-qthelp==2.0.0 # via sphinx sphinxcontrib-serializinghtml==2.0.0 # via sphinx symfc==1.7.3 # via phonopy sympy==1.14.0 # via pymatgen-core tabulate==0.10.0 # via # pymatgen-core # sphinx-markdown-builder tifffile==2026.8.16 # via scikit-image tqdm==4.70.0 # via pymatgen-core typeguard==4.6.0 # via inflect types-requests==2.33.0.20260712 # via emmet-core (pyproject.toml) types-setuptools==84.0.0.20260812 # via emmet-core (pyproject.toml) typing-extensions==4.16.0 # via # ase # emmet-core (pyproject.toml) # mypy # pydantic # pydantic-core # typeguard # typing-inspection typing-inspection==0.4.4 # via # pydantic # pydantic-settings uncertainties==3.2.3 # via pymatgen-core urllib3==2.7.0 # via # requests # types-requests virtualenv==21.7.4 # via pre-commit wincertstore==0.2.1 # via emmet-core (pyproject.toml) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8301334 emmet_core-0.87.2/requirements/ubuntu-latest_py3.14.txt0000644000175100017510000000554715240717720022533 0ustar00runnerrunner# # This file is autogenerated by pip-compile with Python 3.14 # by the following command: # # pip-compile --output-file=requirements/ubuntu-latest_py3.14.txt # annotated-types==0.8.0 # via pydantic bibtexparser==1.4.4 # via pymatgen-core blake3==1.0.9 # via emmet-core (pyproject.toml) certifi==2026.7.22 # via requests charset-normalizer==3.5.1 # via requests contourpy==1.3.3 # via matplotlib cycler==0.12.1 # via matplotlib fonttools==4.63.0 # via matplotlib idna==3.18 # via requests inflect==7.5.0 # via emmet-core (pyproject.toml) joblib==1.5.3 # via pymatgen-core kiwisolver==1.5.0 # via matplotlib latexcodec==3.0.1 # via pybtex lxml==6.1.1 # via pymatgen-core matplotlib==3.11.1 # via pymatgen-core monty==2026.7.16 # via # emmet-core (pyproject.toml) # pymatgen-core more-itertools==11.1.0 # via inflect mpmath==1.3.0 # via sympy narwhals==2.24.0 # via plotly networkx==3.6.1 # via pymatgen-core numpy==2.5.2 # via # contourpy # matplotlib # monty # pandas # pymatgen-core # pymatgen-io-validation # scipy # spglib orjson==3.12.0 # via pymatgen-core packaging==26.3 # via # matplotlib # plotly palettable==3.3.3 # via pymatgen-core pandas==3.0.5 # via pymatgen-core pillow==12.3.0 # via matplotlib plotly==6.9.0 # via pymatgen-core pubchempy==1.0.5 # via emmet-core (pyproject.toml) pybtex==0.26.1 # via emmet-core (pyproject.toml) pydantic==2.13.4 # via # emmet-core (pyproject.toml) # pydantic-settings # pymatgen-io-validation pydantic-core==2.46.4 # via pydantic pydantic-settings==2.15.0 # via # emmet-core (pyproject.toml) # pymatgen-io-validation pymatgen==2026.5.4 # via emmet-core (pyproject.toml) pymatgen-core==2026.8.13 # via # pymatgen # pymatgen-io-validation pymatgen-io-validation==0.1.4 # via emmet-core (pyproject.toml) pyparsing==3.3.2 # via # bibtexparser # matplotlib python-dateutil==2.9.0.post0 # via # matplotlib # pandas python-dotenv==1.2.3 # via pydantic-settings pyyaml==6.0.3 # via pybtex requests==2.34.2 # via # pymatgen-core # pymatgen-io-validation ruamel-yaml==0.19.1 # via monty scipy==1.18.0 # via pymatgen-core six==1.17.0 # via python-dateutil spglib==2.7.0 # via pymatgen-core sympy==1.14.0 # via pymatgen-core tabulate==0.10.0 # via pymatgen-core tqdm==4.70.0 # via pymatgen-core typeguard==4.6.0 # via inflect typing-extensions==4.16.0 # via # emmet-core (pyproject.toml) # pydantic # pydantic-core # typeguard # typing-inspection typing-inspection==0.4.4 # via # pydantic # pydantic-settings uncertainties==3.2.3 # via pymatgen-core urllib3==2.7.0 # via requests ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8301334 emmet_core-0.87.2/requirements/ubuntu-latest_py3.14_extras.txt0000644000175100017510000001760215240717720024114 0ustar00runnerrunner# # This file is autogenerated by pip-compile with Python 3.14 # by the following command: # # pip-compile --all-extras --output-file=requirements/ubuntu-latest_py3.14_extras.txt # alabaster==1.0.0 # via sphinx annotated-types==0.8.0 # via pydantic ase==3.29.0 # via pymatgen-analysis-diffusion ast-serialize==0.8.0 # via mypy babel==2.18.0 # via sphinx bibtexparser==1.4.4 # via pymatgen-core blake3==1.0.9 # via emmet-core (pyproject.toml) certifi==2026.7.22 # via requests cfgv==3.5.0 # via pre-commit charset-normalizer==3.5.1 # via requests contourpy==1.3.3 # via matplotlib coverage[toml]==7.15.4 # via pytest-cov custodian==2025.12.14 # via emmet-core (pyproject.toml) cycler==0.12.1 # via matplotlib distlib==0.4.3 # via virtualenv dnspython==2.8.0 # via pymongo docutils==0.21.2 # via # myst-parser # sphinx # sphinx-markdown-builder # sphinx-rtd-theme execnet==2.1.2 # via pytest-xdist filelock==3.32.3 # via # python-discovery # virtualenv flake8==7.3.0 # via emmet-core (pyproject.toml) fonttools==4.63.0 # via matplotlib h5py==3.16.0 # via phonopy identify==2.6.19 # via pre-commit idna==3.18 # via requests imageio==2.37.4 # via scikit-image imagesize==2.0.0 # via sphinx inflect==7.5.0 # via emmet-core (pyproject.toml) iniconfig==2.3.0 # via pytest jinja2==3.1.6 # via # myst-parser # sphinx joblib==1.5.3 # via # pymatgen-analysis-diffusion # pymatgen-core kiwisolver==1.5.0 # via matplotlib latexcodec==3.0.1 # via pybtex lazy-loader==0.5 # via scikit-image librt==0.15.0 # via mypy lobsterpy==0.6.1 # via emmet-core (pyproject.toml) lxml==6.1.1 # via pymatgen-core markdown-it-py==4.2.0 # via # mdit-py-plugins # myst-parser markupsafe==3.0.3 # via jinja2 matplotlib==3.11.1 # via # ase # phonopy # pymatgen-core # seaborn mccabe==0.7.0 # via flake8 mdit-py-plugins==0.6.1 # via myst-parser mdurl==0.1.2 # via markdown-it-py monty==2026.7.16 # via # custodian # emmet-core (pyproject.toml) # pymatgen-core more-itertools==11.1.0 # via inflect mp-pyrho==0.5.1 # via pymatgen-analysis-defects mpmath==1.3.0 # via sympy mypy==2.3.1 # via emmet-core (pyproject.toml) mypy-extensions==1.1.0 # via # emmet-core (pyproject.toml) # mypy myst-parser==5.1.0 # via emmet-core (pyproject.toml) narwhals==2.24.0 # via plotly networkx==3.6.1 # via # pymatgen-core # scikit-image nodeenv==1.10.0 # via pre-commit numpy==2.5.2 # via # ase # contourpy # h5py # imageio # lobsterpy # matplotlib # monty # pandas # phonopy # pymatgen-analysis-defects # pymatgen-analysis-diffusion # pymatgen-core # pymatgen-io-validation # scikit-image # scipy # seaborn # seekpath # shapely # spglib # symfc # tifffile orjson==3.12.0 # via pymatgen-core packaging==26.3 # via # lazy-loader # matplotlib # plotly # pytest # scikit-image # sphinx palettable==3.3.3 # via pymatgen-core pandas==3.0.5 # via # pymatgen-core # seaborn pathspec==1.1.1 # via mypy phonopy==4.4.0 # via emmet-core (pyproject.toml) phonors==0.3.0 # via phonopy pillow==12.3.0 # via # imageio # matplotlib # scikit-image platformdirs==4.11.3 # via virtualenv plotly==6.9.0 # via pymatgen-core pluggy==1.6.0 # via # pytest # pytest-cov pre-commit==4.6.2 # via emmet-core (pyproject.toml) psutil==7.2.2 # via custodian pubchempy==1.0.5 # via emmet-core (pyproject.toml) pyarrow==25.0.1 # via emmet-core (pyproject.toml) pybtex==0.26.1 # via emmet-core (pyproject.toml) pycodcif==3.0.1 # via emmet-core (pyproject.toml) pycodestyle==2.14.0 # via # emmet-core (pyproject.toml) # flake8 pydantic==2.13.4 # via # emmet-core (pyproject.toml) # pydantic-settings # pymatgen-io-validation pydantic-core==2.46.4 # via pydantic pydantic-settings==2.15.0 # via # emmet-core (pyproject.toml) # pymatgen-io-validation pydocstyle==6.3.0 # via emmet-core (pyproject.toml) pyflakes==3.4.0 # via flake8 pygments==2.21.0 # via # pytest # sphinx pymatgen==2026.5.4 # via # emmet-core (pyproject.toml) # lobsterpy # mp-pyrho # pymatgen-analysis-alloys # pymatgen-analysis-defects # pymatgen-analysis-diffusion pymatgen-analysis-alloys==0.0.9 # via emmet-core (pyproject.toml) pymatgen-analysis-defects==2026.3.20 # via emmet-core (pyproject.toml) pymatgen-analysis-diffusion==2025.11.15 # via emmet-core (pyproject.toml) pymatgen-core==2026.7.16 # via # emmet-core (pyproject.toml) # pymatgen # pymatgen-io-validation pymatgen-io-validation==0.1.4 # via emmet-core (pyproject.toml) pymongo==4.17.0 # via emmet-core (pyproject.toml) pyparsing==3.3.2 # via # bibtexparser # matplotlib pytest==9.1.1 # via # emmet-core (pyproject.toml) # pytest-cov # pytest-xdist pytest-cov==7.1.0 # via emmet-core (pyproject.toml) pytest-xdist==3.8.0 # via emmet-core (pyproject.toml) python-dateutil==2.9.0.post0 # via # matplotlib # pandas python-discovery==1.5.2 # via virtualenv python-dotenv==1.2.3 # via pydantic-settings pyyaml==6.0.3 # via # myst-parser # phonopy # pre-commit # pybtex requests==2.34.2 # via # pymatgen-core # pymatgen-io-validation # sphinx roman-numerals==4.1.0 # via roman-numerals-py roman-numerals-py==4.1.0 # via sphinx ruamel-yaml==0.19.1 # via # custodian # monty scikit-image==0.26.0 # via pymatgen-analysis-defects scipy==1.18.0 # via # ase # pymatgen-core # scikit-image # symfc seaborn==0.13.2 # via pymatgen-analysis-diffusion seekpath==2.2.1 # via emmet-core (pyproject.toml) shapely==2.1.2 # via pymatgen-analysis-alloys six==1.17.0 # via python-dateutil snowballstemmer==3.1.1 # via # pydocstyle # sphinx spglib==2.7.0 # via # phonopy # pymatgen-core # seekpath # symfc sphinx==8.2.3 # via # emmet-core (pyproject.toml) # myst-parser # sphinx-autodoc-typehints # sphinx-markdown-builder # sphinx-rtd-theme # sphinxcontrib-jquery sphinx-autodoc-typehints==3.5.2 # via emmet-core (pyproject.toml) sphinx-markdown-builder==0.6.11 # via emmet-core (pyproject.toml) sphinx-rtd-theme==3.1.0 # via emmet-core (pyproject.toml) sphinxcontrib-applehelp==2.0.0 # via sphinx sphinxcontrib-devhelp==2.0.0 # via sphinx sphinxcontrib-htmlhelp==2.1.0 # via sphinx sphinxcontrib-jquery==4.1 # via sphinx-rtd-theme sphinxcontrib-jsmath==1.0.1 # via sphinx sphinxcontrib-qthelp==2.0.0 # via sphinx sphinxcontrib-serializinghtml==2.0.0 # via sphinx symfc==1.7.3 # via phonopy sympy==1.14.0 # via pymatgen-core tabulate==0.10.0 # via # pymatgen-core # sphinx-markdown-builder tifffile==2026.8.16 # via scikit-image tqdm==4.70.0 # via pymatgen-core typeguard==4.6.0 # via inflect types-requests==2.33.0.20260712 # via emmet-core (pyproject.toml) types-setuptools==84.0.0.20260812 # via emmet-core (pyproject.toml) typing-extensions==4.16.0 # via # ase # emmet-core (pyproject.toml) # mypy # pydantic # pydantic-core # typeguard # typing-inspection typing-inspection==0.4.4 # via # pydantic # pydantic-settings uncertainties==3.2.3 # via pymatgen-core urllib3==2.7.0 # via # requests # types-requests virtualenv==21.7.4 # via pre-commit wincertstore==0.2.1 # via emmet-core (pyproject.toml) ././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1787011029.002778 emmet_core-0.87.2/setup.cfg0000644000175100017510000000004615240717725015230 0ustar00runnerrunner[egg_info] tag_build = tag_date = 0 ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011028.9935217 emmet_core-0.87.2/tests/0000755000175100017510000000000015240717725014551 5ustar00runnerrunner././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8301334 emmet_core-0.87.2/tests/__init__.py0000644000175100017510000000046415240717720016661 0ustar00runnerrunnerfrom emmet.core.io.pymatgen import STRUCTURES_DIR, Structure struct_names = ( "SiO2 Li2O LiFePO4 TlBiSe2 K2O2 Li3V2(PO4)3 Li2O2 CsCl NaFePO4 Pb2TiZrO6 " "SrTiO3 TiO2 BaNiO3 VO2".split() ) test_structures = { name: Structure.from_file(STRUCTURES_DIR / f"{name}.json") for name in struct_names } ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8304517 emmet_core-0.87.2/tests/conftest.py0000644000175100017510000001772315240717720016755 0ustar00runnerrunnerfrom __future__ import annotations from monty.serialization import loadfn import os from emmet.core.io.pymatgen import Structure import pytest from shutil import rmtree from tempfile import mkdtemp from typing import TYPE_CHECKING from emmet.core.testing_utils import TEST_FILES_DIR from emmet.core.types.enums import VaspObject if TYPE_CHECKING: from pathlib import Path from emmet.core.io.pymatgen import BandStructureSymmLine @pytest.fixture(scope="session") def test_dir(): return TEST_FILES_DIR @pytest.fixture def tmp_dir(): """Make temporary directory for tests.""" old_cwd = os.getcwd() new_path = mkdtemp() os.chdir(new_path) yield os.chdir(old_cwd) rmtree(new_path) class SchemaTestData: """Dummy class to be used to contain all test data information.""" class SiOptimizeDouble(SchemaTestData): folder = "Si_old_double_relax" task_files = { "relax2": { "vasprun_file": "vasprun.xml.relax2.gz", "outcar_file": "OUTCAR.relax2.gz", "volumetric_files": ["CHGCAR.relax2.gz"], "contcar_file": "CONTCAR.relax2.gz", }, "relax1": { "vasprun_file": "vasprun.xml.relax1.gz", "outcar_file": "OUTCAR.relax1.gz", "volumetric_files": ["CHGCAR.relax1.gz"], "contcar_file": "CONTCAR.relax1.gz", }, } objects = {"relax2": []} task_doc = { "calcs_reversed": [ { "output": { "vbm": 5.6147, "cbm": 6.2652, "bandgap": 0.6505, "is_gap_direct": False, "is_metal": False, "transition": "(0.000,0.000,0.000)-(0.375,0.375,0.000)", "direct_gap": 2.5561, "run_stats": { "average_memory": 0, "max_memory": 28096.0, "cores": 16, }, }, "input": { "incar": {"NSW": 99}, "nkpoints": 29, "potcar_spec": [{"titel": "PAW_PBE Si 05Jan2001"}], "structure": {"volume": 40.036816205493494}, "is_hubbard": False, "hubbards": None, }, } ], "analysis": {"delta_volume": 0.8638191769757384, "max_force": 0}, "input": { "structure": {"volume": 40.036816205493494}, "potcar_spec": [{"titel": "PAW_PBE Si 05Jan2001"}], "parameters": {"NSW": 99}, "is_hubbard": False, "hubbards": None, }, "output": { "structure": {"volume": 40.90063538246923}, "energy": -10.84687704, "bandgap": 0.6505, }, "custodian": [{"job": {"settings_override": None, "suffix": ".relax1"}}], "included_objects": (), } class SiNonSCFUniform(SchemaTestData): folder = "Si_uniform" task_files = { "standard": { "vasprun_file": "vasprun.xml.gz", "outcar_file": "OUTCAR.gz", "volumetric_files": ["CHGCAR.gz"], "contcar_file": "CONTCAR.gz", } } objects = {"standard": []} task_doc = { "calcs_reversed": [ { "output": { "vbm": 5.6162, "cbm": 6.2243, "bandgap": 0.6103, "is_gap_direct": False, "is_metal": False, "transition": "(0.000,0.000,0.000)-(0.000,0.421,0.000)", "direct_gap": 2.5563, "run_stats": { "average_memory": 0, "max_memory": 31004.0, "cores": 16, }, }, "input": { "incar": {"NSW": 0}, "nkpoints": 220, "potcar_spec": [{"titel": "PAW_PBE Si 05Jan2001"}], "structure": {"volume": 40.88829843008916}, "is_hubbard": False, "hubbards": None, }, } ], "analysis": {"delta_volume": 0, "max_force": 0.5350159115036506}, "input": { "structure": {"volume": 40.88829843008916}, "potcar_spec": [{"titel": "PAW_PBE Si 05Jan2001"}], "parameters": {"NSW": 0}, "is_hubbard": False, "hubbards": None, }, "output": { "structure": {"volume": 40.88829843008916}, "energy": -10.85064059, "bandgap": 0.6103, }, "custodian": [{"job": {"settings_override": None, "suffix": ""}}], "included_objects": (VaspObject.DOS, VaspObject.BANDSTRUCTURE), } class SiStatic(SchemaTestData): folder = "Si_static" task_files = { "standard": { "vasprun_file": "vasprun.xml.gz", "outcar_file": "OUTCAR.gz", "volumetric_files": ["CHGCAR.gz"], "contcar_file": "CONTCAR.gz", } } objects = {"standard": []} task_doc = { "calcs_reversed": [ { "output": { "vbm": 5.6163, "cbm": 6.2644, "bandgap": 0.6506, "is_gap_direct": False, "is_metal": False, "transition": "(0.000,0.000,0.000)-(0.000,0.375,0.000)", "direct_gap": 2.5563, "run_stats": { "average_memory": 0, "max_memory": 28124.0, "cores": 16, }, }, "input": { "incar": {"NSW": 1}, "nkpoints": 29, "potcar_spec": [{"titel": "PAW_PBE Si 05Jan2001"}], "structure": {"volume": 40.88829843008916}, }, } ], "analysis": {"delta_volume": 0, "max_force": 0.0}, "input": { "structure": {"volume": 40.88829843008916}, "potcar_spec": [{"titel": "PAW_PBE Si 05Jan2001"}], "parameters": {"NSW": 0}, "is_hubbard": False, "hubbards": None, }, "output": { "structure": {"volume": 40.88829843008916}, "energy": -10.84678256, "bandgap": 0.6506, "dos_properties": { "Si": { "s": { "filling": 0.624669545020562, "center": -2.5151284433409815, "bandwidth": 7.338662205126851, "skewness": 0.6261990748648925, "kurtosis": 2.0074877073276904, "upper_edge": -8.105469079999999, }, "p": { "filling": 0.3911927710592045, "center": 3.339269798287516, "bandwidth": 5.999449671419663, "skewness": 0.0173776678056677, "kurtosis": 1.907790411890831, "upper_edge": -0.7536690799999999, }, } }, }, "custodian": [{"job": {"settings_override": None, "suffix": ""}}], "included_objects": (), } objects = {cls.__name__: cls for cls in SchemaTestData.__subclasses__()} def get_test_object(object_name): """Get the schema test data object from the class name.""" return objects[object_name] @pytest.fixture(scope="session") def fe_line_mode_bandstructure(test_dir: Path) -> BandStructureSymmLine: """ Fe (mp-13) line-mode band structure """ return loadfn(test_dir / "electronic_structure/Fe_bs.json.gz") @pytest.fixture(scope="session") def mp_149_structure(test_dir: Path) -> Structure: return Structure.from_file(test_dir / "Si_mp_149.cif") ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8304517 emmet_core-0.87.2/tests/conftest_qchem.py0000644000175100017510000003200615240717720020121 0ustar00runnerrunnerimport pytest import numpy as np from monty.io import zopen import json from monty.json import MontyDecoder import importlib from emmet.core.testing_utils import TEST_FILES_DIR OPENBABEL_INSTALLED = importlib.util.find_spec("openbabel") is not None @pytest.fixture(scope="session") def test_dir(): return TEST_FILES_DIR class SchemaTestData: """Dummy class to be used to contain all test data information""" class SinglePointTest(SchemaTestData): folder = "qchem_sp_test" task_files = { "standard": { "qcinput_file": "mol.qin.gz", "qcoutput_file": "mol.qout.gz", } } objects = {"standard": []} task_doc = { "calcs_reversed": [ { "output": { "mulliken": [np.array([-0.713178, 0.357278, 0.3559])], "resp": [np.array([-0.872759, 0.436379, 0.436379])], "final_energy": -76.4493700739, }, "input": { "charge": 0, "rem": { "job_type": "sp", "basis": "def2-qzvppd", "max_scf_cycles": "100", "gen_scfman": "true", "xc_grid": "3", "thresh": "14", "s2thresh": "16", "scf_algorithm": "diis", "resp_charges": "true", "symmetry": "false", "sym_ignore": "true", "method": "wb97mv", "solvent_method": "smd", "ideriv": "1", }, "job_type": "sp", }, } ], "input": { "initial_molecule": { "@module": "pymatgen.core.structure", "@class": "Molecule", "charge": 0.0, "spin_multiplicity": 1, "sites": [ { "name": "O", "species": [{"element": "O", "occu": 1}], "xyz": [-0.80595, 2.22952, -0.01914], "properties": {}, "label": "O", }, { "name": "H", "species": [{"element": "H", "occu": 1}], "xyz": [0.18338, 2.20176, 0.01351], "properties": {}, "label": "H", }, { "name": "H", "species": [{"element": "H", "occu": 1}], "xyz": [-1.09531, 1.61602, 0.70231], "properties": {}, "label": "H", }, ], }, "rem": { "job_type": "sp", "basis": "def2-qzvppd", "max_scf_cycles": "100", "gen_scfman": "true", "xc_grid": "3", "thresh": "14", "s2thresh": "16", "scf_algorithm": "diis", "resp_charges": "true", "symmetry": "false", "sym_ignore": "true", "method": "wb97mv", "solvent_method": "smd", "ideriv": "1", }, "level_of_theory": "wB97M-V/def2-QZVPPD/SMD", "task_type": "Single Point", "calc_type": "wB97M-V/def2-QZVPPD/SMD Single Point", "solvation_lot_info": "wB97M-V/def2-QZVPPD/SMD(SOLVENT=WATER)", }, "output": { "mulliken": [np.array([-0.713178, 0.357278, 0.3559])], "resp": [np.array([-0.872759, 0.436379, 0.436379])], "final_energy": -76.4493700739, "dipoles": { "total": 2.4648, "dipole": [np.array([1.4227, -1.3039, 1.5333])], "RESP_total": 2.5411, "RESP_dipole": [np.array([1.4672, -1.3441, 1.5806])], }, }, "custodian": [ { "job": { "@module": "custodian.qchem.jobs", "@class": "QCJob", "@version": "2022.5.26", "qchem_command": ["qchem"], "max_cores": "40", "multimode": "openmp", "input_file": "mol.qin", "output_file": "mol.qout", "qclog_file": "mol.qclog", "suffix": "", "calc_loc": "/tmp", "nboexe": None, "save_scratch": False, "backup": "true", }, "corrections": [], } ], } class OptimizationTest(SchemaTestData): folder = "qchem_opt_test" task_files = { "standard": { "qcinput_file": "mol.qin.gz", "qcoutput_file": "mol.qout.gz", } } objects = {"standard": []} task_doc = { "calcs_reversed": [ { "output": { "optimized_molecule": { "@module": "pymatgen.core.structure", "@class": "Molecule", "charge": 0, "spin_multiplicity": 1, "sites": [ { "name": "O", "species": [{"element": "O", "occu": 1}], "xyz": [-0.8008592596, 2.2248298937, -0.0136245943], "properties": {}, "label": "O", }, { "name": "H", "species": [{"element": "H", "occu": 1}], "xyz": [0.1637955748, 2.1962925542, 0.0199393927], "properties": {}, "label": "H", }, { "name": "H", "species": [{"element": "H", "occu": 1}], "xyz": [-1.0808163152, 1.6261775521, 0.6903652017], "properties": {}, "label": "H", }, ], }, "mulliken": [-0.373491, 0.186964, 0.186527], "resp": [-0.89522, 0.44761, 0.44761], "final_energy": -76.358341626913, }, "input": { "charge": 0, "rem": { "job_type": "sp", "basis": "def2-qzvppd", "max_scf_cycles": "100", "gen_scfman": "true", "xc_grid": "3", "thresh": "14", "s2thresh": "16", "scf_algorithm": "diis", "resp_charges": "true", "symmetry": "false", "sym_ignore": "true", "method": "wb97mv", "solvent_method": "smd", "ideriv": "1", }, "job_type": "sp", }, } ], "input": { "initial_molecule": { "@module": "pymatgen.core.structure", "@class": "Molecule", "charge": 0.0, "spin_multiplicity": 1, "sites": [ { "name": "O", "species": [{"element": "O", "occu": 1}], "xyz": [-0.80595, 2.22952, -0.01914], "properties": {}, "label": "O", }, { "name": "H", "species": [{"element": "H", "occu": 1}], "xyz": [0.18338, 2.20176, 0.01351], "properties": {}, "label": "H", }, { "name": "H", "species": [{"element": "H", "occu": 1}], "xyz": [-1.09531, 1.61602, 0.70231], "properties": {}, "label": "H", }, ], }, "rem": { "job_type": "opt", "basis": "def2-svpd", "max_scf_cycles": "100", "gen_scfman": "true", "xc_grid": "3", "thresh": "14", "s2thresh": "16", "scf_algorithm": "diis", "resp_charges": "true", "symmetry": "false", "sym_ignore": "true", "method": "wb97mv", "solvent_method": "smd", "ideriv": "1", "geom_opt2": "3", }, "level_of_theory": "wB97M-V/def2-SVPD/SMD", "task_type": "Geometry Optimization", "calc_type": "wB97M-V/def2-SVPD/SMD Geometry Optimization", "solvation_lot_info": "wB97M-V/def2-SVPD/SMD(SOLVENT=WATER)", }, "output": { "initial_molecule": { "@module": "pymatgen.core.structure", "@class": "Molecule", "charge": 0.0, "spin_multiplicity": 1, "sites": [ { "name": "O", "species": [{"element": "O", "occu": 1}], "xyz": [-0.80595, 2.22952, -0.01914], "properties": {}, "label": "O", }, { "name": "H", "species": [{"element": "H", "occu": 1}], "xyz": [0.18338, 2.20176, 0.01351], "properties": {}, "label": "H", }, { "name": "H", "species": [{"element": "H", "occu": 1}], "xyz": [-1.09531, 1.61602, 0.70231], "properties": {}, "label": "H", }, ], }, "optimized_molecule": { "@module": "pymatgen.core.structure", "@class": "Molecule", "charge": 0, "spin_multiplicity": 1, "sites": [ { "name": "O", "species": [{"element": "O", "occu": 1}], "xyz": [-0.8008592596, 2.2248298937, -0.0136245943], "properties": {}, "label": "O", }, { "name": "H", "species": [{"element": "H", "occu": 1}], "xyz": [0.1637955748, 2.1962925542, 0.0199393927], "properties": {}, "label": "H", }, { "name": "H", "species": [{"element": "H", "occu": 1}], "xyz": [-1.0808163152, 1.6261775521, 0.6903652017], "properties": {}, "label": "H", }, ], }, "mulliken": [-0.373491, 0.186964, 0.186527], "resp": [-0.89522, 0.44761, 0.44761], "final_energy": -76.358341626913, }, "custodian": [ { "job": { "@module": "custodian.qchem.jobs", "@class": "QCJob", "@version": "2022.5.26", "qchem_command": ["qchem"], "max_cores": "40", "multimode": "openmp", "input_file": "mol.qin", "output_file": "mol.qout", "qclog_file": "mol.qclog", "suffix": "", "calc_loc": "/tmp", "nboexe": "null", "save_scratch": "false", "backup": "true", }, "corrections": [], } ], } objects = {cls.__name__: cls for cls in SchemaTestData.__subclasses__()} def get_test_object(object_name): """Get the schema test data object from the class name.""" return objects[object_name] def safe_load(path): with zopen(path, "rt") as f: data = json.load(f) if not OPENBABEL_INSTALLED: data.pop("custodian", None) return MontyDecoder().process_decoded(data) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011028.9939215 emmet_core-0.87.2/tests/featurization/0000755000175100017510000000000015240717725017435 5ustar00runnerrunner././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8304517 emmet_core-0.87.2/tests/featurization/__init__.py0000644000175100017510000000000015240717720021527 0ustar00runnerrunner././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011028.9945066 emmet_core-0.87.2/tests/featurization/robocrys/0000755000175100017510000000000015240717725021277 5ustar00runnerrunner././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8306394 emmet_core-0.87.2/tests/featurization/robocrys/__init__.py0000644000175100017510000000000015240717720023371 0ustar00runnerrunner././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011028.9954817 emmet_core-0.87.2/tests/featurization/robocrys/condense/0000755000175100017510000000000015240717725023075 5ustar00runnerrunner././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8306906 emmet_core-0.87.2/tests/featurization/robocrys/condense/test_component.py0000644000175100017510000002205415240717720026506 0ustar00runnerrunnerfrom emmet.core.io.pymatgen import ( get_structure_components, CrystalNN, StructureMatcher, SpacegroupAnalyzer, ) import pytest from emmet.core.featurization.robocrys.condense.component import ( components_are_isomorphic, components_are_vdw_heterostructure, filter_molecular_components, get_component_formula, get_component_formula_and_factor, get_formula_from_components, get_formula_inequiv_components, get_reconstructed_structure, get_structure_inequiv_components, get_sym_inequiv_components, get_vdw_heterostructure_information, ) @pytest.fixture def mapi(test_structures): return CrystalNN().get_bonded_structure(test_structures["mapi"].copy()) @pytest.fixture def mapi_components(mapi): return get_structure_components( mapi, inc_molecule_graph=True, inc_site_ids=True, inc_orientation=True ) @pytest.fixture def vdw_hetero(test_structures): return CrystalNN().get_bonded_structure(test_structures["MoWS4"]) @pytest.fixture def vdw_hetero_components(vdw_hetero): return get_structure_components( vdw_hetero, inc_molecule_graph=True, inc_site_ids=True, inc_orientation=True, ) def test_get_component_formula_and_factor(mapi_components): """Test getting the component formula and factor.""" formula, factor = get_component_formula_and_factor( mapi_components[0], use_common_formulas=True, use_iupac_formula=True ) assert formula == "CH3NH3" assert factor == 1 formula, factor = get_component_formula_and_factor( mapi_components[4], use_common_formulas=True, use_iupac_formula=True ) assert formula == "PbI3" assert factor == 4 # test without common formulas formula, factor = get_component_formula_and_factor( mapi_components[0], use_common_formulas=False, use_iupac_formula=True ) assert formula == "CNH6" assert factor == 1 # test without common formulas and without iupac formula formula, factor = get_component_formula_and_factor( mapi_components[0], use_common_formulas=False, use_iupac_formula=False ) assert formula == "H6CN" assert factor == 1 def test_get_component_formula(mapi_components): """Test getting the component formula.""" formula = get_component_formula( mapi_components[0], use_common_formulas=True, use_iupac_formula=True ) assert formula == "CH3NH3" formula = get_component_formula( mapi_components[4], use_common_formulas=True, use_iupac_formula=True ) assert formula == "PbI3" # test without common formulas formula = get_component_formula( mapi_components[0], use_common_formulas=False, use_iupac_formula=True ) assert formula == "CNH6" # test without common formulas and without iupac formula formula = get_component_formula( mapi_components[0], use_common_formulas=False, use_iupac_formula=False ) assert formula == "H6CN" def test_get_sym_inequiv_components(mapi, mapi_components): """Test getting symmetrically inequivalent structure components.""" sga = SpacegroupAnalyzer(mapi.structure, symprec=0.01) inequiv_comp = get_sym_inequiv_components(mapi_components, sga) assert len(inequiv_comp) == 2 assert inequiv_comp[0]["count"] == 4 assert inequiv_comp[1]["count"] == 1 def test_get_comp_inequiv_components(mapi_components): """Test getting compositionally inequivalent structure components.""" inequiv_comp = get_formula_inequiv_components(mapi_components) assert len(inequiv_comp) == 2 assert inequiv_comp[0]["count"] == 4 assert inequiv_comp[0]["formula"] == "CH3NH3" assert inequiv_comp[1]["count"] == 4 assert inequiv_comp[1]["formula"] == "PbI3" # Test not using common_formulas but with iupac formula inequiv_comp = get_formula_inequiv_components( mapi_components, use_iupac_formula=True, use_common_formulas=False ) assert inequiv_comp[0]["count"] == 4 assert inequiv_comp[0]["formula"] == "CNH6" # test non-iupac formula inequiv_comp = get_formula_inequiv_components( mapi_components, use_iupac_formula=False, use_common_formulas=False ) assert inequiv_comp[0]["count"] == 4 assert inequiv_comp[0]["formula"] == "H6CN" def test_filter_molecular_components(mapi_components): """Test filtering of molecular components.""" mol_comps, comps = filter_molecular_components(mapi_components) mol_dimen = list({c["dimensionality"] for c in mol_comps}) other_dimen = list({c["dimensionality"] for c in comps}) assert len(mol_comps) == 4 assert len(mol_dimen) == 1 assert mol_dimen[0] == 0 assert len(comps) == 1 assert 0 not in other_dimen def test_get_reconstructed_structure(mapi, mapi_components): structure = get_reconstructed_structure(mapi_components, simplify_molecules=False) # check the reconstructred structure matches the original using # pymatgen's structure matcher. sm = StructureMatcher( scale=False, primitive_cell=False, ltol=1e-4, stol=1e-4, angle_tol=1e-4 ) assert sm.fit(structure, mapi.structure) # Test the structure matches when we simplify molecules. # To do this we ignore all C, H, and N atoms. structure = get_reconstructed_structure(mapi_components, simplify_molecules=True) sm = StructureMatcher( scale=False, primitive_cell=False, ltol=1e-4, stol=1e-4, angle_tol=1e-4, ignored_species=["C", "H", "N"], ) assert sm.fit(structure, mapi.structure) def test_get_formula_from_components(test_structures, mapi_components): assert ( get_formula_from_components( mapi_components, use_common_formulas=True, use_iupac_formula=True ) == "CH3NH3PbI3" ) # check not using common formulas assert ( get_formula_from_components( mapi_components, use_common_formulas=False, use_iupac_formula=True ) == "CNH6PbI3" ) # check non-iupac ordering works assert ( get_formula_from_components( mapi_components, use_iupac_formula=False, use_common_formulas=False ) == "H6CNPbI3" ) # test multiple groups of different numbers of compositions s = CrystalNN().get_bonded_structure(test_structures["CuH8CN5Cl3"]) comps = get_structure_components(s) assert ( get_formula_from_components( comps, use_iupac_formula=True, use_common_formulas=True ) == "(CuCN4HCl)2(NH2)2(H2)3(HCl)4" ) # test putting molecules first s = CrystalNN().get_bonded_structure(test_structures["ZrCuH8C2NCl6"]) comps = get_structure_components(s) assert ( get_formula_from_components( comps, molecules_first=False, use_iupac_formula=True, use_common_formulas=True, ) == "ZrCuCl6(CH3)2NH2" ) assert ( get_formula_from_components( comps, molecules_first=True, use_iupac_formula=True, use_common_formulas=True, ) == "(CH3)2NH2ZrCuCl6" ) def test_components_are_vdw_heterostructure(mapi_components, vdw_hetero_components): assert components_are_vdw_heterostructure(vdw_hetero_components) assert not components_are_vdw_heterostructure(mapi_components) def test_get_vdw_heterostructure_information( mapi_components, vdw_hetero, vdw_hetero_components ): data = get_vdw_heterostructure_information( vdw_hetero_components, inc_ordered_components=True, inc_intercalants=True, ) assert len(data["ordered_components"]) == 4 assert data["ordered_components"][0]["structure_graph"].structure.frac_coords[0][ 0 ] == pytest.approx(0.33330876) assert data["ordered_components"][3]["structure_graph"].structure.frac_coords[0][ 0 ] == pytest.approx(0.6666924) assert data["repeating_unit"] == ["MoS2", "WS2"] assert data["num_repetitions"] == 2 assert data["intercalants"] == [] # test error catching with pytest.raises(ValueError): get_vdw_heterostructure_information(mapi_components) with pytest.raises(KeyError): get_vdw_heterostructure_information(get_structure_components(vdw_hetero)) def test_components_are_isomorphic(mapi_components): # check two CH3NH3 components are isomorphic assert components_are_isomorphic(mapi_components[0], mapi_components[1]) # check CH3NH3 and PbI3 are not isomorphic assert not components_are_isomorphic(mapi_components[0], mapi_components[4]) def test_get_structure_inequiv_components(mapi_components): inequiv_comp = get_structure_inequiv_components( mapi_components, use_structure_graph=False ) assert len(inequiv_comp) == 2 assert inequiv_comp[0]["count"] == 4 assert inequiv_comp[1]["count"] == 1 # test using graph/fingerprint matching inequiv_comp = get_structure_inequiv_components( mapi_components, use_structure_graph=True ) assert len(inequiv_comp) == 2 assert inequiv_comp[0]["count"] == 4 assert inequiv_comp[1]["count"] == 1 ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8308017 emmet_core-0.87.2/tests/featurization/robocrys/condense/test_fingerprint.py0000644000175100017510000000303315240717720027027 0ustar00runnerrunnerfrom __future__ import annotations import pytest from emmet.core.featurization.robocrys.condense.fingerprint import ( get_fingerprint_distance, get_site_fingerprints, get_structure_fingerprint, ) @pytest.fixture def fe(test_structures): return test_structures["iron"].copy() def test_get_site_fingerprints(fe): """Test to check site fingerprinting.""" finger = get_site_fingerprints(fe)[0] assert finger["body-centered cubic CN_8"] == pytest.approx(0.576950507) # check as_dict option finger = get_site_fingerprints(fe, as_dict=False)[0] assert finger[30] == pytest.approx(0.576950507) def test_get_structure_fingerprint(fe): """Test to check structure fingerprinting.""" fingerprint = get_structure_fingerprint(fe) assert fingerprint[4] == pytest.approx(1.98432036e-03) # test stats option fingerprint = get_structure_fingerprint(fe, stats=("mean",)) assert fingerprint[31] == pytest.approx(2.51322893e-01) def test_get_fingerprint_distance(fe): """Tests to check getting fingerprint distance.""" finger_1 = [0, 0, 0, 1] finger_2 = [1, 0, 0, 0] dist = get_fingerprint_distance(finger_1, finger_2) assert dist == pytest.approx(1.4142135623730951) # test automatic conversion from structure to fingerprint dist = get_fingerprint_distance(fe, fe) assert dist == pytest.approx(0.0) # test one structure one fingerprint finger_1 = get_structure_fingerprint(fe) dist = get_fingerprint_distance(fe, finger_1) assert dist == pytest.approx(0.0) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8308017 emmet_core-0.87.2/tests/featurization/robocrys/condense/test_mineral.py0000644000175100017510000000517215240717720026135 0ustar00runnerrunnerimport pytest from emmet.core.featurization.robocrys.condense.mineral import MineralMatcher @pytest.fixture def matcher(): return MineralMatcher() @pytest.fixture def tin_dioxide(test_structures): return test_structures["SnO2"].copy() @pytest.fixture def double_perov(test_structures): return test_structures["double_perovskite"].copy() def test_get_aflow_matches(matcher, tin_dioxide): """Test AFLOW prototype matching.""" matches = matcher.get_aflow_matches(tin_dioxide) assert len(matches) == 1, "number of matches is not equal to 1" assert matches[0]["type"] == "Rutile", "SnO2 mineral name incorrect" assert matches[0]["distance"] == pytest.approx( 0.15047694852244528 ), "SnO2 fingerprint distance does not match" assert matches[0].get("structure"), "SnO2 structure not present in match dictionary" def test_get_fingerprint_matches(matcher, tin_dioxide, double_perov): """Test fingerprint based matching.""" matches = matcher.get_fingerprint_matches(tin_dioxide) assert len(matches) == 4, "number of matches is not equal to 1" assert matches[0]["type"] == "Hydrophilite", "SnO2 mineral name incorrect" assert matches[0]["distance"] == pytest.approx( 0.1429748846147379 ), "SnO2 fingerprint distance does not match" assert matches[0].get("structure"), "SnO2 structure not present in match dictionary" # test fingerprint only matches same number of atoms matches = matcher.get_fingerprint_matches(double_perov) assert matches is None # test fingerprint can match different number of atoms matches = matcher.get_fingerprint_matches(double_perov, match_n_sp=False) assert len(matches) == 1, "double perovskite number of matches not correct" assert ( matches[0]["type"] == "(Cubic) Perovskite" ), "Double perovskite mineral name incorrect" assert matches[0]["distance"] == pytest.approx( 0.11697185 ), "double perovskite fingerprint distance does not match" assert matches[0].get( "structure" ), "perovskite structure not present in match dictionary" def test_get_best_mineral_name(matcher, tin_dioxide, double_perov): """Test mineral name matching.""" mineral_data = matcher.get_best_mineral_name(tin_dioxide) assert mineral_data["type"] == "Rutile" assert mineral_data["distance"] == -1.0 assert mineral_data["n_species_type_match"] is True mineral_data = matcher.get_best_mineral_name(double_perov) assert mineral_data["type"] == "(Cubic) Perovskite" assert mineral_data["distance"] == pytest.approx(0.116971854532) assert mineral_data["n_species_type_match"] is False ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8308017 emmet_core-0.87.2/tests/featurization/robocrys/condense/test_molecule.py0000644000175100017510000000352715240717720026315 0ustar00runnerrunnerimport pytest from emmet.core.io.pymatgen import get_structure_components, CrystalNN from emmet.core.featurization.robocrys.condense.component import ( filter_molecular_components, ) from emmet.core.featurization.robocrys.condense.molecule import MoleculeNamer try: from openbabel import openbabel except Exception: openbabel = None @pytest.fixture def methylammonium(test_structures): mapi = CrystalNN().get_bonded_structure(test_structures["mapi"].copy()) mapi_components = get_structure_components(mapi, inc_molecule_graph=True) mol_components, _ = filter_molecular_components(mapi_components) return mol_components[0]["molecule_graph"] def test_init(): """Test initialising MoleculeNamer.""" mn = MoleculeNamer() assert mn mn = MoleculeNamer(use_online_pubchem=False) assert mn @pytest.mark.skipif(not openbabel, reason="Openbabel not installed.") def test_molecule_graph_to_smiles(methylammonium): """Test converting a molecule graph to SMILES string.""" smiles = MoleculeNamer.molecule_graph_to_smiles(methylammonium) assert smiles == "C[NH3]" @pytest.mark.skipif(not openbabel, reason="Openbabel not installed.") def test_get_name_from_pubchem(): """Test downloading the molecule name from Pubchem.""" mn = MoleculeNamer() name = mn.get_name_from_pubchem("C[NH3]") assert name == "methylammonium" @pytest.mark.skipif(not openbabel, reason="Openbabel not installed.") def test_get_name_from_molecule_graph(methylammonium): """Test getting a molecule name from the molecule graph.""" mn = MoleculeNamer() name = mn.get_name_from_molecule_graph(methylammonium) assert name == "methylammonium" # test iupac naming source mn = MoleculeNamer(name_preference=("iupac",)) name = mn.get_name_from_molecule_graph(methylammonium) assert name == "methylazanium" ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8308017 emmet_core-0.87.2/tests/featurization/robocrys/condense/test_site.py0000644000175100017510000002244515240717720025454 0ustar00runnerrunnerfrom emmet.core.io.pymatgen import CrystalNN import pytest from emmet.core.featurization.robocrys.condense.site import ( SiteAnalyzer, geometries_match, nn_summaries_match, nnn_summaries_match, ) @pytest.fixture def cnn(): return CrystalNN() @pytest.fixture def tin_dioxide(cnn, test_structures): return cnn.get_bonded_structure(test_structures["SnO2"].copy()) @pytest.fixture def ba_n(cnn, test_structures): return cnn.get_bonded_structure(test_structures["BaN2"].copy()) def test_init(tin_dioxide, ba_n): """Test to check SiteDescriber can be initialised.""" sa = SiteAnalyzer(tin_dioxide) assert sa is not None, "tin dioxide site analyzer could not be init" # check different structure sa = SiteAnalyzer(ba_n) assert sa is not None, "BaN2 site analyzer could not be initialized" def test_equivalent_sites(ba_n): """Check equivalent sites instance variable set correctly.""" sa = SiteAnalyzer(ba_n, use_symmetry_equivalent_sites=False) assert sa.equivalent_sites == [0, 0, 0, 0, 4, 4] # test using symmetry to determine equivalent sites sa = SiteAnalyzer(ba_n, use_symmetry_equivalent_sites=True) assert isinstance(sa.equivalent_sites, list) assert sa.equivalent_sites == [0, 0, 0, 0, 4, 4] # test symprec option works sa = SiteAnalyzer(ba_n, use_symmetry_equivalent_sites=True, symprec=0.0001) assert sa.equivalent_sites == [0, 1, 1, 0, 4, 4] def test_symmetry_labels(ba_n): """Check equivalent sites instance variable set correctly.""" sa = SiteAnalyzer(ba_n, use_symmetry_equivalent_sites=False) assert sa.symmetry_labels == [1, 1, 1, 1, 1, 1] def test_get_site_geometry(tin_dioxide, ba_n): """Test site geometry description.""" sa = SiteAnalyzer(tin_dioxide) geom_data = sa.get_site_geometry(0) assert geom_data["type"] == "octahedral" assert geom_data["likeness"] == pytest.approx(0.9349776258427136) geom_data = sa.get_site_geometry(4) assert geom_data["type"] == "trigonal planar" assert geom_data["likeness"] == pytest.approx(0.6050243049545359) # check different structure sa = SiteAnalyzer(ba_n) geom_data = sa.get_site_geometry(0) assert geom_data["type"] == "6-coordinate" assert geom_data["likeness"] == 1 def test_get_nearest_neighbors(tin_dioxide, ba_n): """Check getting nearest neighbors.""" sa = SiteAnalyzer(tin_dioxide) info = sa.get_nearest_neighbors(4) assert len(info) == 3 assert info[0]["element"] == "Sn4+" assert info[0]["inequiv_index"] == 0 assert info[0]["dist"] == pytest.approx(2.0922101061490546) info = sa.get_nearest_neighbors(0, inc_inequivalent_site_index=False) assert "inequiv_index" not in info[0] # check different structure without oxi state sa = SiteAnalyzer(ba_n) info = sa.get_nearest_neighbors(0) assert len(info) == 6 assert info[0]["element"] == "N" assert info[0]["inequiv_index"] == 0 assert info[0]["dist"] == pytest.approx(1.2619877178483) def test_get_next_nearest_neighbors(tin_dioxide, ba_n): """Check getting next nearest neighbors.""" sa = SiteAnalyzer(tin_dioxide) info = sa.get_next_nearest_neighbors(5) assert len(info) == 14 idx = [i for i, s in enumerate(info) if s["connectivity"] == "edge"][0] assert info[idx]["element"] == "O2-" assert info[idx]["connectivity"] == "edge" assert info[idx]["geometry"]["type"] == "trigonal planar" assert info[idx]["inequiv_index"] == 2 info = sa.get_next_nearest_neighbors(0, inc_inequivalent_site_index=False) assert "inequiv_index" not in info[0] info = sa.get_next_nearest_neighbors(0) assert info[0]["element"] == "Sn4+" assert info[0]["connectivity"] == "edge" assert info[0]["geometry"]["type"] == "octahedral" assert len(info[0]["angles"]) == 2 assert info[0]["angles"][0] == pytest.approx(101.62287790513848) assert info[0]["distance"] == pytest.approx(3.24322132) # check different structure without oxi state sa = SiteAnalyzer(ba_n) info = sa.get_next_nearest_neighbors(0) assert len(info) == 36 assert info[5]["element"] == "N" assert info[5]["connectivity"] == "edge" assert info[5]["geometry"]["type"] == "6-coordinate" assert len(info[5]["angles"]) == 2 assert info[5]["angles"][0] == pytest.approx(83.91397867959587) assert info[5]["distance"] == pytest.approx(3.549136232944574) def test_get_site_summary(tin_dioxide): """Test getting the site summary.""" sa = SiteAnalyzer(tin_dioxide) data = sa.get_site_summary(0) assert data["element"] == "Sn4+" assert data["geometry"]["type"] == "octahedral" assert data["geometry"]["likeness"] == pytest.approx(0.9349776258427136) assert len(data["nn"]) == 6 assert len(data["nnn"]["corner"]) == 8 assert len(data["nnn"]["edge"]) == 2 assert data["poly_formula"] == "O6" assert data["sym_labels"] == (1,) def test_get_bond_distance_summary(tin_dioxide): """Test getting the bond distance summary.""" sa = SiteAnalyzer(tin_dioxide) data = sa.get_bond_distance_summary(0) assert len(data[2]) == 6 assert data[2][0] == pytest.approx(2.0922101061490546) def test_connectivity_angle_summary(tin_dioxide): """Test getting the connectivity angle summary.""" sa = SiteAnalyzer(tin_dioxide) data = sa.get_connectivity_angle_summary(0) assert len(data[0]["corner"]) == 8 assert len(data[0]["edge"]) == 4 assert data[0]["edge"][0] == pytest.approx(101.62287790513848) def test_nnn_distance_summary(tin_dioxide): """Test getting the nnn distance summary.""" sa = SiteAnalyzer(tin_dioxide) data = sa.get_nnn_distance_summary(0) assert len(data[0]["corner"]) == 8 assert len(data[0]["edge"]) == 2 assert data[0]["edge"][0] == pytest.approx(3.24322132) def test_get_all_site_summaries(tin_dioxide): """Test getting all the site summaries.""" sa = SiteAnalyzer(tin_dioxide) data = sa.get_all_site_summaries() assert len(data.keys()) == 2 assert data[0]["element"] == "Sn4+" assert data[0]["geometry"]["type"] == "octahedral" assert data[0]["geometry"]["likeness"] == pytest.approx(0.9349776258427136) assert len(data[0]["nn"]) == 6 assert len(data[0]["nnn"]["corner"]) == 8 assert len(data[0]["nnn"]["edge"]) == 2 assert data[0]["poly_formula"] == "O6" assert data[0]["sym_labels"] == (1,) def test_get_all_bond_distance_summaries(tin_dioxide): sa = SiteAnalyzer(tin_dioxide) data = sa.get_all_bond_distance_summaries() assert len(data[0][2]) == 6 assert len(data[2][0]) == 3 assert data[0][2][0] == pytest.approx(2.0922101061490546) def test_get_all_connectivity_angle_summaries(tin_dioxide): sa = SiteAnalyzer(tin_dioxide) data = sa.get_all_connectivity_angle_summaries() assert len(data[0][0]["corner"]) == 8 assert len(data[0][0]["edge"]) == 4 assert data[0][0]["edge"][0] == pytest.approx(101.62287790513848) def test_get_all_nnn_distance_summaries(tin_dioxide): sa = SiteAnalyzer(tin_dioxide) data = sa.get_all_nnn_distance_summaries() assert len(data[0][0]["corner"]) == 8 assert len(data[0][0]["edge"]) == 2 assert data[0][0]["edge"][0] == pytest.approx(3.24322132) def test_get_inequivalent_site_indices(ba_n): sa = SiteAnalyzer(ba_n, use_symmetry_equivalent_sites=False) inequiv_indices = sa.get_inequivalent_site_indices(list(range(6))) assert inequiv_indices == [0, 0, 0, 0, 4, 4] # test using symmetry to determine inequivalent sites. sa = SiteAnalyzer(ba_n, use_symmetry_equivalent_sites=True) inequiv_indices = sa.get_inequivalent_site_indices(list(range(6))) assert inequiv_indices == [0, 0, 0, 0, 4, 4] # test symprec option sa = SiteAnalyzer(ba_n, use_symmetry_equivalent_sites=True, symprec=0.000001) inequiv_indices = sa.get_inequivalent_site_indices(list(range(6))) assert inequiv_indices == [0, 1, 1, 0, 4, 4] def test_geometries_match(tin_dioxide): """Test geometry matching function.""" sa = SiteAnalyzer(tin_dioxide, use_symmetry_equivalent_sites=False) geom_a = sa.get_site_geometry(0) geom_b = sa.get_site_geometry(1) geom_c = sa.get_site_geometry(4) assert geometries_match(geom_a, geom_b) assert not geometries_match(geom_a, geom_c) def test_nn_summaries_match(ba_n): """Test nearest neighbour summary matching function.""" sa = SiteAnalyzer(ba_n, use_symmetry_equivalent_sites=True) nn_a = sa.get_nearest_neighbors(0) nn_b = sa.get_nearest_neighbors(1) nn_c = sa.get_nearest_neighbors(4) assert nn_summaries_match(nn_a, nn_b) assert not nn_summaries_match(nn_a, nn_c) # test bond dist tol works assert not nn_summaries_match(nn_a, nn_b, bond_dist_tol=1e-10) # test not matching bond dists assert nn_summaries_match(nn_a, nn_b, bond_dist_tol=1e-10, match_bond_dists=False) def test_nnn_summaries_match(ba_n): """Test nearest neighbour summary matching function.""" sa = SiteAnalyzer(ba_n, use_symmetry_equivalent_sites=True) nnn_a = sa.get_next_nearest_neighbors(0) nnn_b = sa.get_next_nearest_neighbors(3) nnn_c = sa.get_next_nearest_neighbors(4) assert nnn_summaries_match(nnn_a, nnn_b) assert not nnn_summaries_match(nnn_a, nnn_c) # test not matching bond angles assert nnn_summaries_match( nnn_a, nnn_b, bond_angle_tol=1e-10, match_bond_angles=False ) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8308017 emmet_core-0.87.2/tests/featurization/robocrys/condense/test_structure.py0000644000175100017510000001020215240717720026534 0ustar00runnerrunnerimport pytest from emmet.core.featurization.robocrys.condense.condenser import StructureCondenser @pytest.fixture() def tin_dioxide(test_structures): return test_structures["SnO2"].copy() def test_init(): sc = StructureCondenser() assert sc is not None def test_condense_structure_default(tin_dioxide): """Test structure condensing.""" sc = StructureCondenser() data = sc.condense_structure(tin_dioxide) assert data["mineral"]["type"] == "Rutile" assert data["mineral"]["simplified"] is False assert data["mineral"]["n_species_type_match"] is True assert data["mineral"]["distance"] == -1 assert data["spg_symbol"] == "P4_2/mnm" assert data["crystal_system"] == "tetragonal" assert data["dimensionality"] == 3 # check the right number of sites and that the site data is correct # for one site assert len(data["sites"].keys()) == 2 assert data["sites"][0]["element"] == "Sn4+" assert data["sites"][0]["geometry"]["type"] == "octahedral" assert data["sites"][0]["geometry"]["likeness"] == pytest.approx(0.9349817375244279) assert len(data["sites"][0]["nn"]) == 6 assert len(data["sites"][0]["nnn"]["corner"]) == 8 assert len(data["sites"][0]["nnn"]["edge"]) == 2 assert data["sites"][0]["poly_formula"] == "O6" assert data["sites"][0]["sym_labels"] == (1,) # check distances assert len(data["distances"][0][2]) == 6 assert len(data["distances"][2][0]) == 3 assert data["distances"][0][2][0] == pytest.approx(2.092210570377848) # check angles assert len(data["angles"][0][0]["corner"]) == 8 assert len(data["angles"][0][0]["edge"]) == 4 assert data["angles"][0][0]["edge"][0] == pytest.approx(101.62284671698572) # check nnn distances assert len(data["nnn_distances"][0][0]["corner"]) == 8 assert len(data["nnn_distances"][0][0]["edge"]) == 2 assert data["nnn_distances"][0][0]["edge"][0] == pytest.approx(3.24322132) # check components assert data["components"][0]["dimensionality"] == 3 assert data["components"][0]["orientation"] is None assert data["components"][0]["formula"] == "SnO2" assert data["components"][0]["molecule_name"] is None assert data["components"][0]["sites"] == [0, 0, 2, 2, 2, 2] assert data["component_makeup"] == [0] # check vdw heterostructure information doesn't exist assert data["vdw_heterostructure_info"] is None def test_condense_structure_sym(tin_dioxide): """Test nothing changes when we use symmetry to reduce components.""" sc = StructureCondenser(use_symmetry_equivalent_sites=True) data = sc.condense_structure(tin_dioxide) # check the right number of sites and that the site data is correct # for one site assert len(data["sites"].keys()) == 2 assert data["sites"][0]["element"] == "Sn4+" assert data["sites"][0]["geometry"]["type"] == "octahedral" assert data["sites"][0]["geometry"]["likeness"] == pytest.approx(0.9349817375244279) assert len(data["sites"][0]["nn"]) == 6 assert len(data["sites"][0]["nnn"]["corner"]) == 8 assert len(data["sites"][0]["nnn"]["edge"]) == 2 assert data["sites"][0]["poly_formula"] == "O6" assert data["sites"][0]["sym_labels"] == (1,) # check distances assert len(data["distances"][0][2]) == 6 assert len(data["distances"][2][0]) == 3 assert data["distances"][0][2][0] == pytest.approx(2.092210570377848) # check angles assert len(data["angles"][0][0]["corner"]) == 8 assert len(data["angles"][0][0]["edge"]) == 4 assert data["angles"][0][0]["edge"][0] == pytest.approx(101.62284671698572) # check nnn distances assert len(data["nnn_distances"][0][0]["corner"]) == 8 assert len(data["nnn_distances"][0][0]["edge"]) == 2 assert data["nnn_distances"][0][0]["edge"][0] == pytest.approx(3.24322132) # check components assert data["components"][0]["dimensionality"] == 3 assert data["components"][0]["orientation"] is None assert data["components"][0]["formula"] == "SnO2" assert data["components"][0]["molecule_name"] is None assert data["components"][0]["sites"] == [0, 0, 2, 2, 2, 2] assert data["component_makeup"] == [0] ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8308017 emmet_core-0.87.2/tests/featurization/robocrys/conftest.py0000644000175100017510000000333415240717720023474 0ustar00runnerrunnerfrom monty.serialization import loadfn from monty.json import MontyDecoder from pathlib import Path from emmet.core.io.pymatgen import Structure import pytest from typing import Any from emmet.core.testing_utils import TEST_FILES_DIR def _load_condensed_structure_json(filename: str | Path) -> dict[str, Any]: """Load condensed structure data from a file. Args: filename: The filename. Returns: The condensed structure data. """ # JSON does not support using integers a dictionary keys, therefore # manually convert dictionary keys from str to int if possible. def json_keys_to_int(x: Any) -> Any: if isinstance(x, dict): return { int(k) if k.isdigit() else k: json_keys_to_int(v) for k, v in x.items() } return x # For some reason, specifying `object_hook = json_keys_to_int` in `loadfn` # doesn't seem to work. This does reliably: return json_keys_to_int(loadfn(filename, cls=MontyDecoder)) _robo_test_dir = TEST_FILES_DIR / "robocrys" @pytest.fixture def robo_test_dir(): return _robo_test_dir _test_structures = {} for file_name in (_robo_test_dir / "structures").glob("*.json.gz"): _test_structures[file_name.name.split(".", 1)[0]] = Structure.from_file(file_name) _test_condensed_structures = {} for file_name in (_robo_test_dir / "condensed_structures").glob("*.json.gz"): _test_condensed_structures[file_name.name.split(".", 1)[0]] = ( _load_condensed_structure_json(file_name) ) @pytest.fixture(autouse=True, scope="module") def test_structures(): return _test_structures @pytest.fixture(autouse=True, scope="module") def test_condensed_structures(): return _test_condensed_structures ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011028.9958305 emmet_core-0.87.2/tests/featurization/robocrys/describe/0000755000175100017510000000000015240717725023057 5ustar00runnerrunner././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8308017 emmet_core-0.87.2/tests/featurization/robocrys/describe/test_description.py0000644000175100017510000000565215240717720027016 0ustar00runnerrunnerimport pytest from emmet.core.featurization.robocrys import StructureDescriber @pytest.fixture def tin_dioxide(test_condensed_structures): return test_condensed_structures["SnO2"].copy() @pytest.fixture def mapi(test_condensed_structures): return test_condensed_structures["mapi"].copy() def test_describe(tin_dioxide): """Broad tests to check the right information is in the description.""" # test general d = StructureDescriber( describe_oxidation_states=True, describe_symmetry_labels=True, return_parts=False, bond_length_decimal_places=2, fmt="raw", ) description = d.describe(tin_dioxide) assert "Rutile" in description assert "SnO2" in description assert "tetragonal" in description assert "P4_2/mnm" in description assert "Sn(1)4+" in description assert "equivalent" in description assert "corner" in description assert "edge" in description assert "Sn(1)-O(1)" in description assert "2.09" in description # test different settings d = StructureDescriber( describe_oxidation_states=False, describe_symmetry_labels=True, return_parts=False, bond_length_decimal_places=4, fmt="raw", ) description = d.describe(tin_dioxide) assert "Sn(1)" in description assert "Sn(1)-O(1)" in description assert "2.0922" in description # test different settings d = StructureDescriber( describe_oxidation_states=True, describe_symmetry_labels=False, return_parts=False, bond_length_decimal_places=2, fmt="latex", ) description = d.describe(tin_dioxide) assert "Sn^{4+}" in description assert "Sn-O" in description # test return parts d = StructureDescriber( describe_oxidation_states=True, describe_symmetry_labels=True, return_parts=True, bond_length_decimal_places=2, fmt="raw", ) description = d.describe(tin_dioxide) assert "Rutile" in description["mineral"] assert "SnO2" in description["mineral"] assert "tetragonal" in description["mineral"] assert "P4_2/mnm" in description["mineral"] assert description["component_makeup"] == "" assert "Sn(1)4+" in description["components"] assert "equivalent" in description["components"] assert "corner" in description["components"] assert "edge" in description["components"] assert "Sn(1)-O(1)" in description["components"] assert "2.09" in description["components"] def test_grammar_and_punctuation(tin_dioxide, mapi): """Check common grammatical errors are not present.""" d = StructureDescriber() description = d.describe(tin_dioxide) assert ".." not in description assert " " not in description assert ". ." not in description description = d.describe(mapi) assert ".." not in description assert " " not in description assert ". ." not in description ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8311274 emmet_core-0.87.2/tests/featurization/robocrys/describe/test_description_adapter.py0000644000175100017510000001121615240717720030507 0ustar00runnerrunnerimport pytest from emmet.core.featurization.robocrys.describe.adapter import DescriptionAdapter @pytest.fixture def tin_dioxide_da(test_condensed_structures): return DescriptionAdapter(test_condensed_structures["SnO2"].copy()) @pytest.fixture def mapi_da(test_condensed_structures): return DescriptionAdapter(test_condensed_structures["mapi"].copy()) def test_attributes(mapi_da): assert mapi_da.mineral["type"] == "Orthorhombic Perovskite" assert mapi_da.mineral["distance"] == -1 assert mapi_da.formula == "CH3NH3PbI3" assert mapi_da.spg_symbol == "Pnma" assert mapi_da.crystal_system == "orthorhombic" assert mapi_da.dimensionality == 3 assert mapi_da.sites assert mapi_da.distances assert mapi_da.angles assert mapi_da.components assert mapi_da.component_makeup assert mapi_da.elements[0] == "H+" assert mapi_da.sym_labels[0] == "(1)" def test_get_nearest_neighbor_details(tin_dioxide_da, mapi_da): """Check getting nearest neighbor summary for all neighbors.""" all_details = tin_dioxide_da.get_nearest_neighbor_details(0) assert len(all_details) == 1 details = all_details[0] assert details.element == "O2-" assert details.count == 6 assert details.sites == [2] assert details.sym_label == "(1)" # test merging of sym labels all_details = mapi_da.get_nearest_neighbor_details(24, group=True) details = all_details[0] assert details.element == "I-" assert details.count == 6 assert details.sites == [32, 36] assert details.sym_label == "(1,2)" def test_get_next_nearest_neighbor_details(tin_dioxide_da): all_details = tin_dioxide_da.get_next_nearest_neighbor_details(0) assert len(all_details) == 2 details = all_details[0] assert details.element == "Sn4+" assert details.count == 8 assert details.sites == [0] assert details.geometry == "octahedral" assert details.connectivity == "corner" assert details.poly_formula == "O6" assert details.sym_label == "(1)" def test_get_distance_details(tin_dioxide_da, mapi_da): # test get distance using int works distances = tin_dioxide_da.get_distance_details(0, 2) assert len(distances), 3 assert distances[0] == pytest.approx(2.0922101061490546) # test get distance using list works distances = tin_dioxide_da.get_distance_details(0, [2]) assert len(distances), 3 assert distances[0] == pytest.approx(2.0922101061490546) # test getting multiple distances distances = mapi_da.get_distance_details(44, [0, 8]) assert len(distances), 4 assert distances[0] == pytest.approx(1.0386222568611572) def test_get_angle_details(tin_dioxide_da): # test get angles using int works distances = tin_dioxide_da.get_angle_details(0, 0, "corner") assert len(distances), 8 assert distances[0] == pytest.approx(129.18849530149342) # test get angles using list works distances = tin_dioxide_da.get_angle_details(0, [0], "corner") assert len(distances), 8 assert distances[0] == pytest.approx(129.18849530149342) def test_get_component_details(mapi_da): """Check getting component details.""" all_details = mapi_da.get_component_details() assert len(all_details) == 2 details = all_details[0] assert details.formula == "CH3NH3" assert details.count == 4 assert details.dimensionality == 0 assert details.molecule_name == "methylammonium" assert details.orientation is None assert details.index == 0 def test_get_component_summary(mapi_da): """Check getting the component summaries.""" all_groups = mapi_da.get_component_groups() assert len(all_groups) == 2 group = all_groups[0] assert group.count == 4 assert group.formula == "CH3NH3" assert group.dimensionality == 0 assert group.molecule_name == "methylammonium" details = group.components[0] assert details.formula == "CH3NH3" assert details.count == 4 assert details.dimensionality == 0 assert details.molecule_name == "methylammonium" assert details.orientation is None assert details.index == 0 def test_component_site_groups(mapi_da): """Check getting the SiteGroups in a component.""" all_groups = mapi_da.get_component_site_groups(1) assert len(all_groups) == 2 group = all_groups[1] assert group.element == "I-" assert group.count == 2 assert group.sites == [32, 36] def test_get_sym_label(mapi_da): """Test getting symmetry labels.""" assert mapi_da.get_sym_label(0) == "(1)" # test using list to get sym label assert mapi_da.get_sym_label([0]) == "(1)" # test combining multiple labels assert mapi_da.get_sym_label([0, 24]) == "(1,1)" ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8311274 emmet_core-0.87.2/tests/featurization/robocrys/test_adapter.py0000644000175100017510000000360315240717720024325 0ustar00runnerrunnerfrom __future__ import annotations import pytest from emmet.core.featurization.robocrys.adapter import BaseAdapter @pytest.fixture def tin_dioxide_ba(test_condensed_structures): return BaseAdapter(test_condensed_structures["SnO2"].copy()) @pytest.fixture def mapi_ba(test_condensed_structures): return BaseAdapter(test_condensed_structures["mapi"]) def test_attributes(mapi_ba): assert mapi_ba.mineral["type"] == "Orthorhombic Perovskite" assert mapi_ba.mineral["distance"] == -1 assert mapi_ba.formula == "CH3NH3PbI3" assert mapi_ba.spg_symbol == "Pnma" assert mapi_ba.crystal_system == "orthorhombic" assert mapi_ba.dimensionality == 3 assert mapi_ba.sites assert mapi_ba.distances assert mapi_ba.angles assert mapi_ba.components assert mapi_ba.component_makeup assert mapi_ba.elements[0] == "H+" def test_get_distance_details(tin_dioxide_ba, mapi_ba): # test get distance using int works distances = tin_dioxide_ba.get_distance_details(0, 2) assert len(distances), 3 assert distances[0] == pytest.approx(2.0922101061490546) # test get distance using list works distances = tin_dioxide_ba.get_distance_details(0, [2]) assert len(distances), 3 assert distances[0] == pytest.approx(2.0922101061490546) # test getting multiple distances distances = mapi_ba.get_distance_details(44, [0, 8]) assert len(distances), 4 assert distances[0] == pytest.approx(1.0386222568611572) def test_get_angle_details(tin_dioxide_ba): # test get angles using int works distances = tin_dioxide_ba.get_angle_details(0, 0, "corner") assert len(distances), 8 assert distances[0] == pytest.approx(129.18849530149342) # test get angles using list works distances = tin_dioxide_ba.get_angle_details(0, [0], "corner") assert len(distances), 8 assert distances[0] == pytest.approx(129.18849530149342) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8311274 emmet_core-0.87.2/tests/featurization/robocrys/test_util.py0000644000175100017510000000653515240717720023671 0ustar00runnerrunnerfrom emmet.core.io.pymatgen import get_el_sp from emmet.core.featurization.robocrys.util import ( get_el, get_formatted_el, htmlify_spacegroup, superscript_number, unicodeify_spacegroup, ) import pytest def test_common_formulas(): from emmet.core.featurization.robocrys.util import common_formulas assert common_formulas["H6CN"] == "CH3NH3" def test_connected_geometries(): from emmet.core.featurization.robocrys.util import connected_geometries assert "octahedral" in connected_geometries def test_geometry_to_polyhedra(): from emmet.core.featurization.robocrys.util import geometry_to_polyhedra assert geometry_to_polyhedra["octahedral"] == "octahedra" def test_polyhedra_plurals(): from emmet.core.featurization.robocrys.util import polyhedra_plurals assert polyhedra_plurals["pentagonal pyramid"] == "pentagonal pyramids" def test_dimensionality_to_shape(): from emmet.core.featurization.robocrys.util import dimensionality_to_shape assert dimensionality_to_shape[3] == "framework" def test_superscript_number(): assert superscript_number("+23") == "⁺²³" def test_get_al(): """Test getting element names.""" species = get_el_sp("Sn2+") assert get_el(species) == "Sn" element = get_el_sp("Sn") assert get_el(element) == "Sn" assert get_el(50) == "Sn" def test_get_formatted_el(): """Test getting formatted element strings.""" species = get_el_sp("Sn2+") form_el = get_formatted_el(species, "") assert form_el == "Sn2+" element = get_el_sp("Sn") form_el = get_formatted_el(element, "") assert form_el == "Sn" form_el = get_formatted_el(get_el(50), "") assert form_el == "Sn" for species, symm, use_oxi, use_sym, fmt, expected_label in [ ("Sn2+", "(1,2)", True, True, "raw", "Sn(1,2)2+"), ("Sn2+", "(1,2)", False, True, "raw", "Sn(1,2)"), ("Sn2+", "(1,2)", True, False, "raw", "Sn2+"), ("Sn2+", "(1,2)", False, False, "raw", "Sn"), ("Sn2+", "(1,2)", True, True, "latex", "Sn(1,2)^{2+}"), ("Sn2+", "(1,2)", True, True, "html", "Sn(1,2)2+"), ("Sn2+", "(1,2)", True, True, "unicode", "Sn(1,2)²⁺"), ("Sn1.33-", "(3)", True, True, "unicode", "Sn(3)¹\u1427³³⁻"), ]: assert ( get_formatted_el( species, symm, use_oxi_state=use_oxi, use_sym_label=use_sym, fmt=fmt ) == expected_label ) @pytest.mark.xfail def test_unicodeify_spacegroup(): spg_symbol = unicodeify_spacegroup("P-42_1m") assert spg_symbol == "P4̅2₁m" spg_symbol2 = unicodeify_spacegroup("P2_12_12_1") assert spg_symbol2 == "P2₁2₁2₁" @pytest.mark.xfail def test_htmlify_spacegroup(): spg_symbol = htmlify_spacegroup("P-42_1m") assert spg_symbol == "P4̅21m" spg_symbol2 = htmlify_spacegroup("P2_12_12_1") assert spg_symbol2 == "P212121" def test_load_condense_structure_json(test_condensed_structures): condensed_structure = test_condensed_structures["SnO2"].copy() # check that the site keys are correctly converted from str to int site_keys = list(condensed_structure["sites"].keys()) assert isinstance(site_keys[0], int) component_keys = list(condensed_structure["components"].keys()) assert isinstance(component_keys[0], int) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8311274 emmet_core-0.87.2/tests/featurization/test_featurizers.py0000644000175100017510000000451615240717720023412 0ustar00runnerrunnerfrom dataclasses import dataclass import gzip import json import numpy as np import pytest from emmet.core.featurization.featurizers import ( FeatureStats, Featurizer, CN_TARGET_MOTIF_OP, CrystalNNFingerprint, SiteStatsFingerprint, ) @pytest.fixture def fingerprint_test_data(test_dir): with gzip.open(test_dir / "featurizers_data.json.gz", "rb") as f: return json.load(f) @dataclass class badClass(Featurizer): def featurize(self): return [1.0] @dataclass class otherBadClass(Featurizer): def feature_labels(self): return ["label"] def test_abc(): for test_cls in (Featurizer, badClass, otherBadClass): with pytest.raises(TypeError, match="Can't instantiate abstract class"): test_cls() def test_stats(): fingerprinter = SiteStatsFingerprint() min_v = 1 max_v = 42.1 test_arr = np.linspace(min_v, max_v, 23) stats = { "mean": 21.55, "minimum": min_v, "maximum": max_v, "std_dev": 12.392116, } assert all( fingerprinter.get_stat(stat, test_arr) == pytest.approx(val, 1e-6) for stat, val in stats.items() ) assert all( fingerprinter.get_stat(FeatureStats(stat), test_arr) == pytest.approx(val, 1e-6) for stat, val in stats.items() ) assert all(FeatureStats(stat) == stat for stat in stats) with pytest.raises(ValueError, match="Unknown operation"): fingerprinter.get_stat("lorem", test_arr) def test_fingerprinters(fingerprint_test_data, mp_149_structure): assert all( isinstance(k, int) and all(isinstance(s, str) for s in v) for k, v in CN_TARGET_MOTIF_OP.items() ) for preset in ("cn", "ops"): cnnf = CrystalNNFingerprint.from_preset(preset) assert ( cnnf.feature_labels == fingerprint_test_data["crystal_nn"][preset]["labels"] ) assert cnnf.featurize(mp_149_structure, 0) == pytest.approx( fingerprint_test_data["crystal_nn"][preset]["features"] ) ssf = SiteStatsFingerprint(site_featurizer=cnnf) assert ( ssf.feature_labels == fingerprint_test_data["site_stats"][preset]["labels"] ) assert ssf.featurize(mp_149_structure) == pytest.approx( fingerprint_test_data["site_stats"][preset]["features"] ) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011028.9961503 emmet_core-0.87.2/tests/io/0000755000175100017510000000000015240717725015160 5ustar00runnerrunner././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8311274 emmet_core-0.87.2/tests/io/__init__.py0000644000175100017510000000000015240717720017252 0ustar00runnerrunner././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8313437 emmet_core-0.87.2/tests/io/test_pymatgen.py0000644000175100017510000000207415240717720020413 0ustar00runnerrunner"""Test that pymatgen-related imports work.""" import pytest from emmet.core.io.pymatgen import ( _class_map, __dir__ as pmg_io_dir, _name_collision_aliases, ) def test_dir(): pmg_list_dir = pmg_io_dir() assert pmg_list_dir == sorted( ".".join(["pymatgen", base_import, _name_collision_aliases.get(name, name)]) for name, base_import in _class_map.items() ) assert all(isinstance(v, str) for v in pmg_list_dir) def test_bad_import(): with pytest.raises(ImportError, match="cannot import name"): from emmet.core.io.pymatgen import foobar # noqa: F401 @pytest.mark.parametrize("object_name", sorted(_class_map)) def test_imports(object_name: str): """Test that all imports defined in the I/O layer work.""" import emmet.core.io.pymatgen as pmg_io_layer try: getattr(pmg_io_layer, object_name) except (ImportError, AttributeError) as exc: import_str = ".".join(["pymatgen", object_name, _class_map[object_name]]) pytest.fail(f"Import of {import_str!r} failed with exception {exc}") ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011028.9962788 emmet_core-0.87.2/tests/mobility/0000755000175100017510000000000015240717725016401 5ustar00runnerrunner././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8313437 emmet_core-0.87.2/tests/mobility/test_migrationgraph.py0000644000175100017510000001454615240717720023032 0ustar00runnerrunnerimport numpy as np import pytest from monty.serialization import loadfn from emmet.core.io.pymatgen import ComputedEntry, StructureMatcher from emmet.core.mobility.migrationgraph import MigrationGraphDoc @pytest.fixture(scope="session") def get_entries(test_dir): entry_Li = ComputedEntry("Li", -1.90753119) entries = loadfn(test_dir / "mobility/LiMnP2O7_batt.json.gz") return (entries, entry_Li) @pytest.fixture(scope="session") def migration_graph_prop(): """ set the expected parameters for the migrationgraph """ expected_properties = { "LiMnP2O7": { "max_distance": 5, "num_uhops": 8, "longest_hop": 4.92647, "shortest_hop": 2.77240, "min_length_sc": 7, "minmax_num_atoms": (80, 160), } } return expected_properties @pytest.fixture(scope="session") def mg_for_sc_fields(test_dir): """ get MigrationGraph object generated with methods from pymatgen.analysis.diffusion for testing generate_sc_fields """ mg_for_sc = loadfn(test_dir / "mobility/mg_for_sc.json.gz") return mg_for_sc @pytest.fixture(scope="session") def match_mgdoc_npr(test_dir): """ get the MigrationGraphDoc and NebPathwayResult objects to test matching functions """ match_mgdoc = loadfn(test_dir / "mobility/test_match_mgdoc.json.gz") match_npr = loadfn(test_dir / "mobility/test_match_npr_doc.json.gz") return (match_mgdoc, match_npr) @pytest.fixture(scope="session") def match_mgd_prop(): """ set the epected parameters for migrationgraph docs after matching with NebPathwayResult """ return {"num_paths": 2, "len_of_paths": [2, 2], "max_costs": [0.82334, 0.82334]} @pytest.fixture(scope="session") def match_mgd_w_cost_prop(): """ set the epected parameters for migrationgraph docs after matching with NebPathwayResult """ return {"costs": [0.82334, 0.03334], "hop_keys": ["0+1", "2+1"]} @pytest.mark.skip( reason="Incompatible with Pymatgen>=2024.9.10, regression testing in progress..." ) def test_from_entries_and_distance(migration_graph_prop, get_entries): for expected in migration_graph_prop.values(): mgdoc = MigrationGraphDoc.from_entries_and_distance( battery_id="mp-1234", grouped_entries=get_entries[0], working_ion_entry=get_entries[1], hop_cutoff=5, populate_sc_fields=True, min_length_sc=7, minmax_num_atoms=(80, 160), ) mg = mgdoc.migration_graph res_d = { "max_distance": mgdoc.hop_cutoff, "num_uhops": len(mg.unique_hops), "longest_hop": sorted( mg.unique_hops.items(), key=lambda x: x[1]["hop_distance"] )[-1][1]["hop_distance"], "shortest_hop": sorted( mg.unique_hops.items(), key=lambda x: x[1]["hop_distance"] )[0][1]["hop_distance"], "min_length_sc": mgdoc.min_length_sc, "minmax_num_atoms": mgdoc.minmax_num_atoms, } for k, v in expected.items(): assert res_d[k] == pytest.approx(v, 0.01) def test_generate_sc_fields(mg_for_sc_fields): sm = StructureMatcher() ( host_sc, sc_mat, min_length, min_max_num_atoms, coords_dict, combo, ) = MigrationGraphDoc.generate_sc_fields( mg_for_sc_fields, 7, (80, 160), sm, "complete" ) sc_mat_inv = np.linalg.inv(sc_mat) expected_sc_list = [] for one_hop in mg_for_sc_fields.unique_hops.values(): host_sc_insert = host_sc.copy() host_sc_insert.insert(0, "Li", np.dot(one_hop["ipos"], sc_mat_inv)) host_sc_insert.insert(0, "Li", np.dot(one_hop["epos"], sc_mat_inv)) expected_sc_list.append(host_sc_insert) for one_combo in combo: hop_sc = host_sc.copy() sc_iindex, sc_eindex = list(map(int, one_combo.split("+"))) sc_isite = coords_dict[sc_iindex]["site_frac_coords"] sc_esite = coords_dict[sc_eindex]["site_frac_coords"] hop_sc.insert(0, "Li", sc_isite) hop_sc.insert(0, "Li", sc_esite) check_sc_list = [sm.fit(hop_sc, check_sc) for check_sc in expected_sc_list] assert sum(check_sc_list) >= 1 @pytest.mark.skip( reason="Incompatible with Pymatgen>=2024.9.10, regression testing in progress..." ) def test_get_distinct_hop_sites(get_entries): mgdoc = MigrationGraphDoc.from_entries_and_distance( battery_id="mp-1234", grouped_entries=get_entries[0], working_ion_entry=get_entries[1], hop_cutoff=5, populate_sc_fields=True, min_length_sc=7, minmax_num_atoms=(80, 160), ) ( dis_sites_list, dis_combo_list, combo_mapping, ) = MigrationGraphDoc.get_distinct_hop_sites( mgdoc.inserted_ion_coords, mgdoc.insert_coords_combo ) for one_test_combo in ["0+1", "0+2", "0+3", "0+4", "0+5", "0+6", "1+7", "1+2"]: assert one_test_combo in dis_combo_list assert combo_mapping == { "0+1": "9+4", "0+2": "9+8", "0+3": "9+6", "0+4": "9+16", "0+5": "9+17", "0+6": "9+13", "1+7": "4+0", "1+2": "4+8", } def test_get_paths_summary_with_neb_res(match_mgdoc_npr, match_mgd_prop): mgdoc, npr = match_mgdoc_npr paths_summary, mg_new = MigrationGraphDoc.get_paths_summary_with_neb_res( mgdoc.migration_graph, npr, "energy_range" ) res_prop = { "num_paths": len(paths_summary), "len_of_paths": [len(v) for v in paths_summary.values()], "max_costs": [max([i.cost for i in path]) for path in paths_summary.values()], } for k, v in match_mgd_prop.items(): assert res_prop[k] == pytest.approx(v, 0.01) def test_augment_from_mgd_and_npr(match_mgdoc_npr, match_mgd_w_cost_prop): mgdoc, npr = match_mgdoc_npr mgd_w_cost = MigrationGraphDoc.augment_from_mgd_and_npr( mgd=mgdoc, npr=npr, barrier_type="energy_range" ) assert mgd_w_cost.migration_graph_w_cost is not None res_uhops = mgd_w_cost.migration_graph_w_cost.unique_hops res_uhops = [v for k, v in sorted(res_uhops.items())] res_prop = { "costs": [uhop["cost"] for uhop in res_uhops], "hop_keys": [uhop["hop_key"] for uhop in res_uhops], } for k, v in match_mgd_w_cost_prop.items(): assert res_prop[k] == pytest.approx(v, 0.01) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011028.9981797 emmet_core-0.87.2/tests/molecules/0000755000175100017510000000000015240717725016541 5ustar00runnerrunner././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8313437 emmet_core-0.87.2/tests/molecules/__init__.py0000644000175100017510000000000015240717720020633 0ustar00runnerrunner././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8315196 emmet_core-0.87.2/tests/molecules/conftest.py0000644000175100017510000000113515240717720020733 0ustar00runnerrunnerfrom datetime import datetime from monty.serialization import loadfn import pytest from emmet.core.qchem.task import TaskDocument @pytest.fixture(scope="session") def open_shell_nbo_task(test_dir): return TaskDocument(**loadfn(test_dir / "open_shell_nbo_task.json.gz")) @pytest.fixture(scope="session") def liec_tasks(test_dir): data = loadfn(test_dir / "liec_tasks.json.gz", cls=None) for d in data: d["last_updated"] = datetime.strptime( d["last_updated"]["string"], "%Y-%m-%d %H:%M:%S.%f" ) tasks = [TaskDocument(**t) for t in data] return tasks ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8315196 emmet_core-0.87.2/tests/molecules/test_atomic.py0000644000175100017510000000465015240717720021426 0ustar00runnerrunnerfrom emmet.core.molecules.atomic import PartialChargesDoc, PartialSpinsDoc from tests.conftest_qchem import OPENBABEL_INSTALLED def test_partial_charges(liec_tasks): # Test RESP pcd = PartialChargesDoc.from_task( liec_tasks[0], molecule_id="b9ba54febc77d2a9177accf4605767db-C1Li2O3-1-2", preferred_methods=["resp"], ) assert pcd.property_name == "partial_charges" assert pcd.method == "resp" assert pcd.partial_charges == liec_tasks[0].output.resp # Test Mulliken pcd = PartialChargesDoc.from_task( liec_tasks[0], molecule_id="b9ba54febc77d2a9177accf4605767db-C1Li2O3-1-2", preferred_methods=["mulliken"], ) assert pcd.method == "mulliken" assert pcd.partial_charges == liec_tasks[0].output.mulliken # Test Critic2 pcd = PartialChargesDoc.from_task( liec_tasks[3], molecule_id="b9ba54febc77d2a9177accf4605767db-C1Li2O3-1-2", preferred_methods=["critic2"], ) assert pcd.method == "critic2" assert pcd.partial_charges == liec_tasks[3].critic2["processed"]["charges"] # Test NBO pcd = PartialChargesDoc.from_task( liec_tasks[4], molecule_id="b9ba54febc77d2a9177accf4605767db-C1Li2O3-1-2", preferred_methods=["nbo"], ) assert pcd.method == "nbo" nbo_charges = [ float(liec_tasks[4].output.nbo["natural_populations"][0]["Charge"][str(i)]) for i in range(11) ] assert pcd.partial_charges == nbo_charges def test_partial_spins(open_shell_nbo_task): # Test Mulliken psd = PartialSpinsDoc.from_task( open_shell_nbo_task, molecule_id="b9ba54febc77d2a9177accf4605767db-C1Li2O3-1-2", preferred_methods=["mulliken"], ) assert psd.property_name == "partial_spins" assert psd.method == "mulliken" assert psd.partial_spins == [m[1] for m in open_shell_nbo_task.output.mulliken] # Test NBO psd = PartialSpinsDoc.from_task( open_shell_nbo_task, molecule_id="b9ba54febc77d2a9177accf4605767db-C1Li2O3-1-2", preferred_methods=["nbo"], ) assert psd.method == "nbo" spins = [ float( open_shell_nbo_task.output.nbo["natural_populations"][0]["Density"][str(i)] ) for i in range(11) ] assert psd.partial_spins == spins if OPENBABEL_INSTALLED: # Sanity check - do properties have hashes assert psd.species_hash is not None ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8315196 emmet_core-0.87.2/tests/molecules/test_bonds.py0000644000175100017510000000310315240717720021247 0ustar00runnerrunnerimport pytest from emmet.core.molecules.bonds import MoleculeBondingDoc from tests.conftest_qchem import OPENBABEL_INSTALLED @pytest.mark.skipif( not OPENBABEL_INSTALLED, reason="openbabel must be installed to test bonding analysis.", ) def test_bonding(liec_tasks, open_shell_nbo_task): # No Critic2 or NBO ob_mee = MoleculeBondingDoc.from_task( liec_tasks[0], molecule_id="b9ba54febc77d2a9177accf4605767db-C1Li2O3-1-2", preferred_methods=["OpenBabelNN + metal_edge_extender"], ) assert ob_mee.property_name == "bonding" assert ob_mee.method == "OpenBabelNN + metal_edge_extender" assert len(ob_mee.bonds) == 12 assert len(ob_mee.bonds_nometal) == 10 assert set(ob_mee.bond_types.keys()) == {"C-C", "C-H", "C-O", "Li-O"} ob_critic = MoleculeBondingDoc.from_task( liec_tasks[3], molecule_id="b9ba54febc77d2a9177accf4605767db-C1Li2O3-1-2", preferred_methods=["critic2"], ) assert ob_critic.method == "critic2" assert len(ob_critic.bonds) == 12 assert len(ob_critic.bonds_nometal) == 10 assert set(ob_critic.bond_types.keys()) == {"C-C", "C-H", "C-O", "Li-O"} assert ob_mee.molecule_graph.isomorphic_to(ob_critic.molecule_graph) nbo = MoleculeBondingDoc.from_task( open_shell_nbo_task, molecule_id="b9ba54febc77d2a9177accf4605767db-C1Li2O3-1-2", preferred_methods=["nbo"], ) assert nbo.method == "nbo" assert len(nbo.bonds) == 11 assert len(nbo.bonds_nometal) == 9 assert set(nbo.bond_types.keys()) == {"C-H", "C-O", "C-Li", "Li-O"} ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8315196 emmet_core-0.87.2/tests/molecules/test_electric.py0000644000175100017510000000475515240717720021752 0ustar00runnerrunnerimport json import datetime import pytest from monty.io import zopen from emmet.core.qchem.task import TaskDocument from emmet.core.molecules.electric import ElectricMultipoleDoc __author__ = "Evan Spotte-Smith " @pytest.fixture(scope="session") def test_tasks(test_dir): with zopen(test_dir / "multipole_docs.json.gz", "rt") as f: data = json.load(f) for d in data: d["last_updated"] = datetime.datetime.strptime( d["last_updated"]["string"], "%Y-%m-%d %H:%M:%S.%f" ) tasks = [TaskDocument(**t) for t in data] return tasks def test_electric_multipole(test_tasks): # First, test from SP sp_doc = ElectricMultipoleDoc.from_task( test_tasks[0], molecule_id="b9ba54febc77d2a9177accf4605767db-C1Li2O3-1-2" ) assert sp_doc.property_name == "multipole_moments" assert sp_doc.total_dipole == pytest.approx(4.2371) assert sp_doc.dipole_moment[0] == pytest.approx(-3.2443) assert sp_doc.dipole_moment[1] == pytest.approx(-2.4294) assert sp_doc.dipole_moment[2] == pytest.approx(-1.2352) assert sp_doc.resp_total_dipole == pytest.approx(4.2845) assert len(sp_doc.quadrupole_moment) == 6 assert len(sp_doc.octopole_moment) == 10 assert len(sp_doc.hexadecapole_moment) == 15 assert sp_doc.quadrupole_moment["XX"] == pytest.approx(-55.6651) assert sp_doc.octopole_moment["XXZ"] == pytest.approx(9.7933) assert sp_doc.hexadecapole_moment["YYZZ"] == pytest.approx(-78.2281) # Test from force calc force_doc = ElectricMultipoleDoc.from_task( test_tasks[1], molecule_id="b9ba54febc77d2a9177accf4605767db-C1Li2O3-1-2" ) assert force_doc.total_dipole == pytest.approx(6.562) assert force_doc.dipole_moment[0] == pytest.approx(5.4663) # Test from FFOpt ffopt_doc = ElectricMultipoleDoc.from_task( test_tasks[2], molecule_id="b9ba54febc77d2a9177accf4605767db-C1Li2O3-1-2" ) assert ffopt_doc.quadrupole_moment["XZ"] == pytest.approx(0.1372) # Test from opt opt_doc = ElectricMultipoleDoc.from_task( test_tasks[3], molecule_id="b9ba54febc77d2a9177accf4605767db-C1Li2O3-1-2" ) assert opt_doc.octopole_moment["YZZ"] == pytest.approx(35.289) assert opt_doc.total_dipole == ffopt_doc.total_dipole # Test from freq freq_doc = ElectricMultipoleDoc.from_task( test_tasks[4], molecule_id="b9ba54febc77d2a9177accf4605767db-C1Li2O3-1-2" ) assert freq_doc.hexadecapole_moment["ZZZZ"] == pytest.approx(-366.0089) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8315196 emmet_core-0.87.2/tests/molecules/test_metal_binding.py0000644000175100017510000000545615240717720022753 0ustar00runnerrunnerimport pytest from monty.serialization import loadfn from emmet.core.qchem.molecule import MoleculeDoc from emmet.core.molecules.atomic import PartialChargesDoc, PartialSpinsDoc from emmet.core.molecules.bonds import MoleculeBondingDoc from emmet.core.molecules.thermo import MoleculeThermoDoc from emmet.core.molecules.metal_binding import MetalBindingDoc @pytest.fixture(scope="session") def base_mol(test_dir): mol = loadfn((test_dir / "metal_binding_doc" / "base_mol_doc.json.gz").as_posix()) mol_doc = MoleculeDoc(**mol) return mol_doc @pytest.fixture(scope="session") def base_thermo(test_dir): thermo = loadfn((test_dir / "metal_binding_doc" / "thermo.json.gz").as_posix()) thermo_doc = MoleculeThermoDoc(**thermo) return thermo_doc @pytest.fixture(scope="session") def charges(test_dir): charges = loadfn( (test_dir / "metal_binding_doc" / "partial_charges.json.gz").as_posix() ) charges_doc = PartialChargesDoc(**charges) return charges_doc @pytest.fixture(scope="session") def spins(test_dir): spins = loadfn( (test_dir / "metal_binding_doc" / "partial_spins.json.gz").as_posix() ) spins_doc = PartialSpinsDoc(**spins) return spins_doc @pytest.fixture(scope="session") def bonds(test_dir): bonds = loadfn((test_dir / "metal_binding_doc" / "bonds.json.gz").as_posix()) bonds_doc = MoleculeBondingDoc(**bonds) return bonds_doc @pytest.fixture(scope="session") def metal_thermo(test_dir): thermo = loadfn( (test_dir / "metal_binding_doc" / "metal_thermo.json.gz").as_posix() ) metal_thermo_doc = MoleculeThermoDoc(**thermo) return metal_thermo_doc @pytest.fixture(scope="session") def nometal_thermo(test_dir): thermo = loadfn( (test_dir / "metal_binding_doc" / "nometal_thermo.json.gz").as_posix() ) nometal_thermo_doc = MoleculeThermoDoc(**thermo) return nometal_thermo_doc def test_metal_binding( base_mol, charges, spins, bonds, base_thermo, metal_thermo, nometal_thermo ): metal_binding = MetalBindingDoc.from_docs( method="mulliken-OB-mee", metal_indices=[5], base_molecule_doc=base_mol, partial_charges=charges, partial_spins=spins, bonding=bonds, base_thermo=base_thermo, metal_thermo={5: metal_thermo}, nometal_thermo={5: nometal_thermo}, ) assert metal_binding.binding_data[0].metal_index == 5 assert metal_binding.binding_data[0].metal_element == "Li" assert metal_binding.binding_data[0].metal_partial_charge == pytest.approx( -0.073376 ) assert metal_binding.binding_data[0].metal_assigned_charge == 0 assert metal_binding.binding_data[0].metal_assigned_spin == 2 assert metal_binding.binding_data[0].binding_free_energy == pytest.approx( 1.23790290567376 ) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8315196 emmet_core-0.87.2/tests/molecules/test_mol_thermo.py0000644000175100017510000000265115240717720022316 0ustar00runnerrunnerimport pytest from monty.serialization import loadfn from emmet.core.qchem.task import TaskDocument from emmet.core.molecules.thermo import MoleculeThermoDoc, get_free_energy @pytest.fixture(scope="session") def sp(test_dir): data = loadfn((test_dir / "closed_shell_nbo_task.json.gz").as_posix()) task = TaskDocument(**data) return task def test_thermo(liec_tasks, sp): # Just energy; no free energy information doc = MoleculeThermoDoc.from_task( task=sp, molecule_id="b9ba54febc77d2a9177accf4605767db-C1Li2O3-1-2", deprecated=False, ) assert doc.property_name == "thermo" assert doc.electronic_energy == sp.output.final_energy * 27.2114 assert doc.free_energy is None # With all thermodynamic information task = liec_tasks[0] doc = MoleculeThermoDoc.from_task( task, molecule_id="b9ba54febc77d2a9177accf4605767db-C1Li2O3-1-2", deprecated=False, ) assert doc.electronic_energy == task.output.final_energy * 27.2114 assert doc.total_enthalpy == task.output.enthalpy * 0.043363 assert doc.total_entropy == task.output.entropy * 0.000043363 assert doc.free_energy == get_free_energy( task.output.final_energy, task.output.enthalpy, task.output.entropy ) assert doc.rt == task.calcs_reversed[0]["gas_constant"] * 0.043363 assert doc.vibrational_enthalpy == task.calcs_reversed[0]["vib_enthalpy"] * 0.043363 ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8315196 emmet_core-0.87.2/tests/molecules/test_orbitals.py0000644000175100017510000000614215240717720021767 0ustar00runnerrunnerimport pytest from emmet.core.qchem.task import TaskDocument from emmet.core.molecules.orbitals import OrbitalDoc from tests.conftest_qchem import safe_load @pytest.fixture(scope="session") def closed_shell(test_dir): task = TaskDocument(**safe_load((test_dir / "closed_shell_nbo_task.json.gz"))) return task @pytest.fixture(scope="session") def new_closed(test_dir): task = TaskDocument(**safe_load((test_dir / "closed_shell_with3c.json.gz"))) return task @pytest.fixture(scope="session") def new_open(test_dir): task = TaskDocument(**safe_load((test_dir / "open_shell_with3c.json.gz"))) return task def test_orbital(closed_shell, open_shell_nbo_task): # Test closed-shell NBO parsing doc = OrbitalDoc.from_task( closed_shell, "b9ba54febc77d2a9177accf4605767db-C1Li2O3-1-2", deprecated=False ) assert doc.property_name == "natural bonding orbitals" assert doc.open_shell is False assert len(doc.nbo_population) == len(closed_shell.output.initial_molecule) assert doc.nbo_population[0].valence_electrons == pytest.approx(2.75426) assert len(doc.nbo_lone_pairs) == 3 assert doc.nbo_lone_pairs[0].s_character == pytest.approx(0.02) assert doc.nbo_lone_pairs[0].atom_index == 0 assert len(doc.nbo_bonds) == 10 assert doc.nbo_bonds[0].atom1_s_character == pytest.approx(29.93) assert doc.nbo_bonds[0].atom1_index == 0 assert doc.nbo_bonds[0].atom2_index == 3 assert len(doc.nbo_interactions) == 95 assert doc.nbo_interactions[0].donor_index == 8 assert doc.nbo_interactions[0].acceptor_index == 19 assert doc.nbo_interactions[0].energy_difference == pytest.approx(0.95) assert doc.alpha_population is None assert doc.beta_population is None # Test open-shell NBO parsing doc = OrbitalDoc.from_task( open_shell_nbo_task, "b9ba54febc77d2a9177accf4605767db-C1Li2O3-1-2", deprecated=False, ) assert doc.open_shell is True assert len(doc.nbo_population) == len(open_shell_nbo_task.output.initial_molecule) assert doc.alpha_population is not None assert doc.beta_population is not None assert doc.nbo_lone_pairs is None assert doc.nbo_bonds is None def test_new_parser(new_closed, new_open): # Test closed-shell parsing including 3-center bonds and hyperbonds cs_doc = OrbitalDoc.from_task( new_closed, "b9ba54febc77d2a9177accf4605767db-C1Li2O3-1-2", deprecated=False ) assert cs_doc.open_shell is False assert len(cs_doc.nbo_three_center_bonds) == 3 assert cs_doc.nbo_three_center_bonds[1].atom1_polarization == pytest.approx(34.47) assert cs_doc.nbo_hyperbonds[2].hybrid_index_2 == 184 # Test open-shell parsing including 3-center bonds and hyperbonds os_doc = OrbitalDoc.from_task( new_open, "b9ba54febc77d2a9177accf4605767db-C1Li2O3-1-2", deprecated=False ) assert os_doc.open_shell is True assert len(os_doc.alpha_three_center_bonds) == 3 assert os_doc.alpha_three_center_bonds[0].atom3_index == 14 assert len(os_doc.beta_hyperbonds) == 2 assert os_doc.beta_hyperbonds[0].fraction_12 == pytest.approx(41.2) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8315196 emmet_core-0.87.2/tests/molecules/test_redox.py0000644000175100017510000000403315240717720021266 0ustar00runnerrunnerimport pytest from emmet.core.qchem.task import TaskDocument from emmet.core.qchem.molecule import MoleculeDoc from emmet.core.molecules.thermo import MoleculeThermoDoc from emmet.core.molecules.redox import RedoxDoc from tests.conftest_qchem import safe_load @pytest.fixture(scope="session") def base_mol(test_dir): mol = safe_load((test_dir / "redox_doc" / "base_mol.json.gz").as_posix()) mol_doc = MoleculeDoc(**mol) return mol_doc @pytest.fixture(scope="session") def base_thermo(test_dir): thermo = safe_load((test_dir / "redox_doc" / "thermo.json.gz").as_posix()) thermo_doc = MoleculeThermoDoc(**thermo) return thermo_doc @pytest.fixture(scope="session") def red_thermo(test_dir): thermo = safe_load((test_dir / "redox_doc" / "red_thermo.json.gz").as_posix()) thermo_doc = MoleculeThermoDoc(**thermo) return thermo_doc @pytest.fixture(scope="session") def ox_thermo(test_dir): thermo = safe_load((test_dir / "redox_doc" / "ox_thermo.json.gz").as_posix()) thermo_doc = MoleculeThermoDoc(**thermo) return thermo_doc @pytest.fixture(scope="session") def ie_task(test_dir): task = safe_load((test_dir / "redox_doc" / "ie_task.json.gz").as_posix()) task_doc = TaskDocument(**task) return task_doc @pytest.fixture(scope="session") def ea_task(test_dir): task = safe_load((test_dir / "redox_doc" / "ea_task.json.gz").as_posix()) task_doc = TaskDocument(**task) return task_doc def test_redox(base_mol, base_thermo, red_thermo, ox_thermo, ie_task, ea_task): redox_doc = RedoxDoc.from_docs( base_molecule_doc=base_mol, base_thermo_doc=base_thermo, red_doc=red_thermo, ox_doc=ox_thermo, ea_doc=ea_task, ie_doc=ie_task, ) assert redox_doc.electron_affinity == pytest.approx(-3.3024638499209686) assert redox_doc.ionization_energy == pytest.approx(4.903294672107222) assert redox_doc.oxidation_free_energy == pytest.approx(3.9880055108133092) assert redox_doc.reduction_free_energy == pytest.approx(-4.237271030198826) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8315196 emmet_core-0.87.2/tests/molecules/test_summary.py0000644000175100017510000000171515240717720021646 0ustar00runnerrunnerimport pytest from monty.serialization import loadfn from emmet.core.molecules.summary import MoleculeSummaryDoc @pytest.fixture(scope="session") def docs_data(test_dir): raw = loadfn(test_dir / "3dfd058d8b422143d386d1ec9a723987-C1Li1O3-m1-1.json.gz") return raw def test_summary_doc(docs_data): # Reformatting because summary build process changed actual_docs = dict() for k, v in docs_data.items(): if k == "molecules": actual_docs[k] = v else: actual_docs[k] = v[0] summary_doc = MoleculeSummaryDoc.from_docs( molecule_id="3dfd058d8b422143d386d1ec9a723987-C1Li1O3-m1-1", docs=actual_docs ) assert summary_doc.property_name == "summary" assert len(summary_doc.bonding) > 0 assert len(summary_doc.thermo) > 0 assert len(summary_doc.vibration) > 0 assert len(summary_doc.redox) > 0 assert len(summary_doc.orbitals) == 0 assert len(summary_doc.metal_binding) == 0 ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8315196 emmet_core-0.87.2/tests/molecules/test_trajectory.py0000644000175100017510000000625515240717720022343 0ustar00runnerrunnerimport json import copy import datetime import pytest import numpy as np from monty.io import zopen from emmet.core.io.pymatgen import Molecule from emmet.core.qchem.task import TaskDocument from emmet.core.molecules.trajectory import ForcesDoc, TrajectoryDoc @pytest.fixture(scope="session") def force_calc(test_dir): with zopen(test_dir / "force_trajectory" / "force_calc.json.gz", "rt") as f: data = json.load(f) data["last_updated"] = datetime.datetime.now() task = TaskDocument(**data) return task @pytest.fixture(scope="session") def ffopt_task(test_dir): with zopen(test_dir / "force_trajectory" / "ffopt_calc.json.gz", "rt") as f: data = json.load(f) data["last_updated"] = datetime.datetime.now() task = TaskDocument(**data) return task def test_forces(force_calc): doc = ForcesDoc.from_task( force_calc, molecule_id="b9ba54febc77d2a9177accf4605767db-C1Li2O3-1-2" ) assert doc.property_name == "forces" assert len(doc.forces) == 11 assert np.allclose(np.array(doc.forces), np.array(doc.precise_forces), atol=0.001) assert doc.cds_forces is None assert doc.average_force_magnitude == pytest.approx(0.03861184536372114) assert doc.max_force_magnitude == pytest.approx(0.07195037119572352) assert doc.min_force_magnitude == pytest.approx(0.003093974384847178) def test_trajectory(ffopt_task): doc = TrajectoryDoc.from_task( ffopt_task, molecule_id="b9ba54febc77d2a9177accf4605767db-C1Li2O3-1-2" ) assert doc.property_name == "optimization_trajectory" assert doc.num_trajectories == 2 assert len(doc.geometries) == doc.num_trajectories assert len(doc.energies) == doc.num_trajectories assert len(doc.forces) == doc.num_trajectories assert doc.species == ["C", "C", "C", "O", "C", "N", "O", "N", "N", "H", "H", "H"] assert len(doc.geometries[0]) == 13 assert doc.energies[0][0] == pytest.approx(-468.1895254889) mol_objects = doc.molecules assert isinstance(mol_objects[0][0], Molecule) assert len(mol_objects) == doc.num_trajectories traj_objects = doc.as_trajectories() assert len(traj_objects) == 2 def test_missing_trajectory(ffopt_task): # Mess with one of the trajectories, removing gradients missing_grad = copy.deepcopy(ffopt_task) missing_grad.calcs_reversed[-1]["gradients"] = list() doc = TrajectoryDoc.from_task( missing_grad, molecule_id="b9ba54febc77d2a9177accf4605767db-C1Li2O3-1-2" ) # The trajectory with missing gradients should not be included assert doc.num_trajectories == 1 # Likewise, if there are missing geometries missing_geom = copy.deepcopy(ffopt_task) missing_geom.calcs_reversed[-1]["geometries"] = list() doc = TrajectoryDoc.from_task( missing_geom, molecule_id="b9ba54febc77d2a9177accf4605767db-C1Li2O3-1-2" ) assert doc.num_trajectories == 1 # And missing energies missing_energies = copy.deepcopy(ffopt_task) missing_energies.calcs_reversed[-1]["energy_trajectory"] = None doc = TrajectoryDoc.from_task( missing_energies, molecule_id="b9ba54febc77d2a9177accf4605767db-C1Li2O3-1-2" ) assert doc.num_trajectories == 1 ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8315196 emmet_core-0.87.2/tests/molecules/test_vibration.py0000644000175100017510000000240715240717720022145 0ustar00runnerrunnerimport json import datetime import pytest from monty.io import zopen from emmet.core.qchem.task import TaskDocument from emmet.core.molecules.vibration import VibrationDoc @pytest.fixture(scope="session") def raman_task(test_dir): with zopen(test_dir / "raman_task.json.gz", "rt") as f: data = json.load(f) data["last_updated"] = datetime.datetime.strptime( data["last_updated"]["string"], "%Y-%m-%d %H:%M:%S.%f" ) task = TaskDocument(**data) return task def test_vibration(liec_tasks): task = liec_tasks[0] vib_doc = VibrationDoc.from_task( task, molecule_id="b9ba54febc77d2a9177accf4605767db-C1Li2O3-1-2" ) assert vib_doc.property_name == "vibrations" assert len(vib_doc.frequencies) == 27 assert len(vib_doc.frequency_modes) == 27 assert len(vib_doc.ir_intensities) == 27 assert vib_doc.frequencies[0] == pytest.approx(49.47) assert vib_doc.ir_intensities[0] == pytest.approx(93.886) assert vib_doc.ir_activities[0] is True def test_raman(raman_task): vib_doc = VibrationDoc.from_task( raman_task, molecule_id="b9ba54febc77d2a9177accf4605767db-C1Li2O3-1-2" ) assert vib_doc.raman_activities is not None assert vib_doc.raman_intensities[0] == pytest.approx(0.424) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011028.9984899 emmet_core-0.87.2/tests/openmm_md/0000755000175100017510000000000015240717725016524 5ustar00runnerrunner././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8315196 emmet_core-0.87.2/tests/openmm_md/__init__.py0000644000175100017510000000000015240717720020616 0ustar00runnerrunner././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8319974 emmet_core-0.87.2/tests/openmm_md/test_tasks.py0000644000175100017510000000270615240717720021262 0ustar00runnerrunnerfrom pathlib import Path import numpy as np from emmet.core.openmm.tasks import CalculationOutput def test_calc_output_from_directory(test_dir): output_dir = test_dir / "openmm" / "calc_output" # Call the from_directory function calc_out = CalculationOutput.from_directory( output_dir, "state.csv", "trajectory.dcd", elapsed_time=10.0, ) # Assert the expected attributes of the CalculationOutput object assert isinstance(calc_out, CalculationOutput) assert calc_out.dir_name == str(output_dir) assert calc_out.elapsed_time == 10.0 assert calc_out.traj_file == "trajectory.dcd" assert calc_out.state_file == "state.csv" # Assert the contents of the state data assert np.array_equal(calc_out.steps_reported[:3], [100, 200, 300]) assert np.allclose(calc_out.potential_energy[:3], [-26192.4, -25648.6, -25149.6]) assert np.allclose(calc_out.kinetic_energy[:3], [609.4, 1110.4, 1576.4], atol=0.1) assert np.allclose(calc_out.total_energy[:3], [-25583.1, -24538.1, -23573.2]) assert np.allclose(calc_out.temperature[:3], [29.6, 54.0, 76.6], atol=0.1) assert np.allclose(calc_out.volume[:3], [21.9, 21.9, 21.9], atol=0.1) assert np.allclose(calc_out.density[:3], [1.0, 0.99, 0.99], atol=0.1) # Assert the existence of the DCD and state files assert Path(calc_out.dir_name, calc_out.traj_file).exists() assert Path(calc_out.dir_name, calc_out.state_file).exists() ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011028.9991102 emmet_core-0.87.2/tests/qchem/0000755000175100017510000000000015240717725015646 5ustar00runnerrunner././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8319974 emmet_core-0.87.2/tests/qchem/__init__.py0000644000175100017510000000000015240717720017740 0ustar00runnerrunner././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8320873 emmet_core-0.87.2/tests/qchem/test_calc_types.py0000644000175100017510000000125015240717720021376 0ustar00runnerrunnerfrom emmet.core.qchem.calc_types.calc_types import ( FUNCTIONALS, BASIS_SETS, SOLVENT_MODELS, TASK_TYPES, ) _REFERENCE_MEMBER_COUNT = { "LevelOfTheory": len(FUNCTIONALS) * len(BASIS_SETS) * len(SOLVENT_MODELS), "TaskType": len(TASK_TYPES), } _REFERENCE_MEMBER_COUNT["CalcType"] = ( _REFERENCE_MEMBER_COUNT["LevelOfTheory"] * _REFERENCE_MEMBER_COUNT["TaskType"] ) def test_enums(): from emmet.core.qchem.calc_types import enums as qchem_enums for k, count in _REFERENCE_MEMBER_COUNT.items(): # Test that we have expected number of enum members curr_enum = getattr(qchem_enums, k, None) assert len(curr_enum) == count ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8320873 emmet_core-0.87.2/tests/qchem/test_molecules.py0000644000175100017510000000335715240717720021252 0ustar00runnerrunnerimport pytest from emmet.core.qchem.calc_types import TaskType from emmet.core.qchem.molecule import MoleculeDoc try: from openbabel.openbabel import OBAlign _ = OBAlign() has_eigen = True except ImportError: has_eigen = False @pytest.mark.skip(reason="Pymatgen OBAlign needs fix") @pytest.mark.skipif( not has_eigen, reason="OBAlign missing, presumably due to lack of Eigen" ) def test_make_mol(liec_tasks): molecule = MoleculeDoc.from_tasks(liec_tasks) assert molecule.formula_alphabetical == "C3 H4 Li1 O3" assert len(molecule.task_ids) == 5 assert len(molecule.entries) == 5 assert molecule.coord_hash == "4cbc38414f4e0e809d53d6dc34ef0be4" bad_task_group = [ task for task in liec_tasks if task.task_type not in [ TaskType.Geometry_Optimization, TaskType.Frequency_Flattening_Geometry_Optimization, ] ] with pytest.raises(Exception): MoleculeDoc.from_tasks(bad_task_group) @pytest.mark.skip(reason="Pymatgen OBAlign needs fix") @pytest.mark.skipif( not has_eigen, reason="OBAlign missing, presumably due to lack of Eigen" ) def test_make_deprecated_mol(liec_tasks): bad_task_group = [ task for task in liec_tasks if task.task_type not in [ TaskType.Geometry_Optimization, TaskType.Frequency_Flattening_Geometry_Optimization, ] ] molecule = MoleculeDoc.construct_deprecated_molecule(bad_task_group) assert molecule.deprecated assert molecule.formula_alphabetical == "C3 H4 Li1 O3" assert len(molecule.task_ids) == 4 assert molecule.entries is None assert molecule.species_hash is not None def test_schema(): MoleculeDoc.schema() ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8320873 emmet_core-0.87.2/tests/qchem/test_qchem.py0000644000175100017510000001325315240717720020353 0ustar00runnerrunnerimport pytest from monty.serialization import loadfn from emmet.core.qchem.calc_types import ( LevelOfTheory, TaskType, level_of_theory, task_type, solvent, lot_solvent_string, ) from emmet.core.qchem.task import TaskDocument from emmet.core.mpid import MPID def test_task_type(): task_types = [ "Single Point", "Geometry Optimization", "Frequency Analysis", "Frequency Flattening Geometry Optimization", "Transition State Geometry Optimization", "Frequency Flattening Transition State Geometry Optimization", ] inputs = [ {"rem": {"job_type": "sp"}}, {"rem": {"job_type": "opt"}}, {"rem": {"job_type": "freq"}}, {"rem": {"job_type": "opt"}}, {"rem": {"job_type": "ts"}}, {"rem": {"job_type": "freq"}}, ] special_run_types = [ None, None, None, "frequency_flattener", None, "ts_frequency_flattener", ] for _type, orig, special in zip(task_types, inputs, special_run_types): assert task_type(orig, special_run_type=special) == TaskType(_type) def test_level_of_theory(): lots = [ "wB97X-V/def2-TZVPPD/SMD", "wB97X-D/def2-SVPD/PCM", "wB97M-V/6-31g*/VACUUM", ] parameters = [ { "rem": { "method": "wb97xv", "basis": "def2-tzvppd", "solvent_method": "smd", }, "smx": {"solvent": "other"}, }, { "rem": {"method": "wb97xd", "basis": "def2-svpd", "solvent_method": "pcm"}, "pcm": {"theory": "cpcm"}, "solvent": {"dielectric": 78.39}, }, {"rem": {"method": "wb97mv", "basis": "6-31g*"}}, ] for lot, params in zip(lots, parameters): assert level_of_theory(params) == LevelOfTheory(lot) def test_solvent(): # Vacuum calc assert solvent({"rem": {"method": "wb97mv", "basis": "6-31g*"}}) == "NONE" # PCM - non-default assert ( solvent( { "rem": { "method": "wb97xd", "basis": "def2-svpd", "solvent_method": "pcm", }, "pcm": {"theory": "cpcm"}, "solvent": {"dielectric": 20}, } ) == "DIELECTRIC=20,00" ) # PCM - default assert ( solvent( { "rem": { "method": "wb97xd", "basis": "def2-svpd", "solvent_method": "pcm", }, "pcm": {"theory": "cpcm"}, } ) == "DIELECTRIC=78,39" ) # SMD - custom solvent assert ( solvent( { "rem": { "method": "wb97xv", "basis": "def2-tzvppd", "solvent_method": "smd", }, "smx": {"solvent": "other"}, }, custom_smd="4.9,1.558,0.0,0.576,49.94,0.667,0.0", ) == "DIELECTRIC=4,900;N=1,558;ALPHA=0,000;BETA=0,576;GAMMA=49,940;PHI=0,667;PSI=0,000" ) # SMD - missing custom_solvent with pytest.raises(ValueError): solvent( { "rem": { "method": "wb97xv", "basis": "def2-tzvppd", "solvent_method": "smd", }, "smx": {"solvent": "other"}, } ) # SMD - existing solvent assert ( solvent( { "rem": { "method": "wb97xv", "basis": "def2-tzvppd", "solvent_method": "smd", }, "smx": {"solvent": "ethanol"}, } ) == "SOLVENT=ETHANOL" ) # SMD - unknown solvent assert ( solvent( { "rem": { "method": "wb97xv", "basis": "def2-tzvppd", "solvent_method": "smd", }, } ) == "SOLVENT=WATER" ) def test_lot_solv(): answer = "wB97X-D/def2-SVPD/PCM(DIELECTRIC=78,39)" params = { "rem": {"method": "wb97xd", "basis": "def2-svpd", "solvent_method": "pcm"}, "pcm": {"theory": "cpcm"}, "solvent": {"dielectric": 78.39}, } assert lot_solvent_string(params) == answer def test_unexpected_lots(): # No method provided with pytest.raises(ValueError): level_of_theory({"rem": {"basis": "def2-qzvppd"}}) # No basis provided with pytest.raises(ValueError): level_of_theory({"rem": {"method": "b3lyp"}}) # Unknown dispersion correction with pytest.raises(ValueError): level_of_theory({"rem": {"method": "b3lyp", "dft_d": "empirical_grimme"}}) # Unknown functional with pytest.raises(ValueError): level_of_theory({"rem": {"method": "r2scan"}}) # Unknown basis with pytest.raises(ValueError): level_of_theory({"rem": {"method": "wb97xd3", "basis": "aug-cc-pVTZ"}}) @pytest.fixture(scope="session") def tasks(test_dir): data = loadfn((test_dir / "random_bh4_entries.json.gz").as_posix()) return [TaskDocument(**d) for d in data] def test_computed_entry(tasks): entries = [task.entry for task in tasks] ids = {e["entry_id"] for e in entries} expected = { MPID(i) for i in { 675022, 674849, 674968, 674490, 674950, 674338, 674322, 675078, 674385, 675041, } } assert ids == expected ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8320873 emmet_core-0.87.2/tests/test_absorption.py0000644000175100017510000000550615240717720020343 0ustar00runnerrunnerimport numpy as np import pytest from monty.serialization import loadfn from emmet.core.io.pymatgen import Structure from emmet.core import ARROW_COMPATIBLE from emmet.core.absorption import AbsorptionDoc from emmet.core.utils import jsanitize if ARROW_COMPATIBLE: import pyarrow as pa from emmet.core.arrow import arrowize @pytest.fixture(scope="session") def absorption_test_data(test_dir): return loadfn(test_dir / "sample_absorptions.json.gz") @pytest.fixture(scope="module") def absorption_test_doc(absorption_test_data): structure = Structure.from_dict( jsanitize(absorption_test_data["input"]["structure"]) ) task_id = absorption_test_data["task_id"] kpoints = absorption_test_data["orig_inputs"]["kpoints"] cr = absorption_test_data["calcs_reversed"][0]["output"] doc = AbsorptionDoc.from_structure( structure=structure, material_id="mp-{}".format(task_id), task_id=task_id, deprecated=False, energies=cr["frequency_dependent_dielectric"]["energy"], real_d=cr["frequency_dependent_dielectric"]["real"], imag_d=cr["frequency_dependent_dielectric"]["imaginary"], absorption_co=cr["optical_absorption_coeff"], bandgap=absorption_test_data["output"]["bandgap"], nkpoints=kpoints.num_kpts, is_hubbard=False, ) return doc def test_absorption_doc(absorption_test_doc): absorption_coeff = np.array( [ 0, 5504161142.509, 16485480924.4252, 41235259342.4927, 76990619286.8861, 109929386572.273, 164921201202.527, 230913400825.579, 285790460873.292, 371002598552.062, ] ) imag_dielectric = [0.0, 0.0002, 0.0003] energies = [ 0, 0.0309, 0.0617, 0.0926, 0.1235, 0.1543, 0.1852, 0.2161, 0.2469, 0.2778, ] assert absorption_test_doc is not None assert absorption_test_doc.property_name == "Optical absorption spectrum" assert absorption_test_doc.energies[0:10] == energies assert absorption_test_doc.material_id == "mp-1316" assert absorption_test_doc.absorption_coefficient[0:10] == list(absorption_coeff) assert absorption_test_doc.average_imaginary_dielectric[0:3] == imag_dielectric assert absorption_test_doc.bandgap == 4.4652 @pytest.mark.skipif( not ARROW_COMPATIBLE, reason="pyarrow must be installed to run this test." ) def test_arrow(absorption_test_doc): arrow_struct = pa.scalar( absorption_test_doc.model_dump(context={"format": "arrow"}), type=arrowize(AbsorptionDoc), ) test_arrow_doc = AbsorptionDoc(**arrow_struct.as_py(maps_as_pydicts="strict")) assert absorption_test_doc.model_dump() == test_arrow_doc.model_dump() ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8320873 emmet_core-0.87.2/tests/test_arrow.py0000644000175100017510000000444415240717720017315 0ustar00runnerrunnerimport importlib import inspect import itertools import os from pathlib import Path from typing import Optional, Union import pytest from pydantic._internal._model_construction import ModelMetaclass from emmet.core import ARROW_COMPATIBLE pa = pytest.importorskip("pyarrow") if ARROW_COMPATIBLE: from emmet.core.arrow import arrowize def import_models(): core_path = Path(__file__).parent.parent.joinpath("emmet/core") core_models = [] for root, dirs, files in os.walk(core_path): if "__pycache__" in dirs: dirs.remove("__pycache__") parent_module = ".".join( [ "emmet", "core", *list( itertools.takewhile( lambda x: x != "core", reversed(root.split("/")) ) )[::-1], ] ) for file in files: if file not in ["__init__.py", "__pycache__"]: if file[-3:] != ".py": continue file_name = file[:-3] if file_name == "optimade": continue module_name = f"{parent_module}.{file_name}" for name, obj in inspect.getmembers( importlib.import_module(module_name), inspect.isclass ): if ( obj.__module__ == module_name and isinstance(obj, ModelMetaclass) and not hasattr(obj, "arrow_incompatible") ): core_models.append(obj) return core_models def test_arrowize_succeeds(): assert all( dtype == pa.int64() for dtype in [ arrowize(int | None), arrowize(Optional[int]), arrowize(Union[int | None]), ] ) assert all( dtype == pa.map_(pa.string(), pa.int64()) for dtype in [arrowize(dict[str, int]), arrowize(dict[str, int | None])] ) def test_arrowize_fails(): with pytest.raises(AssertionError): arrowize(dict) arrowize(list) arrowize(tuple) @pytest.mark.parametrize("model", import_models()) def test_document_models_for_arrow_compatibility(model): assert isinstance(arrowize(model), pa.DataType) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8320873 emmet_core-0.87.2/tests/test_band_theory.py0000644000175100017510000001620615240717720020460 0ustar00runnerrunnerimport json import numpy as np import pytest from emmet.core.io.pymatgen import Spin, CompleteDos, Dos, Vasprun from emmet.core import ARROW_COMPATIBLE from emmet.core.band_theory import ( BSPathType, ElectronicBS, ProjectedBS, ElectronicDos, ProjectedDos, obtain_path_type, _coarse_list_superset, _loose_path_match, ) from emmet.core.testing_utils import DataArchive if ARROW_COMPATIBLE: import pyarrow as pa from emmet.core.arrow import arrowize @pytest.fixture(scope="module") def bs_fixture(test_dir): vasprun = DataArchive.extract_obj( test_dir / "vasp/r2scan_relax.json.gz", "vasprun.xml.gz", Vasprun, parse_projected_eigen=True, ) pmg_bs = vasprun.get_band_structure() elec_bs = ElectronicBS.from_pmg(pmg_bs, identifier="mp-0") return (vasprun, pmg_bs, elec_bs) @pytest.fixture(scope="module") def dos_fixture(test_dir): vasprun = DataArchive.extract_obj( test_dir / "vasp/Si_uniform.json.gz", "vasprun.xml.gz", Vasprun, parse_projected_eigen=True, ) pmg_dos = vasprun.complete_dos edos = ElectronicDos.from_pmg(pmg_dos, identifier="mp-0") return (vasprun, pmg_dos, edos) def test_elec_band_struct(bs_fixture): vasprun, pmg_bs, elec_bs = bs_fixture assert len(elec_bs.qpoints) == 116 assert np.all( np.abs( np.array(elec_bs.reciprocal_lattice) - np.array(vasprun.final_structure.lattice.reciprocal_lattice.matrix) ) < 1e-6 ) assert elec_bs.structure == vasprun.final_structure assert elec_bs.efermi == pytest.approx(2.72974902) assert elec_bs.band_gap == pytest.approx(0.0) assert elec_bs.is_metal assert all( getattr(elec_bs, f"spin_{s}_bands", None) is not None for s in ("up", "down") ) assert isinstance(elec_bs.projections, ProjectedBS) assert all( isinstance(getattr(elec_bs.projections, f"spin_{s}", None), list) for s in ("up", "down") ) dict_rep = elec_bs.model_dump() assert isinstance(dict_rep["structure"], dict) # round trip JSON dumped = elec_bs.model_dump_json() assert ElectronicBS(**json.loads(dumped)) == elec_bs # round trip pymatgen assert pmg_bs.as_dict() == elec_bs.to_pmg().as_dict() assert ( str(elec_bs) == f"ElectronicBS({elec_bs.identifier}: {elec_bs.structure.formula})" ) assert elec_bs.__repr__() == str(elec_bs) def test_base_electronic_dos(): """Test base electronic DOS class for jellium.""" rs = 4.0 efermi = (9 * np.pi / 4.0) ** (1 / 3) / rs energies = np.linspace(0, 3 * efermi, 200) heg_dos = (2 * energies) ** (0.5) / np.pi**2 for spin_pol in (1, 0.5): edos = ElectronicDos( spin_up_densities=spin_pol * heg_dos, spin_down_densities=spin_pol * heg_dos if spin_pol < 1 else None, energies=energies, efermi=efermi, ) spins_to_check = [Spin.up] pmg_dos = edos.to_pmg() if abs(spin_pol) < 1: spins_to_check.append(Spin.down) else: assert pmg_dos.densities.get(Spin.down) is None for spin in spins_to_check: assert np.all( np.abs( np.array(getattr(edos, f"spin_{spin.name}_densities")) - pmg_dos.densities[spin] ) < 1e-6 ) def test_electronic_dos(dos_fixture): vasprun, pmg_dos, edos = dos_fixture assert edos.efermi == pytest.approx(6.26639781) assert edos.structure == vasprun.final_structure for spin, dos in pmg_dos.densities.items(): assert np.all( np.abs(np.array(getattr(edos, f"spin_{spin.name}_densities")) - dos) < 1e-6 ) assert len(set(edos.projected_densities.site_index)) == len(edos.structure) assert isinstance(edos.projected_densities, ProjectedDos) assert ( ProjectedDos._from_list_of_dict(edos.projected_densities._to_list_of_dict()) == edos.projected_densities ) # test roundtrip json dumped = edos.model_dump_json() assert ElectronicDos(**json.loads(dumped)) == edos # test roundtrip pymatgen new_pmg_dos = edos.to_pmg() assert new_pmg_dos.as_dict() == pmg_dos.as_dict() assert isinstance(new_pmg_dos, CompleteDos) # test partial data return type edos_incomplete = ElectronicDos( **edos.model_dump(exclude=["structure", "projected_densities"]) ) assert isinstance(edos_incomplete.to_pmg(), Dos) assert str(edos) == f"ElectronicDos({edos.identifier}: {edos.structure.formula})" assert edos.__repr__() == str(edos) @pytest.mark.skipif( not ARROW_COMPATIBLE, reason="pyarrow must be installed to run this test." ) def test_arrow(bs_fixture, dos_fixture): _, _, elec_bs = bs_fixture bs_arrow_struct = pa.scalar( elec_bs.model_dump(context={"format": "arrow"}), type=arrowize(ElectronicBS) ) test_arrow_bs_doc = ElectronicBS(**bs_arrow_struct.as_py(maps_as_pydicts="strict")) assert test_arrow_bs_doc == elec_bs _, _, edos = dos_fixture dos_arrow_struct = pa.scalar( edos.model_dump(context={"format": "arrow"}), type=arrowize(ElectronicDos) ) test_arrow_dos_doc = ElectronicDos( **dos_arrow_struct.as_py(maps_as_pydicts="strict") ) assert test_arrow_dos_doc == edos def test_obtain_path_type(fe_line_mode_bandstructure): path_type, kpath, _ = next( obtain_path_type( fe_line_mode_bandstructure.structure, [x.frac_coords for x in fe_line_mode_bandstructure.kpoints], ) ) assert isinstance(path_type, BSPathType) assert path_type == BSPathType.setyawan_curtarolo assert kpath == [ "\\Gamma", "H", "N", "\\Gamma", "P", "H", "P", "N", ] def test_utilities(): assert _coarse_list_superset([1, 2, 3, 4, 5, 6, 7], [3, 4, 5]) assert _coarse_list_superset([1, 2, 3, 4, 5, 6, 7], [3, 4, 5, 6, 7]) assert _coarse_list_superset([1, 2, 3, 4, 5, 6, 7], [1, 2]) assert not _coarse_list_superset(["a", "b", "c", "d", "e"], ["b", "d", "f"]) assert ( _loose_path_match(["\\Gamma", "A", "B", "P_1"]) == BSPathType.setyawan_curtarolo ) assert _loose_path_match(["\\Gamma", "GAMMA", "B"]) == BSPathType.unknown assert _loose_path_match(["\\Gamma", "Γ", "B"]) == BSPathType.unknown assert _loose_path_match(["\\Gamma", "A", "B", r"P_{1}"]) == BSPathType.unknown assert _loose_path_match(["GAMMA", "A", "B", "P_1"]) == BSPathType.hinuma assert _loose_path_match(["\\Gamma", "GAMMA", "B"]) == BSPathType.unknown assert _loose_path_match(["GAMMA", "Γ", "B"]) == BSPathType.unknown assert _loose_path_match(["GAMMA", "a", "B"]) == BSPathType.unknown assert _loose_path_match(["GAMMA", "A", "B", r"P_{1}"]) == BSPathType.unknown assert _loose_path_match(["Γ", "a", "b", r"p_{1}"]) == BSPathType.latimer_munro assert _loose_path_match(["Γ", "\\Gamma", "b"]) == BSPathType.unknown assert _loose_path_match(["GAMMA", "Γ", "b"]) == BSPathType.unknown assert _loose_path_match(["Γ", "a", "b", "P_1"]) == BSPathType.unknown ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8320873 emmet_core-0.87.2/tests/test_calculation.py0000644000175100017510000002311415240717720020454 0ustar00runnerrunnerfrom hashlib import md5 import pytest from emmet.core.testing_utils import assert_schemas_equal, DataArchive from tests.conftest import get_test_object def test_init(): from emmet.core.vasp.calculation import ( Calculation, CalculationInput, CalculationOutput, RunStatistics, ) calc_input = CalculationInput() assert calc_input is not None calc_input = CalculationOutput() assert calc_input is not None calc_input = RunStatistics() assert calc_input is not None calc_input = Calculation() assert calc_input is not None @pytest.mark.parametrize( "object_name,task_name", [ pytest.param("SiOptimizeDouble", "relax1", id="SiOptimizeDouble"), pytest.param("SiStatic", "standard", id="SiStatic"), pytest.param("SiNonSCFUniform", "standard", id="SiNonSCFUniform"), ], ) def test_calculation_input(test_dir, object_name, task_name): from monty.json import MontyDecoder, jsanitize from emmet.core.io.pymatgen import Vasprun from emmet.core.vasp.calculation import CalculationInput test_object = get_test_object(object_name) with DataArchive.extract( test_dir / "vasp" / f"{test_object.folder}.json.gz" ) as dir_name: vasprun_file = dir_name / test_object.task_files[task_name]["vasprun_file"] test_doc = CalculationInput.from_vasprun(Vasprun(vasprun_file)) valid_doc = test_object.task_doc["calcs_reversed"][0]["input"] assert_schemas_equal(test_doc, valid_doc) # test document can be jsanitized d = jsanitize(test_doc, strict=True, enum_values=True, allow_bson=True) # and decoded MontyDecoder().process_decoded(d) # Test hubbard U re-parsing test_doc_dct = test_doc.model_dump() hubbards = list(range(1, 1 + len(test_doc.structure.composition.elements))) test_doc_dct["incar"]["LDAU"] = True test_doc_dct["incar"]["LDAUU"] = hubbards test_doc_dct["is_hubbard"] = True test_doc_dct.pop("hubbards", None) hubbard_doc = CalculationInput(**test_doc_dct) assert hubbard_doc.is_hubbard assert hubbard_doc.hubbards == { ele: hubbards[i] for i, ele in enumerate(hubbard_doc.potcar) } @pytest.mark.parametrize( "object_name,task_name", [ pytest.param("SiOptimizeDouble", "relax2", id="SiOptimizeDouble"), pytest.param("SiStatic", "standard", id="SiStatic"), pytest.param("SiNonSCFUniform", "standard", id="SiNonSCFUniform"), ], ) def test_calculation_output(test_dir, object_name, task_name): from monty.json import MontyDecoder, jsanitize from emmet.core.io.pymatgen import Outcar, Poscar, Vasprun from emmet.core.vasp.calculation import CalculationOutput test_object = get_test_object(object_name) with DataArchive.extract( test_dir / "vasp" / f"{test_object.folder}.json.gz" ) as dir_name: vasprun = Vasprun(dir_name / test_object.task_files[task_name]["vasprun_file"]) outcar = Outcar(dir_name / test_object.task_files[task_name]["outcar_file"]) contcar = Poscar.from_file( dir_name / test_object.task_files[task_name]["contcar_file"] ) test_doc = CalculationOutput.from_vasp_outputs(vasprun, outcar, contcar) valid_doc = test_object.task_doc["calcs_reversed"][0]["output"] assert_schemas_equal(test_doc, valid_doc) assert test_doc.efermi == vasprun.get_band_structure(efermi="smart").efermi # test document can be jsanitized d = jsanitize(test_doc, strict=True, enum_values=True, allow_bson=True) # and decoded MontyDecoder().process_decoded(d) def test_mag_calculation_output(test_dir): from emmet.core.io.pymatgen import Outcar, Poscar, Vasprun from emmet.core.vasp.calculation import CalculationOutput # Test magnetic properties with DataArchive.extract(test_dir / "vasp" / "magnetic_run.json.gz") as dir_name: d = CalculationOutput.from_vasp_outputs( Vasprun(dir_name / "vasprun.xml.gz"), Outcar(dir_name / "OUTCAR.gz"), Poscar.from_file(dir_name / "CONTCAR.gz"), ) assert d.model_dump()["mag_density"] == pytest.approx(0.19384725901794095) @pytest.mark.parametrize( "object_name,task_name", [ pytest.param("SiOptimizeDouble", "relax1", id="SiOptimizeDouble"), pytest.param("SiStatic", "standard", id="SiStatic"), pytest.param("SiNonSCFUniform", "standard", id="SiNonSCFUniform"), ], ) def test_run_statistics(test_dir, object_name, task_name): from monty.json import MontyDecoder, jsanitize from emmet.core.io.pymatgen import Outcar from emmet.core.vasp.calculation import RunStatistics test_object = get_test_object(object_name) outcar = DataArchive.extract_obj( test_dir / "vasp" / f"{test_object.folder}.json.gz", test_object.task_files[task_name]["outcar_file"], Outcar, ) test_doc = RunStatistics.from_outcar(outcar) valid_doc = test_object.task_doc["calcs_reversed"][0]["output"]["run_stats"] assert_schemas_equal(test_doc, valid_doc) # test document can be jsanitized d = jsanitize(test_doc, strict=True, enum_values=True, allow_bson=True) # and decoded MontyDecoder().process_decoded(d) @pytest.mark.parametrize( "object_name,task_name", [ pytest.param("SiOptimizeDouble", "relax2", id="SiOptimizeDouble"), pytest.param("SiStatic", "standard", id="SiStatic"), pytest.param("SiNonSCFUniform", "standard", id="SiNonSCFUniform"), ], ) def test_calculation(test_dir, object_name, task_name): from monty.json import MontyDecoder, jsanitize from emmet.core.vasp.calculation import Calculation test_object = get_test_object(object_name) files = test_object.task_files[task_name] with DataArchive.extract( test_dir / "vasp" / f"{test_object.folder}.json.gz" ) as dir_name: test_doc, objects = Calculation.from_vasp_files(dir_name, task_name, **files) valid_doc = test_object.task_doc["calcs_reversed"][0] assert_schemas_equal(test_doc, valid_doc) assert set(objects.keys()) == set(test_object.objects[task_name]) # check bader and ddec6 keys exist assert test_doc.bader is None assert test_doc.ddec6 is None # test document can be jsanitized d = jsanitize(test_doc, strict=True, enum_values=True, allow_bson=True) # and decoded MontyDecoder().process_decoded(d) @pytest.mark.parametrize("use_emmet_models", [True, False]) def test_calculation_run_type_metagga(test_dir, use_emmet_models): # Test to ensure that meta-GGA calculations are correctly identified # The VASP files were kindly provided by @Andrew-S-Rosen in issue #960 import numpy as np from emmet.core.vasp.calculation import Calculation from emmet.core.tasks import OutputDoc with DataArchive.extract(test_dir / "vasp" / "r2scan_relax.json.gz") as dir_name: calc, vasp_objects = Calculation.from_vasp_files( dir_name=dir_name, task_name="relax", vasprun_file="vasprun.xml.gz", outcar_file="OUTCAR.gz", contcar_file="CONTCAR.gz", parse_bandstructure=True, parse_dos=True, store_trajectory="full", use_emmet_models=use_emmet_models, ) assert "r2SCAN" in repr(calc.run_type) assert "r2SCAN" in repr(calc.calc_type) if use_emmet_models: from emmet.core.trajectory import RelaxTrajectory from emmet.core.band_theory import ElectronicBS, ElectronicDos assert isinstance(vasp_objects["trajectory"], RelaxTrajectory) assert isinstance(vasp_objects["bandstructure"], ElectronicBS) assert isinstance(vasp_objects["dos"], ElectronicDos) else: from emmet.core.io.pymatgen import Trajectory, BandStructure, CompleteDos assert isinstance(vasp_objects["trajectory"], Trajectory) assert isinstance(vasp_objects["bandstructure"], BandStructure) assert isinstance(vasp_objects["dos"], CompleteDos) # ensure parsing of forces and stress from trajectory rather than # ionic steps works correctly regardless of models used calc.output.ionic_steps = None output_calc = OutputDoc.from_vasp_calc_doc( calc, trajectory=vasp_objects["trajectory"] ) for k in ("forces", "stress"): assert np.allclose( getattr(output_calc, k), ( getattr(vasp_objects["trajectory"], k)[-1] if use_emmet_models else vasp_objects["trajectory"].frame_properties[-1].get(k) ), ) def test_PotcarSpec(test_dir): from emmet.core.vasp.calculation import PotcarSpec from emmet.core.io.pymatgen import PotcarSingle, Potcar try: # First test, PotcarSingle object potcar = PotcarSingle.from_symbol_and_functional(symbol="Si", functional="PBE") ps_spec = PotcarSpec.from_potcar_single(potcar_single=potcar) assert ps_spec.titel.split("")[1] == potcar.symbol potcar_hash = md5(potcar.data.encode()).hexdigest() assert ps_spec.hash == potcar_hash assert ps_spec.summary_stats == potcar._summary_stats # Second test, Potcar object containing mulitple PotcarSingle obejcts potcars = Potcar(symbols=["Ga_d", "As"], functional="PBE") ps_spec = PotcarSpec.from_potcar(potcar=potcars) for ips, ps in enumerate(ps_spec): assert ps.titel == potcars[ips].symbol assert ps.hash == potcars[ips].md5_header_hash assert ps.summary_stats == potcars[ips]._summary_stats except (OSError, ValueError): # missing Pymatgen POTCARs, cannot perform test assert True ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8320873 emmet_core-0.87.2/tests/test_chemenv.py0000644000175100017510000000342215240717720017603 0ustar00runnerrunnerimport pytest from emmet.core.io.pymatgen import Structure from . import test_structures from emmet.core import ARROW_COMPATIBLE from emmet.core.chemenv import ChemEnvDoc if ARROW_COMPATIBLE: import pyarrow as pa from emmet.core.arrow import arrowize @pytest.mark.xfail(reason="Requires new release of pymatgen?", strict=False) @pytest.mark.parametrize("structure", test_structures.values()) def test_chemenv(structure: Structure): """Very simple test to make sure this actually works""" doc = ChemEnvDoc.from_structure( structure=structure, material_id=33, deprecated=False ) valences = [getattr(site.specie, "oxi_state", None) for site in structure] valences = [v for v in valences if v is not None] if len(valences) == len(structure): assert doc.model_dump()["warnings"] is None # elif structure.composition.almost_equals(Composition("CsCl")): # # We do not have reference polyhedra above a certain number of neighbors. # # ChemEnv cannot deliver an answer without oxidation states. # assert doc.model_dump()["warnings"] == "ChemEnv algorithm failed." else: assert ( doc.model_dump()["warnings"] == "No oxidation states available. Cation-anion bonds cannot be identified." ) @pytest.mark.skipif( not ARROW_COMPATIBLE, reason="pyarrow must be installed to run this test." ) def test_arrow( structure=next(iter(test_structures.values())), ): doc = ChemEnvDoc.from_structure( structure=structure, material_id=33, deprecated=False ) arrow_struct = pa.scalar( doc.model_dump(context={"format": "arrow"}), type=arrowize(ChemEnvDoc) ) test_arrow_doc = ChemEnvDoc(**arrow_struct.as_py(maps_as_pydicts="strict")) assert doc == test_arrow_doc ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8320873 emmet_core-0.87.2/tests/test_defects.py0000644000175100017510000000064715240717720017601 0ustar00runnerrunnerfrom emmet.core.defect import DefectTaskDoc from emmet.core.testing_utils import DataArchive def test_parsing_defect_directory(test_dir): from emmet.core.io.pymatgen import Defect with DataArchive.extract(test_dir / "vasp/defect_run.json.gz") as temp_dir: defect_task = DefectTaskDoc.from_directory(temp_dir) assert isinstance(defect_task.defect, Defect) assert defect_task.defect_name == "O_Te" ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8320873 emmet_core-0.87.2/tests/test_elasticity.py0000644000175100017510000000643715240717720020341 0ustar00runnerrunnerimport numpy as np import pytest from monty.serialization import loadfn from emmet.core.io.pymatgen import Deformation, Strain, Stress, Tensor, TensorMapping from emmet.core import ARROW_COMPATIBLE from emmet.core.elasticity import ( ElasticityDoc, generate_derived_fitting_data, generate_primary_fitting_data, ) if ARROW_COMPATIBLE: import pyarrow as pa from emmet.core.arrow import arrowize @pytest.fixture(scope="session") def fitting_data(test_dir): """Primary fitting data""" data = loadfn(test_dir / "elasticity/SiC_fitting_data.json.gz") structure = data["structure"] deformations = [Deformation(x) for x in data["deformations"]] stresses = [Stress(x) for x in data["stresses"]] equilibrium_stress = Stress(data["equilibrium_stress"]) return structure, deformations, stresses, equilibrium_stress @pytest.fixture(scope="session") def reference_data(test_dir): """Reference data""" data = loadfn(test_dir / "elasticity/SiC_reference_data.json.gz") derived_strains = [Strain(x) for x in data["derived_strains"]] derived_stresses = [Stress(x) for x in data["derived_stresses"]] elastic_tensor_raw = data["elastic_tensor_raw"] return derived_strains, derived_stresses, elastic_tensor_raw def test_generate_derived_fitting_data(fitting_data, reference_data): structure, deformations, stresses, equilibrium_stress = fitting_data ref_d_strains, ref_d_stresses, _ = reference_data strains, _, _, _ = generate_primary_fitting_data(deformations, stresses) _, d_strains, d_stresses, _ = generate_derived_fitting_data( structure, strains, stresses ) def sequence_of_tensors_equal(a: list[Tensor], b: list[Tensor]): mapping = TensorMapping( tensors=a, values=[None for _ in range(len(a))], tol=1e-5 ) for i, x in enumerate(b): if x not in mapping: raise AssertionError( f"Cannot find a corresponding tensor in `a` that matches tensor " f"{i} in `b`" ) sequence_of_tensors_equal(d_strains, ref_d_strains) sequence_of_tensors_equal(d_stresses, ref_d_stresses) def test_from_deformations_and_stresses(fitting_data, reference_data): structure, deformations, stresses, equilibrium_stress = fitting_data _, _, ref_elastic_tensor = reference_data doc = ElasticityDoc.from_deformations_and_stresses( structure=structure, deformations=deformations, stresses=stresses, equilibrium_stress=equilibrium_stress, material_id=1, ) assert np.allclose(doc.elastic_tensor.raw, ref_elastic_tensor, atol=1e-6) @pytest.mark.skipif( not ARROW_COMPATIBLE, reason="pyarrow must be installed to run this test." ) def test_arrow(fitting_data): structure, deformations, stresses, equilibrium_stress = fitting_data doc = ElasticityDoc.from_deformations_and_stresses( structure=structure, deformations=deformations, stresses=stresses, equilibrium_stress=equilibrium_stress, material_id=1, ) arrow_struct = pa.scalar( doc.model_dump(context={"format": "arrow"}), type=arrowize(ElasticityDoc) ) test_arrow_doc = ElasticityDoc(**arrow_struct.as_py(maps_as_pydicts="strict")) assert doc == test_arrow_doc ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8320873 emmet_core-0.87.2/tests/test_electrodes.py0000644000175100017510000002104215240717720020305 0ustar00runnerrunnerimport gzip import json from pathlib import Path import pytest from pydantic import TypeAdapter from emmet.core.io.pymatgen import ( PhaseDiagram, ConversionElectrode, InsertionElectrode, InsertionVoltagePair, Composition, Element, ) from emmet.core import ARROW_COMPATIBLE from emmet.core.electrode import ( ConversionElectrodeDoc, ConversionVoltagePairDoc, InsertionElectrodeDoc, get_battery_formula, ) from emmet.core.mpid import AlphaID from emmet.core.types.pymatgen_types.computed_entries_adapter import ( ComputedEntryType, ComputedStructureEntryType, ) from emmet.core.types.typing import validate_compound_identifier if ARROW_COMPATIBLE: import pyarrow as pa from emmet.core.arrow import arrowize @pytest.fixture(scope="session") def entry_material_ids() -> list[AlphaID]: return [ AlphaID(idx) for idx in ["mp-13", "mp-595", "mvc-4", "mp-108", "mp-100001", "mvc-2"] ] @pytest.fixture(scope="session") def entries_LCO(test_dir: Path) -> list[dict]: with gzip.open(test_dir / "LiCoO2_batt.json.gz", "rt") as f: return json.load(f) @pytest.fixture(scope="session") def insertion_elec(test_dir, entry_material_ids): """ Recycle the test cases from pymatgen """ # entry_Li = ComputedEntry("Li", -1.90753119, entry_id="mp-1234-UNKNOWN") entry_Li = TypeAdapter(ComputedEntryType).validate_python( { "@module": "pymatgen.entries.computed_entries", "@class": "ComputedEntry", "composition": {"Li": 1.0}, "energy": -1.90753119, "correction": 0.0, "entry_id": "mp-1234-UNKNOWN", } ) # more cases can be added later if problems are found with gzip.open(test_dir / "LiTiO2_batt.json.gz", "rt") as f: entries_LTO = json.load(f) for i, entry in enumerate(entries_LTO): entries_LTO[i]["data"]["material_id"] = entry_material_ids[i].string entries_LTO[i]["entry_id"] = f"{entry_material_ids[i].string}-UNKNOWN" entries_LTO = TypeAdapter(list[ComputedStructureEntryType]).validate_python( entries_LTO ) ie_LTO = InsertionElectrode.from_entries(entries_LTO, entry_Li) d = { "LTO": (ie_LTO, entries_LTO[0].structure, entry_Li), } return d @pytest.fixture(scope="session") def conversion_elec(test_dir, entries_LCO): conversion_electrodes = {} for entry in entries_LCO: entry["entry_id"] = f"{entry['entry_id']}-UNKNOWN" entries_LCO = TypeAdapter(list[ComputedStructureEntryType]).validate_python( entries_LCO ) c = ConversionElectrode.from_composition_and_entries( Composition("LiCoO2"), entries_LCO, working_ion_symbol="Li" ) conversion_electrodes["LiCoO2"] = { "working_ion": "Li", "CE": c, "entries": entries_LCO, } expected_properties = { "LiCoO2": { "average_voltage": 2.26940307125, "capacity_grav": 903.19752911225669, "capacity_vol": 2903.35804724, "energy_grav": 2049.7192465127678, "energy_vol": 6588.8896693479574, } } return { k: (conversion_electrodes[k], expected_properties[k]) for k in conversion_electrodes.keys() } def test_InsertionDocs(insertion_elec): for k, (elec, struct, wion_entry) in insertion_elec.items(): # Make sure that main document can be created using an InsertionElectrode object ie = InsertionElectrodeDoc.from_entries( grouped_entries=elec.stable_entries, working_ion_entry=wion_entry, ) assert ie.battery_id == validate_compound_identifier( f"{min(mpid for mpid in ie.material_ids if not mpid.string.startswith('mvc'))}" f"_{ie.working_ion}", suffixes=(Element,), use_prefix=True, ) assert ie.average_voltage == elec.get_average_voltage() assert len(ie.material_ids) > 2 # Thermo ID breaks this, TODO for when builder is re-written # Make sure that each adjacent pair can be converted into a sub electrode # for sub_elec in elec.get_sub_electrodes(adjacent_only=True): # vp = InsertionVoltagePairDoc.from_sub_electrode(sub_electrode=sub_elec) # assert vp.average_voltage == sub_elec.get_average_voltage() # assert "mp" in vp.id_charge # assert type(ie.model_dump()["host_structure"]) == dict # This might be a requirement in the future assert all( isinstance(vp, InsertionVoltagePair) for vp in ie.electrode_object.voltage_pairs ) def test_ConversionDocs_from_entries(conversion_elec): for k, (elec, expected) in conversion_elec.items(): vp = ConversionElectrodeDoc.from_composition_and_entries( Composition(k), entries=elec["entries"], working_ion_symbol=elec["working_ion"], battery_id="mp-1234_Li", thermo_type="GGA_GGA+U", ) res_d = vp.model_dump() for k, v in expected.items(): assert res_d[k] == pytest.approx(v, 0.01) def test_ConversionDocs_from_composition_and_pd(conversion_elec, test_dir, entries_LCO): entries = [ TypeAdapter(ComputedStructureEntryType).validate_python(doc) for doc in entries_LCO ] pd = PhaseDiagram(entries) for k, (elec, expected) in conversion_elec.items(): vp = ConversionElectrodeDoc.from_composition_and_pd( comp=Composition(k), pd=pd, working_ion_symbol=elec["working_ion"], battery_id="mp-1234_Li", thermo_type="GGA_GGA+U", ) res_d = vp.model_dump() for k, v in expected.items(): assert res_d[k] == pytest.approx(v, 0.01) def test_ConversionDocs_from_sub_electrodes(conversion_elec): for k, (elec, expected) in conversion_elec.items(): for sub_elec in elec["CE"].get_sub_electrodes(adjacent_only=True): vp = ConversionVoltagePairDoc.from_sub_electrode(sub_electrode=sub_elec) assert vp.average_voltage == sub_elec.get_average_voltage() def test_get_battery_formula(): test_cases = [ (Composition("Li2CoO3"), Composition("Li7(CoO3)2"), Element("Li")), (Composition("Al4(CoO4)3"), Composition("Al2CoO4"), Element("Al")), (Composition("Li17(Co4O9)2"), Composition("Li21(Co4O9)2"), Element("Li")), ] results = [get_battery_formula(*case) for case in test_cases] assert results == ["Li2-3.5CoO3", "Al1.33-2CoO4", "Li8.5-10.5Co4O9"] @pytest.mark.skipif( not ARROW_COMPATIBLE, reason="pyarrow must be installed to run this test." ) def test_arrow_insertion(insertion_elec): elec, struct, wion_entry = next(iter(insertion_elec.values())) doc = InsertionElectrodeDoc.from_entries( grouped_entries=elec.stable_entries, working_ion_entry=wion_entry, ) assert doc.battery_id == validate_compound_identifier( f"{min(mpid for mpid in doc.material_ids if not mpid.string.startswith('mvc'))}" f"_{doc.working_ion}", suffixes=(Element,), use_prefix=True, ) arrow_struct = pa.scalar( doc.model_dump(context={"format": "arrow"}), type=arrowize(InsertionElectrodeDoc), ) test_arrow_doc = InsertionElectrodeDoc( **arrow_struct.as_py(maps_as_pydicts="strict") ) # can't assert doc == test_arrow_doc: # 1. unstable_entries and stable_entries are constructed as a tuple in pmg, # serializing into json/arrow makes tuples into arrays, so they round trip # back into python lists. the type in pmg is Iterable, so the pmg object is # constructed without warning when a dict is splatted in, but the container # type is mismatched, i.e., list((a, b, c)) != tuple((a, b, c))... assert doc.model_dump() == test_arrow_doc.model_dump() @pytest.mark.skipif( not ARROW_COMPATIBLE, reason="pyarrow must be installed to run this test." ) def test_arrow_conversion(conversion_elec): k = next(iter(conversion_elec)) elec, expected = next(iter(conversion_elec.values())) doc = ConversionElectrodeDoc.from_composition_and_entries( Composition(k), entries=elec["entries"], working_ion_symbol=elec["working_ion"], battery_id="mp-1234_Li", thermo_type="GGA_GGA+U", ) arrow_struct = pa.scalar( doc.model_dump(context={"format": "arrow"}), type=arrowize(ConversionElectrodeDoc), ) test_arrow_doc = ConversionElectrodeDoc( **arrow_struct.as_py(maps_as_pydicts="strict") ) assert doc == test_arrow_doc ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8320873 emmet_core-0.87.2/tests/test_electronic_structure.py0000644000175100017510000001212615240717720022426 0ustar00runnerrunnerimport pytest from monty.serialization import loadfn from emmet.core import ARROW_COMPATIBLE from emmet.core.electronic_structure import ElectronicStructureDoc from emmet.core.material import PropertyOrigin from emmet.core.mpid import AlphaID from emmet.core.types.electronic_structure import BSShim, DosShim from emmet.core.utils import jsanitize if ARROW_COMPATIBLE: import pyarrow as pa from emmet.core.arrow import arrowize @pytest.fixture(scope="session") def structure(test_dir): """ Fe (mp-13) structure with correct magmoms """ return loadfn(test_dir / "electronic_structure/Fe_structure.json.gz") @pytest.fixture(scope="session") def dos(test_dir): """ Fe (mp-13) dos """ return loadfn(test_dir / "electronic_structure/Fe_dos.json.gz") def test_from_bsdos_1(fe_line_mode_bandstructure, dos, structure): origins = [ PropertyOrigin(name="setyawan_curtarolo", task_id="mp-1056141"), PropertyOrigin(name="dos", task_id="mp-1671247"), ] es_doc = ElectronicStructureDoc.from_bsdos( bandstructures=BSShim( setyawan_curtarolo=("mp-1056141", fe_line_mode_bandstructure, 2) ), dos=DosShim(dos=("mp-1671247", dos, 2)), origins=origins, structures={ AlphaID("mp-1671247", padlen=8): structure, AlphaID("mp-1056141", padlen=8): structure, }, material_id="mp-13", deprecated=False, meta_structure=structure, ) assert es_doc.material_id == "mp-13" assert es_doc.band_gap == 0.0 assert es_doc.cbm is None assert es_doc.vbm is None assert es_doc.efermi == 5.18804178 assert es_doc.is_gap_direct is False assert es_doc.is_metal is True assert str(es_doc.magnetic_ordering) == "Ordering.FM" assert es_doc.bandstructure.setyawan_curtarolo.task_id == "mp-1056141" assert es_doc.bandstructure.setyawan_curtarolo.band_gap == 0.0 assert es_doc.bandstructure.setyawan_curtarolo.efermi == 5.18804178 assert es_doc.bandstructure.setyawan_curtarolo.nbands == 96.0 @pytest.fixture def bandstructure_fs(test_dir): return loadfn(test_dir / "electronic_structure/es_bs_objs.json.gz") @pytest.fixture def dos_fs(test_dir): return loadfn(test_dir / "electronic_structure/es_dos_objs.json.gz") def test_from_bsdos_2(bandstructure_fs, dos_fs): dos = dos_fs[0]["data"] bs = bandstructure_fs[0]["data"] structures = { AlphaID("mp-823888", padlen=8): dos.structure, AlphaID("mp-1612487", padlen=8): bs.structure, } origins = [ PropertyOrigin(name="setyawan_curtarolo", task_id="mp-1612487"), PropertyOrigin(name="dos", task_id="mp-823888"), ] es_doc = ElectronicStructureDoc.from_bsdos( bandstructures=BSShim(setyawan_curtarolo=(("mp-1612487", bs, 2))), dos=DosShim(dos=("mp-823888", dos, 2)), origins=origins, structures=structures, material_id="mp-25375", deprecated=False, meta_structure=dos.structure, ) assert es_doc.material_id == "mp-25375" assert es_doc.band_gap == pytest.approx(1.9916) assert es_doc.vbm == pytest.approx(2.4169) assert es_doc.cbm == pytest.approx(4.4085) assert es_doc.efermi == pytest.approx(2.49084067) assert es_doc.is_gap_direct is False assert es_doc.is_metal is False assert str(es_doc.magnetic_ordering) == "Ordering.NM" assert es_doc.bandstructure.setyawan_curtarolo.task_id == "mp-1612487" assert es_doc.bandstructure.setyawan_curtarolo.band_gap == pytest.approx(1.9916) assert es_doc.bandstructure.setyawan_curtarolo.efermi == pytest.approx(2.49084067) assert es_doc.bandstructure.setyawan_curtarolo.nbands == pytest.approx(64.0) @pytest.mark.skipif( not ARROW_COMPATIBLE, reason="pyarrow must be installed to run this test." ) def test_arrow(bandstructure_fs, dos_fs): dos = dos_fs[0]["data"] bs = bandstructure_fs[0]["data"] structures = { AlphaID("mp-823888", padlen=8): dos.structure, AlphaID("mp-1612487", padlen=8): bs.structure, } origins = [ PropertyOrigin(name="setyawan_curtarolo", task_id="mp-1612487"), PropertyOrigin(name="dos", task_id="mp-823888"), ] doc = ElectronicStructureDoc.from_bsdos( bandstructures=BSShim(setyawan_curtarolo=(("mp-1612487", bs, 2))), dos=DosShim(dos=("mp-823888", dos, 2)), origins=origins, structures=structures, material_id="mp-25375", deprecated=False, meta_structure=dos.structure, ) arrow_struct = pa.scalar( doc.model_dump(context={"format": "arrow"}), type=arrowize(ElectronicStructureDoc), ) test_arrow_doc = ElectronicStructureDoc( **arrow_struct.as_py(maps_as_pydicts="strict") ) # have to compare dicts due to dumping model to json the first time: # DOS spins and orbital types don't rehydrate into pmg objects (Spin, OrbitalType) # str representation of the doesn't map back to the enum vals in pmg (ints) assert jsanitize(doc.model_dump(), allow_bson=True) == jsanitize( test_arrow_doc.model_dump(), allow_bson=True ) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8320873 emmet_core-0.87.2/tests/test_lobster.py0000644000175100017510000001143215240717720017630 0ustar00runnerrunner"""Test Lobster document class.""" from pathlib import Path import pytest from emmet.core.io.pymatgen import ( Structure, Cohp, CompleteCohp, LobsterCompleteDos, Bandoverlaps, Charge, Grosspop, Icohplist, MadelungEnergies, SitePotential, ) try: from emmet.core.lobster import ( CalcQualitySummary, LobsterinModel, LobsteroutModel, LobsterTaskDocument, StrongestBonds, ) except ImportError: pytest.skip(allow_module_level=True) @pytest.fixture(scope="module") def lobster_test_dir(test_dir: Path) -> Path: return test_dir / "lobster" @pytest.mark.parametrize("save_cohp_plots", [True, False]) @pytest.mark.parametrize("store_lso_dos", [True, False]) @pytest.mark.parametrize("add_coxxcar_to_task_document", [True, False]) def test_lobster_task_doc( lobster_test_dir, save_cohp_plots, store_lso_dos, add_coxxcar_to_task_document, tmp_path, ): lobster_doc = LobsterTaskDocument.from_directory( dir_name=lobster_test_dir / "mp-2534", save_cohp_plots=save_cohp_plots, store_lso_dos=store_lso_dos, add_coxxcar_to_task_document=add_coxxcar_to_task_document, save_cba_jsons=tmp_path / "cba.json.gz", save_computational_data_jsons=tmp_path / "computational_data.json.gz", ) assert isinstance(lobster_doc, LobsterTaskDocument) if add_coxxcar_to_task_document: assert { *map( type, (lobster_doc.cohp_data, lobster_doc.cobi_data, lobster_doc.coop_data), ) } == {CompleteCohp} else: assert { *map( type, (lobster_doc.cohp_data, lobster_doc.cobi_data, lobster_doc.coop_data), ) } == {type(None)} assert Path(tmp_path / "cba.json.gz").is_file() assert Path(tmp_path / "computational_data.json.gz").is_file() assert isinstance(lobster_doc.structure, Structure) assert isinstance(lobster_doc.lobsterout, LobsteroutModel) assert lobster_doc.lobsterout.charge_spilling[0] == pytest.approx( 0.00989999, abs=1e-7 ) assert isinstance(lobster_doc.lobsterin, LobsterinModel) assert isinstance(lobster_doc.calc_quality_summary, CalcQualitySummary) assert lobster_doc.lobsterin.cohp_start_energy == -5 assert isinstance(lobster_doc.strongest_bonds, StrongestBonds) assert lobster_doc.strongest_bonds.strongest_bonds_icohp["As-Ga"] == pytest.approx( {"bond_strength": -4.32971, "length": 2.4899} ) assert lobster_doc.strongest_bonds.strongest_bonds_icobi["As-Ga"] == pytest.approx( {"bond_strength": 0.82707, "length": 2.4899} ) assert lobster_doc.strongest_bonds.strongest_bonds_icoop["As-Ga"] == pytest.approx( {"bond_strength": 0.31405, "length": 2.4899} ) assert lobster_doc.strongest_bonds.which_bonds == "all" assert lobster_doc.strongest_bonds_cation_anion.strongest_bonds_icohp[ "As-Ga" ] == pytest.approx({"bond_strength": -4.32971, "length": 2.4899}) assert lobster_doc.strongest_bonds_cation_anion.strongest_bonds_icobi[ "As-Ga" ] == pytest.approx({"bond_strength": 0.82707, "length": 2.4899}) assert lobster_doc.strongest_bonds_cation_anion.strongest_bonds_icoop[ "As-Ga" ] == pytest.approx({"bond_strength": 0.31405, "length": 2.4899}) assert lobster_doc.strongest_bonds_cation_anion.which_bonds == "cation-anion" assert isinstance( lobster_doc.lobsterpy_data.cohp_plot_data.data["Ga1: 4 x As-Ga"], Cohp ) assert lobster_doc.lobsterpy_data.which_bonds == "all" assert lobster_doc.lobsterpy_data_cation_anion.which_bonds == "cation-anion" assert lobster_doc.lobsterpy_data.number_of_considered_ions == 2 assert isinstance( lobster_doc.lobsterpy_data_cation_anion.cohp_plot_data.data["Ga1: 4 x As-Ga"], Cohp, ) assert isinstance(lobster_doc.lobsterpy_text, str) assert isinstance(lobster_doc.lobsterpy_text_cation_anion, str) assert isinstance(lobster_doc.dos, LobsterCompleteDos) assert isinstance(lobster_doc.charges, Charge) assert isinstance(lobster_doc.madelung_energies, MadelungEnergies) assert isinstance(lobster_doc.site_potentials, SitePotential) assert isinstance(lobster_doc.band_overlaps, Bandoverlaps) assert { *map( type, (lobster_doc.icohp_list, lobster_doc.icobi_list, lobster_doc.icoop_list), ) } == {Icohplist} assert isinstance(lobster_doc.gross_populations, Grosspop) assert lobster_doc.chemsys == "As-Ga" # delete files generated as part of test exec to_delete_ext = ["*.pdf", "*.txt", "*json*"] for ext in to_delete_ext: for file in (lobster_test_dir / "mp-2534").rglob(ext): if file.is_file(): file.unlink() ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8320873 emmet_core-0.87.2/tests/test_magnetism.py0000644000175100017510000000354015240717720020143 0ustar00runnerrunnerimport pytest from monty.serialization import loadfn from emmet.core.io.pymatgen import Structure from emmet.core import ARROW_COMPATIBLE from emmet.core.magnetism import MagnetismDoc from emmet.core.utils import jsanitize if ARROW_COMPATIBLE: import pyarrow as pa from emmet.core.arrow import arrowize @pytest.fixture(scope="module") def magnetism_mats(test_dir): return loadfn(test_dir / "magnetism/magnetism_mats_sample.json.gz") def test_magnetism_doc(magnetism_mats): test_orderings = {"mp-1034331": "FM", "mp-753472": "NM"} for material in magnetism_mats: structure = Structure.from_dict(jsanitize(material["structure"])) total_magnetization = material["magnetism"]["total_magnetization"] material_id = material["task_id"] doc = MagnetismDoc.from_structure( structure=structure, material_id=material_id, total_magnetization=total_magnetization, deprecated=False, ) assert doc is not None assert doc.total_magnetization == pytest.approx(abs(total_magnetization)) if material_id in test_orderings: assert doc.ordering == test_orderings[material_id] @pytest.mark.skipif( not ARROW_COMPATIBLE, reason="pyarrow must be installed to run this test." ) def test_arrow(magnetism_mats): doc = MagnetismDoc.from_structure( structure=Structure.from_dict(jsanitize(magnetism_mats[0]["structure"])), material_id=magnetism_mats[0]["task_id"], total_magnetization=magnetism_mats[0]["magnetism"]["total_magnetization"], deprecated=False, ) arrow_struct = pa.scalar( doc.model_dump(context={"format": "arrow"}), type=arrowize(MagnetismDoc) ) test_arrow_doc = MagnetismDoc(**arrow_struct.as_py(maps_as_pydicts="strict")) assert doc.model_dump() == test_arrow_doc.model_dump() ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8322716 emmet_core-0.87.2/tests/test_ml.py0000644000175100017510000000403215240717720016564 0ustar00runnerrunnerimport pytest # from matcalc.utils import get_universal_calculator from emmet.core.io.pymatgen import Structure, STRUCTURES_DIR from emmet.core.elasticity import BulkModulus, ElasticTensorDoc, ShearModulus from emmet.core.ml import MLDoc # if TYPE_CHECKING: # from ase.calculators.calculator import Calculator expected_keys = { # -- metadata -- "material_id": str, "structure": Structure, "deprecated": bool, "matcalc_version": type(None), # str, "model_name": type(None), # str, "model_version": type(None), # str, # -- relaxation -- "final_structure": Structure, "energy": float, "volume": float, "a": float, "b": float, "c": float, "alpha": float, "beta": float, "gamma": float, # -- eos -- "eos": dict, "bulk_modulus_bm": float, # -- phonon -- "temperatures": list, "free_energy": list, "entropy": list, "heat_capacity": list, # -- elasticity -- "elastic_tensor": ElasticTensorDoc, "shear_modulus": ShearModulus, "bulk_modulus": BulkModulus, "young_modulus": float, } # @pytest.mark.parametrize( # ("calculator", "prop_kwargs"), # [ # (get_universal_calculator("chgnet"), None), # ("M3GNet-MP-2021.2.8-PES", {"ElasticityCalc": {"relax_structure": False}}), # ], # ) @pytest.mark.skip(reason="Temporary skip. Needs attention.") def test_ml_doc(calculator, prop_kwargs: dict) -> None: struct = Structure.from_file(STRUCTURES_DIR / "Si.json") doc = MLDoc( structure=struct, calculator=calculator, material_id="mp-33", deprecated=False, prop_kwargs=prop_kwargs, ) # check that all expected keys are present missing = sorted({*expected_keys} - {*doc.__dict__}) assert not missing, f"keys {missing=}" # check that all keys have expected type for key, typ in expected_keys.items(): actual = getattr(doc, key) assert isinstance( actual, typ ), f"{key=} expected type={typ.__name__}, got {type(actual).__name__}" ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8322716 emmet_core-0.87.2/tests/test_model_fields.py0000644000175100017510000006610415240717720020612 0ustar00runnerrunner"""Ensure that document models used in API do not change fields.""" from importlib import import_module import pytest ref_model_fields = { "emmet.core._general_store.GeneralStoreDoc": [ "kind", "markdown", "meta", "last_updated", ], "emmet.core._messages.MessagesDoc": [ "title", "body", "authors", "type", "last_updated", ], "emmet.core._user_settings.UserSettingsDoc": ["consumer_id", "settings"], "emmet.core.absorption.AbsorptionDoc": [ "builder_meta", "nsites", "elements", "nelements", "composition", "composition_reduced", "formula_pretty", "formula_anonymous", "chemsys", "volume", "density", "density_atomic", "symmetry", "material_id", "deprecated", "deprecation_reasons", "last_updated", "origins", "warnings", "structure", "property_name", "energies", "energy_max", "absorption_coefficient", "average_imaginary_dielectric", "average_real_dielectric", "bandgap", "nkpoints", ], "emmet.core.alloys.AlloyPairDoc": ["builder_meta", "alloy_pair", "pair_id"], "emmet.core.bonds.BondingDoc": [ "builder_meta", "nsites", "elements", "nelements", "composition", "composition_reduced", "formula_pretty", "formula_anonymous", "chemsys", "volume", "density", "density_atomic", "symmetry", "material_id", "deprecated", "deprecation_reasons", "last_updated", "origins", "warnings", "structure", "property_name", "structure_graph", "method", "bond_types", "bond_length_stats", "coordination_envs", "coordination_envs_anonymous", ], "emmet.core.chemenv.ChemEnvDoc": [ "builder_meta", "nsites", "elements", "nelements", "composition", "composition_reduced", "formula_pretty", "formula_anonymous", "chemsys", "volume", "density", "density_atomic", "symmetry", "material_id", "deprecated", "deprecation_reasons", "last_updated", "origins", "warnings", "structure", "property_name", "valences", "species", "chemenv_symbol", "chemenv_iupac", "chemenv_iucr", "chemenv_name", "chemenv_name_with_alternatives", "csm", "method", "mol_from_site_environments", "wyckoff_positions", ], "emmet.core.dois.DOIDoc": ["doi", "material_id"], "emmet.core.elasticity.ElasticityDoc": [ "builder_meta", "nsites", "elements", "nelements", "composition", "composition_reduced", "formula_pretty", "formula_anonymous", "chemsys", "volume", "density", "density_atomic", "symmetry", "material_id", "deprecated", "deprecation_reasons", "last_updated", "origins", "warnings", "structure", "property_name", "order", "elastic_tensor", "compliance_tensor", "bulk_modulus", "shear_modulus", "sound_velocity", "thermal_conductivity", "young_modulus", "universal_anisotropy", "homogeneous_poisson", "debye_temperature", "fitting_data", "fitting_method", "state", ], "emmet.core.electrode.InsertionElectrodeDoc": [ "builder_meta", "battery_type", "thermo_type", "battery_formula", "working_ion", "num_steps", "max_voltage_step", "last_updated", "framework", "framework_formula", "elements", "nelements", "chemsys", "formula_anonymous", "warnings", "formula_charge", "formula_discharge", "max_delta_volume", "average_voltage", "capacity_grav", "capacity_vol", "energy_grav", "energy_vol", "fracA_charge", "fracA_discharge", "stability_charge", "stability_discharge", "id_charge", "id_discharge", "host_structure", "adj_pairs", "material_ids", "entries_composition_summary", "electrode_object", ], "emmet.core.electronic_structure.ElectronicStructureDoc": [ "band_gap", "cbm", "vbm", "efermi", "is_gap_direct", "is_metal", "magnetic_ordering", "builder_meta", "nsites", "elements", "nelements", "composition", "composition_reduced", "formula_pretty", "formula_anonymous", "chemsys", "volume", "density", "density_atomic", "symmetry", "material_id", "deprecated", "deprecation_reasons", "last_updated", "origins", "warnings", "structure", "property_name", "bandstructure", "dos", ], "emmet.core.eos.EOSDoc": [ "task_id", "energies", "eos", "volumes", ], "emmet.core.grain_boundary.GrainBoundaryDoc": [ "material_id", "sigma", "type", "rotation_axis", "gb_plane", "rotation_angle", "gb_energy", "initial_structure", "final_structure", "pretty_formula", "w_sep", "structure", "chemsys", "last_updated", ], "emmet.core.magnetism.MagnetismDoc": [ "builder_meta", "nsites", "elements", "nelements", "composition", "composition_reduced", "formula_pretty", "formula_anonymous", "chemsys", "volume", "density", "density_atomic", "symmetry", "material_id", "deprecated", "deprecation_reasons", "last_updated", "origins", "warnings", "structure", "property_name", "ordering", "is_magnetic", "exchange_symmetry", "num_magnetic_sites", "num_unique_magnetic_sites", "types_of_magnetic_species", "magmoms", "total_magnetization", "total_magnetization_normalized_vol", "total_magnetization_normalized_formula_units", ], "emmet.core.molecules.atomic.PartialChargesDoc": [ "charge", "spin_multiplicity", "natoms", "elements", "nelements", "nelectrons", "composition", "composition_reduced", "formula_alphabetical", "formula_pretty", "formula_anonymous", "chemsys", "symmetry", "species_hash", "coord_hash", "property_name", "property_id", "molecule_id", "deprecated", "deprecation_reasons", "level_of_theory", "solvent", "lot_solvent", "last_updated", "origins", "warnings", "method", "partial_charges", ], "emmet.core.molecules.atomic.PartialSpinsDoc": [ "charge", "spin_multiplicity", "natoms", "elements", "nelements", "nelectrons", "composition", "composition_reduced", "formula_alphabetical", "formula_pretty", "formula_anonymous", "chemsys", "symmetry", "species_hash", "coord_hash", "property_name", "property_id", "molecule_id", "deprecated", "deprecation_reasons", "level_of_theory", "solvent", "lot_solvent", "last_updated", "origins", "warnings", "method", "partial_spins", ], "emmet.core.molecules.bonds.MoleculeBondingDoc": [ "charge", "spin_multiplicity", "natoms", "elements", "nelements", "nelectrons", "composition", "composition_reduced", "formula_alphabetical", "formula_pretty", "formula_anonymous", "chemsys", "symmetry", "species_hash", "coord_hash", "property_name", "property_id", "molecule_id", "deprecated", "deprecation_reasons", "level_of_theory", "solvent", "lot_solvent", "last_updated", "origins", "warnings", "molecule_graph", "method", "bond_types", "bonds", "bonds_nometal", ], "emmet.core.molecules.orbitals.OrbitalDoc": [ "charge", "spin_multiplicity", "natoms", "elements", "nelements", "nelectrons", "composition", "composition_reduced", "formula_alphabetical", "formula_pretty", "formula_anonymous", "chemsys", "symmetry", "species_hash", "coord_hash", "property_name", "property_id", "molecule_id", "deprecated", "deprecation_reasons", "level_of_theory", "solvent", "lot_solvent", "last_updated", "origins", "warnings", "open_shell", "nbo_population", "nbo_lone_pairs", "nbo_bonds", "nbo_three_center_bonds", "nbo_hyperbonds", "nbo_interactions", "alpha_population", "beta_population", "alpha_lone_pairs", "beta_lone_pairs", "alpha_bonds", "beta_bonds", "alpha_three_center_bonds", "beta_three_center_bonds", "alpha_hyperbonds", "beta_hyperbonds", "alpha_interactions", "beta_interactions", ], "emmet.core.molecules.redox.RedoxDoc": [ "charge", "spin_multiplicity", "natoms", "elements", "nelements", "nelectrons", "composition", "composition_reduced", "formula_alphabetical", "formula_pretty", "formula_anonymous", "chemsys", "symmetry", "species_hash", "coord_hash", "property_name", "property_id", "molecule_id", "deprecated", "deprecation_reasons", "level_of_theory", "solvent", "lot_solvent", "last_updated", "origins", "warnings", "base_property_id", "electron_affinity", "ea_task_id", "ionization_energy", "ie_task_id", "reduction_energy", "reduction_free_energy", "red_molecule_id", "red_property_id", "oxidation_energy", "oxidation_free_energy", "ox_molecule_id", "ox_property_id", "reduction_potential", "oxidation_potential", ], "emmet.core.molecules.summary.MoleculeSummaryDoc": [ "charge", "spin_multiplicity", "natoms", "elements", "nelements", "nelectrons", "composition", "composition_reduced", "formula_alphabetical", "formula_pretty", "formula_anonymous", "chemsys", "symmetry", "species_hash", "coord_hash", "property_name", "property_id", "molecule_id", "deprecated", "deprecation_reasons", "level_of_theory", "solvent", "lot_solvent", "last_updated", "origins", "warnings", "molecules", "molecule_levels_of_theory", "inchi", "inchi_key", "task_ids", "similar_molecules", "constituent_molecules", "unique_calc_types", "unique_task_types", "unique_levels_of_theory", "unique_solvents", "unique_lot_solvents", "thermo", "vibration", "orbitals", "partial_charges", "partial_spins", "bonding", "multipole_moments", "redox", "metal_binding", "has_props", ], "emmet.core.molecules.thermo.MoleculeThermoDoc": [ "charge", "spin_multiplicity", "natoms", "elements", "nelements", "nelectrons", "composition", "composition_reduced", "formula_alphabetical", "formula_pretty", "formula_anonymous", "chemsys", "symmetry", "species_hash", "coord_hash", "property_name", "property_id", "molecule_id", "deprecated", "deprecation_reasons", "level_of_theory", "solvent", "lot_solvent", "last_updated", "origins", "warnings", "electronic_energy", "correction", "base_level_of_theory", "base_solvent", "base_lot_solvent", "correction_level_of_theory", "correction_solvent", "correction_lot_solvent", "combined_lot_solvent", "zero_point_energy", "rt", "total_enthalpy", "total_entropy", "translational_enthalpy", "translational_entropy", "rotational_enthalpy", "rotational_entropy", "vibrational_enthalpy", "vibrational_entropy", "free_energy", ], "emmet.core.molecules.vibration.VibrationDoc": [ "charge", "spin_multiplicity", "natoms", "elements", "nelements", "nelectrons", "composition", "composition_reduced", "formula_alphabetical", "formula_pretty", "formula_anonymous", "chemsys", "symmetry", "species_hash", "coord_hash", "property_name", "property_id", "molecule_id", "deprecated", "deprecation_reasons", "level_of_theory", "solvent", "lot_solvent", "last_updated", "origins", "warnings", "molecule", "frequencies", "frequency_modes", "ir_intensities", "ir_activities", "raman_intensities", "raman_activities", ], "emmet.core.molecules_jcesr.MoleculesDoc": [ "elements", "nelements", "EA", "IE", "charge", "pointgroup", "smiles", "task_id", "molecule", "formula_pretty", "svg", ], "emmet.core.oxidation_states.OxidationStateDoc": [ "builder_meta", "nsites", "elements", "nelements", "composition", "composition_reduced", "formula_pretty", "formula_anonymous", "chemsys", "volume", "density", "density_atomic", "symmetry", "material_id", "deprecated", "deprecation_reasons", "last_updated", "origins", "warnings", "structure", "property_name", "possible_species", "possible_valences", "average_oxidation_states", "method", ], "emmet.core.phonon.PhononBSDOSDoc": [ "nsites", "elements", "nelements", "composition", "composition_reduced", "formula_pretty", "formula_anonymous", "chemsys", "volume", "density", "density_atomic", "symmetry", "identifier", "phonon_method", "phonon_bandstructure", "phonon_dos", "epsilon_static", "epsilon_electronic", "born", "force_constants", "last_updated", "sum_rules_breaking", "structure", "total_dft_energy", "volume_per_formula_unit", "formula_units", "supercell_matrix", "primitive_matrix", "code", "post_process_settings", "thermal_displacement_data", "calc_meta", ], "emmet.core.polar.DielectricDoc": [ "builder_meta", "nsites", "elements", "nelements", "composition", "composition_reduced", "formula_pretty", "formula_anonymous", "chemsys", "volume", "density", "density_atomic", "symmetry", "material_id", "deprecated", "deprecation_reasons", "last_updated", "origins", "warnings", "structure", "property_name", "total", "ionic", "electronic", "e_total", "e_ionic", "e_electronic", "n", ], "emmet.core.polar.PiezoelectricDoc": [ "builder_meta", "nsites", "elements", "nelements", "composition", "composition_reduced", "formula_pretty", "formula_anonymous", "chemsys", "volume", "density", "density_atomic", "symmetry", "material_id", "deprecated", "deprecation_reasons", "last_updated", "origins", "warnings", "structure", "property_name", "total", "ionic", "electronic", "e_ij_max", "max_direction", "strain_for_max", ], "emmet.core.provenance.ProvenanceDoc": [ "builder_meta", "nsites", "elements", "nelements", "composition", "composition_reduced", "formula_pretty", "formula_anonymous", "chemsys", "volume", "density", "density_atomic", "symmetry", "material_id", "deprecated", "deprecation_reasons", "last_updated", "origins", "warnings", "structure", "property_name", "created_at", "references", "authors", "remarks", "tags", "theoretical", "database_IDs", "history", ], "emmet.core.qchem.molecule.MoleculeDoc": [ "builder_meta", "charge", "spin_multiplicity", "natoms", "elements", "nelements", "nelectrons", "composition", "composition_reduced", "formula_alphabetical", "formula_pretty", "formula_anonymous", "chemsys", "symmetry", "species_hash", "coord_hash", "molecule_id", "molecule", "deprecated", "deprecation_reasons", "initial_molecules", "task_ids", "deprecated_tasks", "calc_types", "last_updated", "created_at", "origins", "warnings", "species", "molecules", "molecule_levels_of_theory", "inchi", "inchi_key", "task_types", "levels_of_theory", "solvents", "lot_solvents", "unique_calc_types", "unique_task_types", "unique_levels_of_theory", "unique_solvents", "unique_lot_solvents", "entries", "best_entries", "constituent_molecules", "similar_molecules", ], "emmet.core.qchem.task.TaskDocument": [ "charge", "spin_multiplicity", "natoms", "elements", "nelements", "nelectrons", "composition", "composition_reduced", "formula_alphabetical", "formula_pretty", "formula_anonymous", "chemsys", "symmetry", "species_hash", "coord_hash", "builder_meta", "calc_code", "version", "dir_name", "task_id", "completed", "completed_at", "last_updated", "is_valid", "state", "cputime", "walltime", "calcs_reversed", "orig", "output", "critic2", "custom_smd", "special_run_type", "smiles", "tags", "warnings", ], "emmet.core.robocrys.RobocrystallogapherDoc": [ "builder_meta", "nsites", "elements", "nelements", "composition", "composition_reduced", "formula_pretty", "formula_anonymous", "chemsys", "volume", "density", "density_atomic", "symmetry", "material_id", "deprecated", "deprecation_reasons", "last_updated", "origins", "warnings", "structure", "property_name", "description", "condensed_structure", "robocrys_version", ], "emmet.core.similarity.SimilarityDoc": [ "builder_meta", "nsites", "elements", "nelements", "composition", "composition_reduced", "formula_pretty", "formula_anonymous", "chemsys", "volume", "density", "density_atomic", "symmetry", "material_id", "method", "deprecated", "deprecation_reasons", "last_updated", "origins", "warnings", "structure", "property_name", "sim", "feature_vector", ], "emmet.core.substrates.SubstratesDoc": [ "sub_form", "sub_id", "film_orient", "area", "energy", "film_id", "norients", "orient", ], "emmet.core.summary.SummaryDoc": [ "prop_origins", # exclude=True and SkipJsonSchema in model "builder_meta", "nsites", "elements", "nelements", "composition", "composition_reduced", "formula_pretty", "formula_anonymous", "chemsys", "volume", "density", "density_atomic", "symmetry", "material_id", "deprecated", "deprecation_reasons", "last_updated", "origins", "phonon_IDs", "warnings", "structure", "property_name", "task_ids", "uncorrected_energy_per_atom", "energy_per_atom", "formation_energy_per_atom", "energy_above_hull", "is_stable", "equilibrium_reaction_energy_per_atom", "decomposes_to", "xas", "grain_boundaries", "band_gap", "cbm", "vbm", "efermi", "is_gap_direct", "is_metal", "bandstructure", "dos", "dos_energy_up", "dos_energy_down", "is_magnetic", "ordering", "total_magnetization", "total_magnetization_normalized_vol", "total_magnetization_normalized_formula_units", "num_magnetic_sites", "num_unique_magnetic_sites", "types_of_magnetic_species", "bulk_modulus", "shear_modulus", "universal_anisotropy", "homogeneous_poisson", "e_total", "e_ionic", "e_electronic", "n", "e_ij_max", "weighted_surface_energy_EV_PER_ANG2", "weighted_surface_energy", "weighted_work_function", "surface_anisotropy", "shape_factor", "has_reconstructed", "possible_species", "has_props", "theoretical", "database_IDs", ], "emmet.core.surface_properties.SurfacePropDoc": [ "surfaces", "weighted_surface_energy_EV_PER_ANG2", "weighted_surface_energy", "surface_anisotropy", "pretty_formula", "shape_factor", "weighted_work_function", "has_reconstructed", "material_id", "structure", ], "emmet.core.synthesis.core.SynthesisSearchResultModel": [ "doi", "paragraph_string", "synthesis_type", "reaction_string", "reaction", "target", "targets_formula", "precursors_formula", "targets_formula_s", "precursors_formula_s", "precursors", "operations", "search_score", "highlights", ], "emmet.core.tasks.TaskDoc": [ "nsites", "elements", "nelements", "composition", "composition_reduced", "formula_pretty", "formula_anonymous", "chemsys", "volume", "density", "density_atomic", "symmetry", "batch_id", "calc_type", "completed_at", "dir_name", "icsd_id", "input", "last_updated", "orig_inputs", "output", "run_type", "structure", "tags", "task_id", "task_type", "transformations", "vasp_objects", "additional_json", "analysis", "author", "calcs_reversed", "custodian", "entry", "included_objects", "run_stats", "state", "task_label", "vasp_version", ], "emmet.core.thermo.ThermoDoc": [ "builder_meta", "nsites", "elements", "nelements", "composition", "composition_reduced", "formula_pretty", "formula_anonymous", "chemsys", "volume", "density", "density_atomic", "symmetry", "material_id", "deprecated", "deprecation_reasons", "last_updated", "origins", "warnings", "structure", "property_name", "thermo_type", "uncorrected_energy_per_atom", "energy_per_atom", "energy_uncertainy_per_atom", "formation_energy_per_atom", "energy_above_hull", "is_stable", "equilibrium_reaction_energy_per_atom", "decomposes_to", "decomposition_enthalpy", "decomposition_enthalpy_decomposes_to", "energy_type", "entry_types", "entries", ], "emmet.core.vasp.material.MaterialsDoc": [ "builder_meta", "nsites", "elements", "nelements", "composition", "composition_reduced", "formula_pretty", "formula_anonymous", "chemsys", "volume", "density", "density_atomic", "symmetry", "material_id", "deprecated", "deprecation_reasons", "last_updated", "origins", "warnings", "structure", "initial_structures", "task_ids", "deprecated_tasks", "calc_types", "created_at", "task_types", "run_types", "entries", ], "emmet.core.xas.XASDoc": [ "builder_meta", "nsites", "elements", "nelements", "composition", "composition_reduced", "formula_pretty", "formula_anonymous", "chemsys", "volume", "density", "density_atomic", "symmetry", "spectrum_name", "task_id", "last_updated", "warnings", "spectrum", "absorbing_element", "spectrum_type", "edge", ], } @pytest.mark.parametrize("import_str,ref_fields", ref_model_fields.items()) def test_model_field_drift(import_str, ref_fields): module, class_name = import_str.rsplit(".", 1) model_class = getattr(import_module(module), class_name) assert set(model_class.model_fields) == set(ref_fields) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8322716 emmet_core-0.87.2/tests/test_molecule_metadata.py0000644000175100017510000000316115240717720021623 0ustar00runnerrunnerimport pytest from emmet.core.io.pymatgen import Molecule, Composition, Element from emmet.core.structure import MoleculeMetadata @pytest.fixture() def molecule(): return Molecule(species=["O", "O"], coords=[[0, 0, 0], [0.0, 0.0, 1.16]]) def test_from_molecule(molecule): metadata = MoleculeMetadata.from_molecule(molecule, extra_field="extra_value") assert metadata.natoms == 2 assert metadata.elements == [Element("O")] assert metadata.nelements == 1 assert metadata.composition == Composition("O2") assert metadata.composition_reduced == Composition("O2").reduced_composition assert metadata.formula_alphabetical == "O2" assert metadata.formula_pretty == "O2" assert metadata.formula_anonymous == "A" assert metadata.chemsys == "O" assert metadata.symmetry.point_group == "D*h" assert metadata.charge == 0 assert metadata.spin_multiplicity == 1 assert metadata.nelectrons == 16 assert metadata.model_config.get("extra") is None, ( "Should not allow extra field to keep validation strict, if " "extra fields are needed, set extra='allow' on a subclass" ) assert metadata.model_dump().get("extra_field") is None def test_from_comp(molecule): metadata = MoleculeMetadata.from_composition(molecule.composition) assert metadata.elements == [Element("O")] assert metadata.nelements == 1 assert metadata.composition == Composition("O2") assert metadata.composition_reduced == Composition("O2").reduced_composition assert metadata.formula_pretty == "O2" assert metadata.formula_anonymous == "A" assert metadata.chemsys == "O" ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8322716 emmet_core-0.87.2/tests/test_mpid.py0000644000175100017510000002402515240717720017111 0ustar00runnerrunner"""Test identifier-related tools in emmet-core.""" import json import numpy as np from pydantic import BaseModel import pytest from emmet.core.mpid import ( MPID, MPculeID, AlphaID, VALID_ALPHA_SEPARATORS, validate_identifier, ) def random_identifiers(num_idx: int = 10) -> set[str | MPID | AlphaID]: randints = set() c = 0 while len(randints) < num_idx: # ensure mix of IDs which are MPID and AlphaID if c % 2 == 0: low = 1 high = AlphaID._cut_point else: low = AlphaID._cut_point high = 1_000_000_000 randints.add(np.random.randint(low=low, high=high)) c += 1 idxs = set() for idx in randints: idxs.update({f"mp-{idx}", MPID(f"mvc-{idx}"), AlphaID(idx)}) return idxs def test_mpid(): assert MPID("mp-3") == MPID("mp-3") assert MPID("mp-3") < 3 assert MPID("mp-3") < MPID("np-3") assert MPID("mp-3") > MPID("mp-2") assert 3 > MPID("mp-3") assert MPID(MPID("mp-1234")) < MPID(1234) assert "mp-1234" < MPID(1234) assert MPID("1234") > MPID("mp-1234") assert MPID("1234") == MPID(1234) assert MPID("1234") == "1234" assert MPID("mp-12345") > MPID("mp-1234") assert min( [MPID("mp-44545"), MPID("mp-33"), MPID("mp-2134234"), MPID(33), MPID("33")] ) == MPID("mp-33") assert ( len(set([MPID("mp-33"), MPID("mp-44545"), MPID("mp-33"), MPID("mp-2134234")])) == 3 ) for invalid_value, msg in { "-100": "MPID string representation must follow", -14: "MPID cannot represent a negative integer.", }.items(): with pytest.raises(ValueError, match=msg): MPID(invalid_value) MPID(3) ulid_mpid = MPID("01HMVV88CCQ6JQ2Y1N8F3ZTVWP-Li") assert ulid_mpid.parts == ("01HMVV88CCQ6JQ2Y1N8F3ZTVWP", 0) with pytest.raises(ValueError, match="MPID string representation must follow"): MPID("GGIRADF") def test_mpculeid(): assert MPculeID("b9ba54febc77d2a9177accf4605767db-F6Li1P1-1-2") == MPculeID( "b9ba54febc77d2a9177accf4605767db-F6Li1P1-1-2" ) assert ( MPculeID("b9ba54febc77d2a9177accf4605767db-F6Li1P1-1-2") == "b9ba54febc77d2a9177accf4605767db-F6Li1P1-1-2" ) assert MPculeID("b9ba54febc77d2a9177accf4605767db-F6Li1P1-1-2") < MPculeID( "b9ba54febc77d2a9177accf4605767db-F6Li1P1-2-1" ) assert MPculeID("b9ba54febc77d2a9177accf4605767db-F6Li1P1-2-1") > MPculeID( "b9ba54febc77d2a9177accf4605767db-F6Li1P1-1-2" ) assert MPculeID("mpcule-98bab8f3795eae3fd8e28f5ff2d476e8-C3H8-0-1") < MPculeID( "b9ba54febc77d2a9177accf4605767db-F6Li1P1-1-2" ) def test_to_str(): assert str(MPID("mp-149")) == "mp-149" assert ( str(MPculeID("b9ba54febc77d2a9177accf4605767db-F6Li1P1-1-2")) == "b9ba54febc77d2a9177accf4605767db-F6Li1P1-1-2" ) def test_alpha_id(): # Test initialization from MPID-like string mpid_like_idx = AlphaID("mp-149") assert mpid_like_idx == AlphaID(149, prefix="mp", separator="-") assert mpid_like_idx._prefix == "mp" assert mpid_like_idx._separator == "-" assert int(mpid_like_idx) == 149 assert mpid_like_idx == MPID("mp-149") assert mpid_like_idx == AlphaID(MPID("mp-149")) assert AlphaID("mvc-160") == MPID("mvc-160") assert AlphaID(160) == MPID("160") assert mpid_like_idx + MPID("mp-140") == AlphaID("mp-289") assert mpid_like_idx - MPID("mp-140") == AlphaID("mp-9") # Test relationality - should still compare integers when prefix and separator don't match assert mpid_like_idx > MPID("mp-140") mvc_idx = AlphaID("mvc-140") assert not mpid_like_idx < mvc_idx assert mpid_like_idx > mvc_idx majik_num = 137 ref_idx = AlphaID(majik_num, prefix="cats", separator=":") assert ( ref_idx.__repr__() == f"AlphaID(cats:{AlphaID._integer_to_alpha_rep(majik_num)})" ) assert ref_idx < AlphaID( majik_num + 100, prefix=ref_idx._prefix, separator=ref_idx._separator ) assert ref_idx > AlphaID( majik_num - 100, prefix=ref_idx._prefix, separator=ref_idx._separator ) # test padding padded_idx = AlphaID(majik_num, prefix="cats", separator=":", padlen=8) assert ref_idx == padded_idx assert int(ref_idx) == int(padded_idx) assert str(padded_idx) == ref_idx._prefix + ref_idx._separator + ( 8 - len(str(ref_idx._identifier)) ) * "a" + str(ref_idx._identifier) # test invalid sep for sep in ("^", "&", "%", "$", "#"): with pytest.raises(ValueError, match="Invalid separator"): AlphaID(majik_num, prefix="v", separator=sep) # test copy ref_idx_copy = ref_idx.copy() ref_idx += 1 assert ref_idx != ref_idx_copy assert AlphaID(majik_num, padlen=10) < AlphaID(majik_num + 1) assert AlphaID(majik_num, padlen=10) > AlphaID(majik_num - 1) # Test initialization from int ordered_seps = list(VALID_ALPHA_SEPARATORS) for pfx in (None, "task"): for isep, separator in enumerate(ordered_seps): idx = AlphaID(majik_num, prefix=pfx, separator=separator) assert isinstance(hash(idx), int) # Roundtrip assert idx == AlphaID(str(idx)) assert int(idx) == majik_num # Test integer equality assert idx == majik_num # Test in/equality when prefix and separators do not match assert idx != AlphaID(majik_num, prefix="unusedprefix") # Test equality when separators differ but prefixes are None # (separators are unset) idx_diff_sep = AlphaID( majik_num, prefix=pfx, separator=ordered_seps[ (isep + 1) % len(ordered_seps) ], # ensures separator differs ) if pfx is None: assert idx == idx_diff_sep else: assert idx != idx_diff_sep # Test integer addition assert idx + 1 == majik_num + 1 assert idx + 1 == AlphaID(majik_num + 1, prefix=pfx, separator=separator) # Test string addition/subtraction assert idx + "y" == AlphaID(majik_num + 24, prefix=pfx, separator=separator) assert idx - "z" == AlphaID(majik_num - 25, prefix=pfx, separator=separator) # Test equality, addition, subtraction of AlphaID other_idx = AlphaID("task:100") assert other_idx != idx if pfx == "task" and separator == ":": assert idx - other_idx == majik_num - 100 assert idx - other_idx == AlphaID(f"task:{majik_num - 100}") assert idx + other_idx == majik_num + 100 assert idx + other_idx == AlphaID(f"task:{majik_num + 100}") else: match_strs = [] if pfx != "task": match_strs.append("Prefixes do not match") if pfx and separator != ":": match_strs.append("Separators do not match") for match_str in match_strs: with pytest.raises(TypeError, match=match_str): idx - other_idx with pytest.raises(TypeError, match=match_str): idx + other_idx # Can't add nor compare floats with AlphaID with pytest.raises(NotImplementedError, match="Cannot compare AlphaID"): idx + 1.0 with pytest.raises(NotImplementedError, match="Cannot compare AlphaID"): idx < float(int(idx)) # test sorting test_ints = (10, 100, 50, 20, 50000) test_idxs = [AlphaID(i) for i in test_ints] sorted_idxs = sorted(test_idxs) assert all(isinstance(_idx, AlphaID) for _idx in sorted_idxs) assert [int(v) for v in sorted_idxs] == sorted(test_ints) assert max(test_idxs) == AlphaID(max(test_ints)) assert min(test_idxs) == AlphaID(min(test_ints)) # test iterative addition / subtraction last_val = None next_val = None for i in range(5000): alpha = AlphaID(i) assert int(alpha) == i if i > 0: assert alpha - 1 == last_val assert alpha - AlphaID(1) == last_val assert alpha == next_val last_val = alpha next_val = alpha + 1 # For legacy purposes, ensure that MPIDs with lower values # present as int when calling .string assert AlphaID("mp-149").string == "mp-149" assert AlphaID("mp-3347529").string == "mp-3347529" assert AlphaID("mp-3347530").string == str(AlphaID("mp-3347530")) for invalid_val, msg in { "-100": "Missing prefix and/or identifer.", "pfx-": "Missing prefix and/or identifer.", ":": "Missing prefix and/or identifer.", -100: "AlphaID cannot represent a negative integer.", }.items(): with pytest.raises(ValueError, match=msg): AlphaID(invalid_val) # Test next safe AlphaID # The following words are "obscene" in a # non-English language but are safe in English # Test works by ensuring that the AlphaID less than # a profane word skips the profane word. for word in ("pano", "johny", "sega"): curr_alpha_id = AlphaID(word) prev_alpha_id = curr_alpha_id - 1 assert prev_alpha_id.next_safe == curr_alpha_id + 1 @pytest.mark.parametrize("id_cls", [MPID, AlphaID]) def test_pydantic(id_cls): # test that AlphaID is supported by pydantic de-/serialization class TestClass(BaseModel): ID: id_cls idx = id_cls(101010) test_case = TestClass(ID=idx) assert test_case.model_dump() == {"ID": idx} assert json.loads(test_case.model_dump_json()) == {"ID": str(idx)} with pytest.raises(ValueError, match=f"Invalid {id_cls.__name__} Format"): TestClass(ID=10.0) def test_validate_on_mpid_alpha(): for idx in random_identifiers(): assert validate_identifier(idx, serialize=False) == AlphaID(idx).formatted assert validate_identifier(idx, serialize=True) == str(AlphaID(idx)) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8322716 emmet_core-0.87.2/tests/test_neb.py0000644000175100017510000001232615240717720016725 0ustar00runnerrunner"""Test NEB document class.""" import shutil from pathlib import Path from tempfile import TemporaryDirectory import pytest from monty.serialization import loadfn from emmet.core.io.pymatgen import Structure from emmet.core.neb import ( BarrierAnalysis, NebIntermediateImagesDoc, NebMethod, NebPathwayResult, NebResult, NebTaskDoc, ) from emmet.core.testing_utils import assert_schemas_equal from emmet.core.vasp.calculation import Calculation, CalculationInput @pytest.fixture(scope="module") def neb_test_dir(test_dir: Path) -> Path: return test_dir / "neb" @pytest.mark.parametrize("from_dir", [True, False]) def test_neb_task_doc(neb_test_dir, from_dir: bool): if from_dir: with TemporaryDirectory() as tmpdir: shutil.unpack_archive(neb_test_dir / "neb_sample_calc.zip", tmpdir, "zip") neb_doc = NebIntermediateImagesDoc.from_directory(Path(tmpdir) / "neb") num_images = 3 intermed_idxs = (0, 1, 2) assert isinstance(neb_doc.orig_inputs, CalculationInput) assert isinstance(neb_doc.inputs, CalculationInput) else: neb_doc = NebTaskDoc(**loadfn(neb_test_dir / "Si_neb_doc.json.bz2")) num_images = 5 intermed_idxs = (1, 2, 3) assert neb_doc.num_images == num_images neb_doc_dict = neb_doc.model_dump() assert len(neb_doc.images) == num_images assert len(neb_doc.energies) == num_images assert len(neb_doc.images) == num_images # test that NEB image calculations are all VASP Calculation objects assert len(neb_doc.calculations) == num_images for image_idx, image_calc in enumerate(neb_doc.calculations): assert_schemas_equal(image_calc, neb_doc_dict["calculations"][image_idx]) assert isinstance(image_calc, Calculation) assert all(isinstance(doc.input, CalculationInput) for doc in neb_doc.calculations) # check NEB method parsing assert neb_doc.neb_method == NebMethod.CLIMBING_IMAGE # check that all structures exist assert all(isinstance(image, Structure) for image in neb_doc.images) # Check that image calculation dirs all have common root / expected format assert all( neb_doc.directories[idx].startswith(str(neb_doc.dir_name)) for idx in intermed_idxs ) assert all( Path(neb_doc.directories[idx]) == Path(neb_doc.dir_name) / f"{image_idx+1:02}" for image_idx, idx in enumerate(intermed_idxs) ) # Check that VASP objects stored for each image calc assert len(neb_doc.objects) == num_images assert all( energy == pytest.approx(neb_doc.calculations[idx].output.energy) for idx, energy in enumerate(neb_doc.energies) ) def test_from_directories(neb_test_dir): with TemporaryDirectory() as tmpdir: tmpdir = Path(tmpdir) shutil.unpack_archive(neb_test_dir / "neb_sample_calc.zip", tmpdir, "zip") neb_doc = NebTaskDoc.from_directories( [tmpdir / f"relax_endpoint_{idx+1}" for idx in range(2)], tmpdir / "neb", ) assert all(isinstance(calc, Calculation) for calc in neb_doc.calculations) for i, ep_dir in enumerate(neb_doc.directories): if i == 0 or i == neb_doc.num_images - 1: assert "relax_endpoint_" in ep_dir else: assert f"neb/{i:02}" in ep_dir assert len(neb_doc.energies) == neb_doc.num_images assert len(neb_doc.images) == neb_doc.num_images assert isinstance(neb_doc.barrier_analysis, BarrierAnalysis) assert all( getattr(neb_doc.barrier_analysis, k, None) is not None for k in ( "energies", "frame_index", "cubic_spline_pars", "ts_frame_index", "ts_energy", "ts_in_frames", "forward_barrier", "reverse_barrier", ) ) assert all( getattr(neb_doc, f"{direction}_barrier") == getattr(neb_doc.barrier_analysis, f"{direction}_barrier", None) for direction in ("forward", "reverse") ) def test_top_level_assignment(test_dir, mp_149_structure): fictive_doc = NebPathwayResult( hops={ k: NebResult(forward_barrier=barriers[0], reverse_barrier=barriers[1]) for k, barriers in { "a": (1.0, None), "b": (5.0, 10.0), "c": (None, 0.1), }.items() }, host_structure=mp_149_structure, ) assert fictive_doc.forward_barriers == { "a": 1.0, "b": 5.0, "c": None, } assert fictive_doc.reverse_barriers == { "a": None, "b": 10.0, "c": 0.1, } assert fictive_doc.max_barriers == { "a": 1.0, "b": 10.0, "c": 0.1, } assert fictive_doc.host_formula == mp_149_structure.formula assert fictive_doc.host_formula_reduced == mp_149_structure.reduced_formula assert fictive_doc.host_chemsys == mp_149_structure.chemical_system energies = { "0+1": [0.0, 0.5, 0.1], "0+2": [-0.5, 1.25, 0.75], } fictive_doc = NebPathwayResult( hops={k: NebResult(energies=ens) for k, ens in energies.items()} ) assert fictive_doc.barrier_ranges == { k: max(ens) - min(ens) for k, ens in energies.items() } ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8322716 emmet_core-0.87.2/tests/test_optimade.py0000644000175100017510000000127315240717720017762 0ustar00runnerrunnerimport pytest from emmet.core.io.pymatgen import Structure from . import test_structures from emmet.core.utils import utcnow try: from emmet.core.optimade import OptimadeMaterialsDoc except Exception: pytest.skip("could not import 'optimade' ", allow_module_level=True) @pytest.mark.xfail(reason="Optimade + fastapi issues.") @pytest.mark.parametrize("structure", test_structures.values()) def test_oxidation_state(structure: Structure): """Very simple test to make sure this actually works""" print(f"Should work : {structure.composition}") doc = OptimadeMaterialsDoc.from_structure( structure, material_id=33, last_updated=utcnow() ) assert doc is not None ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8322716 emmet_core-0.87.2/tests/test_oxidation_states.py0000644000175100017510000000173215240717720021541 0ustar00runnerrunnerimport pytest from emmet.core.oxidation_states import OxidationStateDoc, OxiStateAssigner from emmet.core.io.pymatgen import Structure from . import test_structures @pytest.mark.parametrize("structure", test_structures.values()) def test_oxidation_state(structure: Structure): """Very simple test to make sure this actually works""" struct_has_charges_assigned = ( structure.composition.copy().remove_charges() != structure.composition ) for method in ( None, "bva", ): doc = OxidationStateDoc.from_structure( structure, material_id=33, deprecated=False ) assert doc is not None assert doc.structure is not None if struct_has_charges_assigned and not method: assert doc.method == OxiStateAssigner.MANUAL elif doc.possible_valences: assert doc.method in {OxiStateAssigner.BVA, OxiStateAssigner.GUESS} else: assert doc.method is None ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8322716 emmet_core-0.87.2/tests/test_phonon.py0000644000175100017510000001360415240717720017462 0ustar00runnerrunner"""Test phonon document models.""" from copy import deepcopy from tempfile import NamedTemporaryFile import numpy as np import pytest from monty.serialization import loadfn from emmet.core.io.pymatgen import ( Structure, CompletePhononDos, PhononDos as PmgPhononDos, ) from emmet.core import ARROW_COMPATIBLE from emmet.core.phonon import PhononBSDOSDoc, PhononDOS from emmet.core.testing_utils import assert_schemas_equal if ARROW_COMPATIBLE: import pyarrow as pa from emmet.core.arrow import arrowize @pytest.fixture(scope="module") def legacy_ph_task(test_dir): return loadfn(test_dir / "mp-23302_phonon.json.gz", cls=None) def test_legacy_migration(legacy_ph_task): # ensure that legacy phonon data can be migrated to current schema assert all(legacy_ph_task.get(k) for k in ("ph_bs", "ph_dos")) # deepcopy needed because migration changes input objects ph_doc = PhononBSDOSDoc.migrate_fields(**deepcopy(legacy_ph_task)) assert_schemas_equal(ph_doc, PhononBSDOSDoc.model_config) # check remap of phonon DOS for k in ("densities", "frequencies"): assert np.all( np.abs( np.array(getattr(ph_doc.phonon_dos, k, [])) - np.array(legacy_ph_task["ph_dos"].get(k, [])) ) < 1e-6 ) # check remap of phonon bandstructure assert np.all( np.abs( np.array(getattr(ph_doc.phonon_bandstructure, "frequencies", [])) - np.array(legacy_ph_task["ph_bs"].get("bands", [])) ) < 1e-6 ) # check that Phonon DOS converts to CompletePhononDOS object assert isinstance(ph_doc.phonon_dos.to_pmg, CompletePhononDos) # when structure or projected DOS fields are missing, `to_pmg` returns a PhononDos object for k in ( "structure", "projected_densities", ): model_config = deepcopy(ph_doc.model_dump()) model_config["phonon_dos"].pop(k) new_task = PhononBSDOSDoc.migrate_fields(**model_config) assert isinstance(new_task.phonon_dos.to_pmg, PmgPhononDos) temps = [5, 100, 300, 500, 800] ref_data = { "entropy": [ 0.10690825310989084, 64.92923008995054, 118.49759120562243, 143.87894281334866, 167.29013558233882, ], "heat_capacity": [ 0.3096320518171239, 47.157121712113714, 49.56965404227773, 49.77072796011147, 49.8399369784691, ], "internal_energy": [ 1988.2321308731728, 5269.03083181402, 15060.035206972329, 24999.080865250606, 39943.07283219206, ], "free_energy": [ 1987.697589607624, -1223.8921771810346, -20489.242154714408, -46940.39054142372, -93889.03563367906, ], } tprops = ph_doc.compute_thermo_quantities(temps) assert all( t == pytest.approx(tprops["temperature"][i]) for i, t in enumerate(temps) ) for k, ref_vals in ref_data.items(): assert all( tprops[k][i] == pytest.approx(ref_val) for i, ref_val in enumerate(ref_vals) ) # check population of structure metadata fields assert ph_doc.composition == ph_doc.structure.composition assert ph_doc.volume == ph_doc.structure.volume assert ph_doc.nelements == len(ph_doc.structure.composition.elements) assert sorted(ph_doc.elements) == sorted(ph_doc.structure.composition.elements) # check that crystal toolkit convenience function works assert ph_doc.phonon_bandstructure._to_pmg_es_bs.get_vbm() assert ph_doc.phonon_bandstructure._to_pmg_es_bs.get_cbm() @pytest.mark.skipif( not ARROW_COMPATIBLE, reason="pyarrow must be installed to run this test." ) def test_arrow(tmp_dir, legacy_ph_task): ph_doc = PhononBSDOSDoc.migrate_fields(**legacy_ph_task) arrow_struct = pa.scalar( ph_doc.model_dump(context={"format": "arrow"}), type=arrowize(PhononBSDOSDoc) ) test_arrow_doc = PhononBSDOSDoc(**arrow_struct.as_py(maps_as_pydicts="strict")) assert test_arrow_doc assert ph_doc.phonon_bandstructure == test_arrow_doc.phonon_bandstructure # test primitive structure caching assert test_arrow_doc.phonon_bandstructure._primitive_structure is None assert isinstance( test_arrow_doc.phonon_bandstructure.primitive_structure, Structure ) assert ( test_arrow_doc.phonon_bandstructure._primitive_structure == test_arrow_doc.phonon_bandstructure.primitive_structure ) def test_phonopy_dos_integration(tmp_dir): """Test phonopy dat-file parsing.""" temp = 0.063253 ph_data = np.random.random((20, 2)) ph_str = f"# Sigma = {temp}\n\t" + "\n\t".join( "\t".join(f"{x}" for x in row) for row in ph_data ) temp_file = NamedTemporaryFile(suffix="dat", mode="w") temp_file.write(ph_str) temp_file.seek(0) ph_dos = PhononDOS.from_phonopy(temp_file.name) temp_file.close() for idx, attr in enumerate(["frequencies", "densities"]): assert np.all(np.abs(ph_data[:, idx] - np.array(getattr(ph_dos, attr))) < 1e-6) def test_acoustic_sum_rule_per_atom_row_sum(): """Test for the acoustic_sum_rule formula.""" rng = np.random.default_rng(seed=0) n_atoms = 4 # ASR-correct: zero out each row's mean so sum_j Phi_ij = 0 for every i. fcs = rng.standard_normal((n_atoms, n_atoms, 3, 3)) fcs -= fcs.mean(axis=1, keepdims=True) doc = PhononBSDOSDoc(force_constants=fcs.tolist()) max_dev = np.abs(doc.acoustic_sum_rule).max() assert max_dev < 1e-12 assert max_dev == doc.check_sum_rule_deviations.asr # Violation: all-ones FCs --> every row sum is n_atoms. fcs_bad = np.ones((n_atoms, n_atoms, 3, 3)) doc_bad = PhononBSDOSDoc(force_constants=fcs_bad.tolist()) assert doc_bad.check_sum_rule_deviations.asr == float(n_atoms) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8322716 emmet_core-0.87.2/tests/test_polar.py0000644000175100017510000002625515240717720017304 0ustar00runnerrunnerimport pytest from emmet.core.io.pymatgen import Lattice, Structure from emmet.core import ARROW_COMPATIBLE from emmet.core.polar import DielectricDoc, PiezoelectricDoc if ARROW_COMPATIBLE: import pyarrow as pa from emmet.core.arrow import arrowize @pytest.fixture(scope="module") def dielectric_structure(): test_latt = Lattice.cubic(3.0) test_struc = Structure(lattice=test_latt, species=["Fe"], coords=[[0, 0, 0]]) return test_struc @pytest.fixture(scope="module") def epsilon_static(): return [ [10.81747665, -0.00378371, 0.0049036], [-0.00373185, 10.82629335, -0.00432847], [0.0036548, -0.00479139, 8.68204827], ] @pytest.fixture(scope="module") def epsilon_ionic(): return [ [30.98960925, -0.09107371, 0.00226948], [-0.09107371, 31.44264572, -0.00427919], [0.00226948, -0.00427919, 29.21747234], ] def test_dielectric(dielectric_structure, epsilon_static, epsilon_ionic): doc = DielectricDoc.from_ionic_and_electronic( material_id="mp-149", structure=dielectric_structure, epsilon_static=epsilon_static, epsilon_ionic=epsilon_ionic, deprecated=False, ) assert isinstance(doc, DielectricDoc) assert doc.property_name == "dielectric" assert doc.material_id == "mp-149" assert doc.n == pytest.approx(3.17940376590938) assert doc.e_total == pytest.approx(40.6585061611482) assert doc.e_ionic == pytest.approx(30.5498978544694) @pytest.mark.skipif( not ARROW_COMPATIBLE, reason="pyarrow must be installed to run this test." ) def test_arrow_dielectric(dielectric_structure, epsilon_static, epsilon_ionic): doc = DielectricDoc.from_ionic_and_electronic( material_id="mp-149", structure=dielectric_structure, epsilon_static=epsilon_static, epsilon_ionic=epsilon_ionic, deprecated=False, ) arrow_struct = pa.scalar( doc.model_dump(context={"format": "arrow"}), type=arrowize(DielectricDoc) ) test_arrow_doc = DielectricDoc(**arrow_struct.as_py(maps_as_pydicts="strict")) assert doc.model_dump() == test_arrow_doc.model_dump() @pytest.fixture(scope="module") def piezoelectric_structure(): d = { "@module": "pymatgen.core.structure", "@class": "Structure", "charge": None, "lattice": { "matrix": [ [0.0, 5.077586, 0.0], [8.769167, 0.0, 0.0], [0.0, -1.7206, -4.819114], ], "a": 5.077586, "b": 8.769167, "c": 5.11706205795826, "alpha": 90.0, "beta": 109.648423669999, "gamma": 90.0, "volume": 214.576831815117, }, "sites": [ { "species": [{"element": "Li", "occu": 1}], "abc": [0.5, 0.997223, 0.0], "xyz": [8.744815023241, 2.538793, 0.0], "label": "Li", "properties": {}, }, { "species": [{"element": "Li", "occu": 1}], "abc": [0.0, 0.007335, 0.5], "xyz": [0.064321839945, -0.8603, -2.409557], "label": "Li", "properties": {}, }, { "species": [{"element": "Li", "occu": 1}], "abc": [0.5, 0.673599, 0.0], "xyz": [5.906902122033, 2.538793, 0.0], "label": "Li", "properties": {}, }, { "species": [{"element": "Li", "occu": 1}], "abc": [0.0, 0.848636, 0.0], "xyz": [7.441830806212, 0.0, 0.0], "label": "Li", "properties": {}, }, { "species": [{"element": "Li", "occu": 1}], "abc": [0.0, 0.497223, 0.0], "xyz": [4.360231523241, 0.0, 0.0], "label": "Li", "properties": {}, }, { "species": [{"element": "Li", "occu": 1}], "abc": [0.5, 0.507335, 0.5], "xyz": [4.448905339945, 1.678493, -2.409557], "label": "Li", "properties": {}, }, { "species": [{"element": "Li", "occu": 1}], "abc": [0.0, 0.173599, 0.0], "xyz": [1.522318622033, 0.0, 0.0], "label": "Li", "properties": {}, }, { "species": [{"element": "Li", "occu": 1}], "abc": [0.5, 0.348636, 0.0], "xyz": [3.057247306212, 2.538793, 0.0], "label": "Li", "properties": {}, }, { "species": [{"element": "Fe", "occu": 1}], "abc": [0.5, 0.840139, 0.5], "xyz": [7.367319194213, 1.678493, -2.409557], "label": "Fe", "properties": {}, }, { "species": [{"element": "Fe", "occu": 1}], "abc": [0.0, 0.674037, 0.5], "xyz": [5.910743017179, -0.8603, -2.409557], "label": "Fe", "properties": {}, }, { "species": [{"element": "Fe", "occu": 1}], "abc": [0.0, 0.340139, 0.5], "xyz": [2.982735694213, -0.8603, -2.409557], "label": "Fe", "properties": {}, }, { "species": [{"element": "Fe", "occu": 1}], "abc": [0.5, 0.174037, 0.5], "xyz": [1.526159517179, 1.678493, -2.409557], "label": "Fe", "properties": {}, }, { "species": [{"element": "O", "occu": 1}], "abc": [0.779705, 0.500278, 0.268671], "xyz": [4.387021328426, 3.49674386953, -1.294756177494], "label": "O", "properties": {}, }, { "species": [{"element": "O", "occu": 1}], "abc": [0.720295, 0.000278, 0.731329], "xyz": [0.002437828426, 2.39903513047, -3.524357822506], "label": "O", "properties": {}, }, { "species": [{"element": "O", "occu": 1}], "abc": [0.755993, 0.189934, 0.270851], "xyz": [1.665562964978, 3.372593242298, -1.305261846014], "label": "O", "properties": {}, }, { "species": [{"element": "O", "occu": 1}], "abc": [0.744007, 0.689934, 0.729149], "xyz": [6.050146464978, 2.523185757702, -3.513852153986], "label": "O", "properties": {}, }, { "species": [{"element": "O", "occu": 1}], "abc": [0.766434, 0.333483, 0.723505], "xyz": [2.924368118661, 2.646771845324, -3.48665307457], "label": "O", "properties": {}, }, { "species": [{"element": "O", "occu": 1}], "abc": [0.733566, 0.833483, 0.276495], "xyz": [7.308951618661, 3.249007154676, -1.33246092543], "label": "O", "properties": {}, }, { "species": [{"element": "O", "occu": 1}], "abc": [0.279705, 0.000278, 0.268671], "xyz": [0.002437828426, 0.95795086953, -1.294756177494], "label": "O", "properties": {}, }, { "species": [{"element": "O", "occu": 1}], "abc": [0.220295, 0.500278, 0.731329], "xyz": [4.387021328426, -0.13975786953, -3.524357822506], "label": "O", "properties": {}, }, { "species": [{"element": "O", "occu": 1}], "abc": [0.255993, 0.689934, 0.270851], "xyz": [6.050146464978, 0.833800242298, -1.305261846014], "label": "O", "properties": {}, }, { "species": [{"element": "O", "occu": 1}], "abc": [0.244007, 0.189934, 0.729149], "xyz": [1.665562964978, -0.015607242298, -3.513852153986], "label": "O", "properties": {}, }, { "species": [{"element": "O", "occu": 1}], "abc": [0.266434, 0.833483, 0.723505], "xyz": [7.308951618661, 0.107978845324, -3.48665307457], "label": "O", "properties": {}, }, { "species": [{"element": "O", "occu": 1}], "abc": [0.233566, 0.333483, 0.276495], "xyz": [2.924368118661, 0.710214154676, -1.33246092543], "label": "O", "properties": {}, }, ], } test_struc = Structure.from_dict(d) return test_struc @pytest.fixture(scope="module") def piezo_static(): return [ [0.07886, -0.07647, -0.01902, 0.0, -0.18077, 0.0], [0.0, 0.0, 0.0, -0.10377, 0.0, 0.18109], [0.0, 0.0, 0.0, -0.07831, 0.0, 0.04849], ] @pytest.fixture(scope="module") def piezo_ionic(): return [ [-0.53096, 0.12789, -0.01236, 0.0, 0.09352, 0.0], [-0.00013, 9e-05, 3e-05, 0.2681, 0.00042, -0.09373], [-0.00018, -9e-05, -0.00029, 0.15863, 0.0001, -0.22751], ] def test_piezoelectric(piezoelectric_structure, piezo_static, piezo_ionic): doc = PiezoelectricDoc.from_ionic_and_electronic( material_id="mp-149", structure=piezoelectric_structure, piezo_static=piezo_static, piezo_ionic=piezo_ionic, deprecated=False, ) assert isinstance(doc, PiezoelectricDoc) assert doc.property_name == "piezoelectric" assert doc.material_id == "mp-149" assert doc.e_ij_max == pytest.approx(0.464365904540805) assert [abs(n) for n in doc.strain_for_max] == pytest.approx( [ 0.0675760207481869, 0.97358569089405, 0.110731643941102, 0.0, 0.187890624929232, 0.0, ] ) total = [ [0.0, 0.0, 0.0, 0.08032, 0.0, -0.17902], [-0.03138, -0.4521, 0.05142, 0.0, -0.08725, 0.0], [0.0, 0.0, 0.0, 0.16433, 0.0, 0.08736], ] for i in range(3): assert doc.total[i] == pytest.approx(total[i]) @pytest.mark.skipif( not ARROW_COMPATIBLE, reason="pyarrow must be installed to run this test." ) def test_arrow_piezo(piezoelectric_structure, piezo_static, piezo_ionic): doc = PiezoelectricDoc.from_ionic_and_electronic( material_id="mp-149", structure=piezoelectric_structure, piezo_static=piezo_static, piezo_ionic=piezo_ionic, deprecated=False, ) arrow_struct = pa.scalar( doc.model_dump(context={"format": "arrow"}), type=arrowize(PiezoelectricDoc) ) test_arrow_doc = PiezoelectricDoc(**arrow_struct.as_py(maps_as_pydicts="strict")) assert doc.model_dump() == test_arrow_doc.model_dump() ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8322716 emmet_core-0.87.2/tests/test_provenance.py0000644000175100017510000000436015240717720020320 0ustar00runnerrunnerimport pytest from emmet.core.io.pymatgen import Lattice, Structure, Author, HistoryNode, StructureNL from emmet.core import ARROW_COMPATIBLE from emmet.core.provenance import ( Database, ProvenanceDescription, ProvenanceDoc, SNLDict, ) from emmet.core.utils import utcnow if ARROW_COMPATIBLE: import pyarrow as pa from emmet.core.arrow import arrowize @pytest.fixture def structure(): test_latt = Lattice.cubic(3.0) test_struc = Structure(lattice=test_latt, species=["Fe"], coords=[[0, 0, 0]]) return test_struc @pytest.fixture def snls(structure): docs = [ StructureNL( structure, authors=[Author("test{i}", "test@test.com").as_dict()], history=[HistoryNode("nothing", "url.com", {})], created_at=utcnow(), references="", ).as_dict() for i in range(3) ] docs[0]["snl_id"] = "icsd-2" docs[1]["snl_id"] = "user-1" docs[2]["snl_id"] = "pf-3" return [SNLDict(**d) for d in docs] def test_from_snls(snls, structure): doc = ProvenanceDoc.from_SNLs( material_id="mp-3", structure=structure, snls=snls, deprecated=False ) assert isinstance(doc, ProvenanceDoc) assert doc.property_name == "provenance" assert doc.material_id == "mp-3" assert doc.theoretical is True assert doc.database_IDs == { Database.ICSD: ["icsd-2"], } # Test experimental detection snls[0].about.history[0].description = ProvenanceDescription(experimental=True) assert ( ProvenanceDoc.from_SNLs( material_id="mp-3", snls=snls, structure=structure, deprecated=False ).theoretical is False ) assert doc.model_dump()["property_name"] == "provenance" @pytest.mark.skipif( not ARROW_COMPATIBLE, reason="pyarrow must be installed to run this test." ) def test_arrow(snls, structure): doc = ProvenanceDoc.from_SNLs( material_id="mp-3", structure=structure, snls=snls, deprecated=False ) arrow_struct = pa.scalar( doc.model_dump(context={"format": "arrow"}), type=arrowize(ProvenanceDoc) ) test_arrow_doc = ProvenanceDoc(**arrow_struct.as_py(maps_as_pydicts="strict")) assert doc.model_dump() == test_arrow_doc.model_dump() ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8322716 emmet_core-0.87.2/tests/test_qc_task.py0000644000175100017510000001017615240717720017607 0ustar00runnerrunnerimport pytest from emmet.core.testing_utils import assert_schemas_equal from tests.conftest_qchem import get_test_object, OPENBABEL_INSTALLED @pytest.mark.parametrize( "object_name, task_name", [ pytest.param("SinglePointTest", "standard", id="SinglePointTest"), pytest.param("OptimizationTest", "standard", id="OptimizationTest"), ], ) # Can add more later, something like freq, pesscan, ts, # FFOptTest, once we get flows working for qchem in atomate2 def test_input_summary(test_dir, object_name, task_name): from monty.json import MontyDecoder, jsanitize from emmet.core.qc_tasks import InputDoc from emmet.core.qchem.calculation import Calculation test_object = get_test_object(object_name) dir_name = test_dir / "qchem" / test_object.folder files = test_object.task_files[task_name] calc_doc = Calculation.from_qchem_files(dir_name, task_name, **files) test_doc = InputDoc.from_qchem_calc_doc(calc_doc) valid_doc = test_object.task_doc["input"] assert_schemas_equal(test_doc, valid_doc) # test document can be jsanitized d = jsanitize(test_doc, strict=True, enum_values=True, allow_bson=True) # and decoded MontyDecoder().process_decoded(d) @pytest.mark.parametrize( "object_name, task_name", [ pytest.param("SinglePointTest", "standard", id="SinglePointTest"), pytest.param("OptimizationTest", "standard", id="OptimizationTest"), ], ) def test_output_summary(test_dir, object_name, task_name): from monty.json import MontyDecoder, jsanitize from emmet.core.qc_tasks import OutputDoc from emmet.core.qchem.calculation import Calculation test_object = get_test_object(object_name) dir_name = test_dir / "qchem" / test_object.folder files = test_object.task_files[task_name] calc_doc = Calculation.from_qchem_files(dir_name, task_name, **files) test_doc = OutputDoc.from_qchem_calc_doc(calc_doc) valid_doc = test_object.task_doc["output"] assert_schemas_equal(test_doc, valid_doc) # test document can be janitized d = jsanitize(test_doc, strict=True, enum_values=True, allow_bson=True) # and decoded MontyDecoder().process_decoded(d) @pytest.mark.parametrize( "object_name", [ pytest.param("SinglePointTest", id="SinglePointTest"), pytest.param("OptimizationTest", id="OptimizationTest"), ], ) def test_task_doc(test_dir, object_name): from monty.json import MontyDecoder, jsanitize from emmet.core.qc_tasks import TaskDoc test_object = get_test_object(object_name) dir_name = test_dir / "qchem" / test_object.folder test_doc = TaskDoc.from_directory(dir_name) assert_schemas_equal(test_doc, test_object.task_doc) # test document can be jsanitized d = jsanitize(test_doc, strict=True, enum_values=True, allow_bson=True) # and decoded if not OPENBABEL_INSTALLED: d.pop("custodian", None) MontyDecoder().process_decoded(d) # Test that additional_fields works test_doc = TaskDoc.from_directory(dir_name, additional_fields={"foo": "bar"}) assert test_doc.model_dump()["additional_fields"] == {"foo": "bar"} @pytest.mark.parametrize( "object_name", [ pytest.param("SinglePointTest", id="SinglePointTest"), pytest.param("OptimizationTest", id="OptimizationTest"), ], ) def test_task_doc_val_flag(test_dir, object_name): from monty.json import MontyDecoder, jsanitize from emmet.core.qc_tasks import TaskDoc test_object = get_test_object(object_name) dir_name = test_dir / "qchem" / test_object.folder test_doc = TaskDoc.from_directory(dir_name, validate_lot=False) assert_schemas_equal(test_doc, test_object.task_doc) # test document can be jsanitized d = jsanitize(test_doc, strict=True, enum_values=True, allow_bson=True) # and decoded if not OPENBABEL_INSTALLED: d.pop("custodian", None) MontyDecoder().process_decoded(d) # Test that additional_fields works test_doc = TaskDoc.from_directory( dir_name, validate_lot=False, additional_fields={"foo": "bar"} ) assert test_doc.model_dump()["additional_fields"] == {"foo": "bar"} ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8322716 emmet_core-0.87.2/tests/test_robocrys.py0000644000175100017510000000227615240717720020026 0ustar00runnerrunnerimport pytest from emmet.core.io.pymatgen import Structure from . import test_structures from emmet.core import ARROW_COMPATIBLE from emmet.core.robocrys import RobocrystallogapherDoc if ARROW_COMPATIBLE: import pyarrow as pa from emmet.core.arrow import arrowize @pytest.mark.parametrize("structure", test_structures.values()) def test_robocrys(structure: Structure): """Very simple test to make sure this actually works""" doc = RobocrystallogapherDoc.from_structure( structure=structure, material_id=33, deprecated=False, ) assert doc is not None @pytest.mark.skipif( not ARROW_COMPATIBLE, reason="pyarrow must be installed to run this test." ) def test_arrow(): structure = next(iter(test_structures.values())) doc = RobocrystallogapherDoc.from_structure( structure=structure, material_id=33, deprecated=False, ) arrow_struct = pa.scalar( doc.model_dump(context={"format": "arrow"}), type=arrowize(RobocrystallogapherDoc), ) test_arrow_doc = RobocrystallogapherDoc( **arrow_struct.as_py(maps_as_pydicts="strict") ) assert doc.model_dump() == test_arrow_doc.model_dump() ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8322716 emmet_core-0.87.2/tests/test_settings.py0000644000175100017510000000270515240717720020021 0ustar00runnerrunnerimport json import os from pathlib import PosixPath from random import random from monty.serialization import dumpfn, loadfn from monty.tempfile import ScratchDir from emmet.core.settings import EmmetSettings def test_default_config_path(tmp_path: PosixPath): """Make sure the default config path works""" rand_symprec = random() with open(tmp_path / "temp_config.json", "w") as f: json.dump({"SYMPREC": rand_symprec}, f) os.environ["EMMET_CONFIG_FILE"] = str(tmp_path.resolve() / "temp_config.json") test_config = EmmetSettings() assert test_config.SYMPREC == rand_symprec def test_allow_extra_fields(tmp_path: PosixPath): """Makes sure emmet config can be subclassed without loading issues""" with open(tmp_path / "temp_config.json", "w") as f: json.dump({"sub_class_prop": True}, f) os.environ["EMMET_CONFIG_FILE"] = str(tmp_path.resolve() / "temp_config.json") EmmetSettings() def test_from_url(): """Makes sure loading from a URL Works""" os.environ["EMMET_CONFIG_FILE"] = ( "https://raw.githubusercontent.com/materialsproject/emmet/master/test_files/test_settings.json" ) test_config = EmmetSettings() assert test_config.ANGLE_TOL == 1.0 def test_seriallization(): test_config = EmmetSettings() with ScratchDir("."): dumpfn(test_config, "test.json") reload_config = loadfn("test.json") assert isinstance(reload_config, EmmetSettings) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8322716 emmet_core-0.87.2/tests/test_similarity.py0000644000175100017510000001165115240717720020347 0ustar00runnerrunner"""Test structure similarity features.""" from pydantic import TypeAdapter import pytest from emmet.core.io.pymatgen import Structure from emmet.core.types.typing import MaterialIdentifierType import numpy as np from emmet.core.similarity import ( CrystalNNSimilarity, M3GNetSimilarity, matgl, vector_difference_matrix, SimilarityScorer, SimilarityDoc, SimilarityEntry, _vector_from_hex_and_norm, _vector_to_hex_and_norm, ) structures = { "aaabc": Structure( 3.5 * np.array([[0.0, 0.5, 0.5], [0.5, 0.0, 0.5], [0.5, 0.5, 0.0]]), ["Cu"], [[0.0, 0.0, 0.0]], ), # fcc Cu "aaadf": Structure( 4 * np.array([[0.0, 0.5, 0.5], [0.5, 0.0, 0.5], [0.5, 0.5, 0.0]]), ["Cu", "Cl"], [[0.0, 0.0, 0.0], [0.25, 0.25, 0.25]], ), # diamond cubic CuCl "mp-13293451": Structure( 2.8 * np.array( [ [0.5, -(3.0 ** (0.5)) / 2.0, 0.0], [0.5, 3.0 ** (0.5) / 2.0, 0.0], [0.0, 0.0, 2], ] ), ["Mo", "S"], [ [2 / 3, 1 / 3, 0.25], [1 / 3, 2 / 3, 0.75], ], ), # weird hcp MoS } def test_crystalnn_featurize(): scorer = CrystalNNSimilarity() feature_vectors, dist_matrix = scorer.get_all_similarity_scores( list(structures.values()) ) assert feature_vectors.shape == ( len(structures), 122, ) # this majik number is defined by the fingerprint ref_matrix = np.array( [ [100.0, 5.27097101, 35.0290187], [5.27097101, 100.0, 8.01686918], [35.0290187, 8.01686918, 100.0], ] ) assert dist_matrix.shape == (len(structures), len(structures)) assert np.all(np.abs(dist_matrix - dist_matrix.T) < 1e-6) # should be symmetric assert np.all(np.abs(dist_matrix - ref_matrix) < 1e-6) num_matches = 2 similarity_docs = scorer.build_similarity_collection_from_structures( structures, num_procs=1, num_top=num_matches, ) # Ensure that similarity docs are correctly constructed, # and order of docs follows order of structures in the # source dict. # Also check that the number of included similarity entries # is the same as specified by `num_matches` validated_idxs = TypeAdapter(dict[str, MaterialIdentifierType]).validate_python( {idx: idx for idx in structures} ) assert all( ( isinstance(similarity_docs[i], SimilarityDoc) and len(similarity_docs[i].sim) == num_matches and similarity_docs[i].material_id == validated_idxs[idx] and all( isinstance(entry, SimilarityEntry) for entry in similarity_docs[i].sim ) ) for i, idx in enumerate(structures.keys()) ) # Check that the feature vectors are the same as before assert np.all( np.abs(fv - similarity_docs[i].feature_vector) < 1e-6 for i, fv in enumerate(feature_vectors) ) # Check that the matched similarity entries have the same # similarity scores as computed in `ref_matrix` id_to_index = {validated_idxs[idfr]: i for i, idfr in enumerate(structures.keys())} assert all( all( 100 - entry.dissimilarity == pytest.approx(ref_matrix[i, id_to_index[entry.task_id]]) for entry in doc.sim ) for i, doc in enumerate(similarity_docs) ) # Ensure roundtrip conversion to/from hex str does not modify vectors assert all( np.allclose(fv, _vector_from_hex_and_norm(*_vector_to_hex_and_norm(fv))) for fv in feature_vectors ) @pytest.mark.skipif(matgl is None, reason="matgl is not installed.") def test_m3gnet_featurize(): scorer = M3GNetSimilarity() fvs, dist_matrix = scorer.get_all_similarity_scores(list(structures.values())) assert fvs.shape == (len(structures), 128) ref_matrix = np.array( [ [100.0, 3.274432636807312, 0.1410324007577568], [3.274432636807312, 100.0, 0.20956578539322868], [0.1410324007577568, 0.20956578539322868, 100.0], ] ) assert np.all(np.abs(dist_matrix - dist_matrix.T) < 1e-6) # should be symmetric assert np.all(np.abs(dist_matrix - ref_matrix) < 1e-6) def test_vector_diff_mat(): vectors = np.random.random((7, 11)) brute_force_diffs = np.zeros(2 * (vectors.shape[0],)) for i in range(vectors.shape[0]): for j in range(vectors.shape[0]): brute_force_diffs[i, j] = np.linalg.norm(vectors[i] - vectors[j]) assert np.all(np.abs(brute_force_diffs - vector_difference_matrix(vectors)) < 1e-12) def test_vendi(): assert all( abs(rank - SimilarityScorer.get_vendi_score(np.eye(rank))) < 1e-12 for rank in range(1, 6) ) assert all( abs(1.0 - SimilarityScorer.get_vendi_score(np.ones((rank, rank)))) < 1e-12 for rank in range(1, 6) ) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8322716 emmet_core-0.87.2/tests/test_structure_group.py0000644000175100017510000000554215240717720021437 0ustar00runnerrunnerimport pytest from monty.serialization import loadfn from emmet.core.io.pymatgen import Composition from emmet.core import ARROW_COMPATIBLE from emmet.core.structure_group import StructureGroupDoc, _get_id_lexi from emmet.core.utils import jsanitize if ARROW_COMPATIBLE: import pyarrow as pa from emmet.core.arrow import arrowize @pytest.fixture(scope="session") def entries_lto(test_dir): """ Recycle the test cases from pymatgen """ entries = loadfn(test_dir / "LiTiO2_batt.json.gz") for itr, ient in enumerate(entries): ient.entry_id = f"mp-{itr}" return entries @pytest.fixture(scope="session") def entries_lfeo(test_dir): """ Recycle the test cases from pymatgen """ entries = loadfn(test_dir / "Li-Fe-O.json.gz") return entries def test_StructureGroupDoc_from_grouped_entries(entries_lto): sgroup_doc = StructureGroupDoc.from_grouped_entries( entries_lto, ignored_specie="Li", ) assert sgroup_doc.group_id == "mp-0_Li" assert sgroup_doc.material_ids == ["mp-0", "mp-1", "mp-2", "mp-3", "mp-4", "mp-5"] assert sgroup_doc.framework_formula == "TiO2" assert sgroup_doc.ignored_specie == "Li" assert sgroup_doc.chemsys == "Li-O-Ti" assert sgroup_doc.has_distinct_compositions is True @pytest.mark.skip(reason="Temp skip for spglib seg fault") def test_StructureGroupDoc_from_ungrouped_entries(entries_lfeo): entry_dict = {ient.entry_id: ient for ient in entries_lfeo} sgroup_docs = StructureGroupDoc.from_ungrouped_structure_entries( entries_lfeo, ignored_specie="Li" ) # Make sure that all the structure in each group has the same framework for sgroup_doc in sgroup_docs: framework_ref = sgroup_doc.framework_formula ignored = sgroup_doc.ignored_specie for entry_id in sgroup_doc.material_ids: dd_ = entry_dict[entry_id].composition.as_dict() if ignored in dd_: dd_.pop(ignored) framework = Composition.from_dict(dd_).reduced_formula assert framework == framework_ref def test_lexi_id(): assert _get_id_lexi("01HMVV88CCQ6JQ2Y1N8F3ZTVWP") == ( "01HMVV88CCQ6JQ2Y1N8F3ZTVWP", 0, ) assert _get_id_lexi("mp-123") == ("mp", 123) assert _get_id_lexi("123") == ("", 123) @pytest.mark.skipif( not ARROW_COMPATIBLE, reason="pyarrow must be installed to run this test." ) def test_arrow(entries_lto): doc = StructureGroupDoc.from_grouped_entries( entries_lto, ignored_specie="Li", ) arrow_struct = pa.scalar( doc.model_dump(context={"format": "arrow"}), type=arrowize(StructureGroupDoc) ) test_arrow_doc = StructureGroupDoc(**arrow_struct.as_py(maps_as_pydicts="strict")) assert jsanitize(doc.model_dump(), allow_bson=True) == jsanitize( test_arrow_doc.model_dump(), allow_bson=True ) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8322716 emmet_core-0.87.2/tests/test_structure_metadata.py0000644000175100017510000000405515240717720022061 0ustar00runnerrunnerimport pytest from emmet.core.io.pymatgen import Element, Lattice, Structure from emmet.core.structure import StructureMetadata from emmet.core.symmetry import CrystalSystem, SymmetryData @pytest.fixture def structure(): test_latt = Lattice.cubic(3.0) test_struc = Structure(lattice=test_latt, species=["Fe"], coords=[[0, 0, 0]]) return test_struc def test_symmetry(structure): symm_doc = SymmetryData.from_structure(structure) assert symm_doc.number == 221 assert symm_doc.point_group == "m-3m" assert symm_doc.symbol == "Pm-3m" assert symm_doc.crystal_system == CrystalSystem.cubic assert symm_doc.model_dump()["crystal_system"] == CrystalSystem.cubic assert str(symm_doc.model_dump()["crystal_system"]) == "Cubic" def test_structure_metadata(structure): meta_doc = StructureMetadata.from_structure(structure) assert meta_doc.nsites == 1 assert meta_doc.elements == [Element.Fe] assert meta_doc.nelements == 1 assert meta_doc.formula_pretty == "Fe" assert meta_doc.formula_anonymous == "A" assert meta_doc.chemsys == "Fe" assert meta_doc.volume == pytest.approx(27.0) assert meta_doc.density == pytest.approx(3.4345483027509993) assert meta_doc.density_atomic == pytest.approx(27.0) def test_structure_metadata_fewer_fields(structure): meta_doc = StructureMetadata.from_structure( structure, fields=["nsites", "nelements", "volume"] ) assert meta_doc.nsites == 1 assert meta_doc.nelements == 1 assert meta_doc.volume == pytest.approx(27.0) def test_composition(structure): meta_doc = StructureMetadata.from_structure(structure) comp_meta_doc = StructureMetadata.from_composition(structure.composition) assert meta_doc.elements == comp_meta_doc.elements assert meta_doc.nelements == comp_meta_doc.nelements assert meta_doc.formula_pretty == comp_meta_doc.formula_pretty assert meta_doc.formula_anonymous == comp_meta_doc.formula_anonymous assert meta_doc.chemsys == comp_meta_doc.chemsys def test_schema(): StructureMetadata.schema() ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8322716 emmet_core-0.87.2/tests/test_symmetry.py0000644000175100017510000000226715240717720020055 0ustar00runnerrunner"""Test symmetry-related utilities.""" from emmet.core.io.pymatgen import SYMM_DATA import pytest from emmet.core.symmetry import ( _get_space_group_symbol_to_number_mapping, get_crystal_system_from_international_number, CrystalSystem, ) def test_spacegroup_symbol_number_mapping(): sgsn = _get_space_group_symbol_to_number_mapping() assert all(isinstance(k, str) and isinstance(v, int) for k, v in sgsn.items()) assert all( sgsn.get(k) == v and get_crystal_system_from_international_number(v) == CrystalSystem.cubic for k, v in { "Fm-3m": 225, "Im-3m": 229, "Ia-3d": 230, }.items() ) assert all(k in sgsn for k in SYMM_DATA["abbreviated_spacegroup_symbols"]) assert sorted(set(sgsn.values())) == list(range(1, 231)) def test_get_crystal_system(): for sgn in range(232): if sgn < 1 or sgn > 230: with pytest.raises(ValueError, match="Invalid space group number"): get_crystal_system_from_international_number(sgn) else: assert isinstance( get_crystal_system_from_international_number(sgn), CrystalSystem ) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8322716 emmet_core-0.87.2/tests/test_task.py0000644000175100017510000002501715240717720017124 0ustar00runnerrunnerimport pytest from tests.conftest import get_test_object from emmet.core import ARROW_COMPATIBLE from emmet.core.tasks import TaskDoc from emmet.core.testing_utils import DataArchive, assert_schemas_equal from emmet.core.vasp.task_valid import TaskState if ARROW_COMPATIBLE: import pyarrow as pa from emmet.core.arrow import arrowize @pytest.mark.parametrize( "object_name", [ pytest.param("SiOptimizeDouble", id="SiOptimizeDouble"), pytest.param("SiStatic", id="SiStatic"), pytest.param("SiNonSCFUniform", id="SiNonSCFUniform"), ], ) def test_analysis_summary(test_dir, object_name): from monty.json import MontyDecoder, jsanitize from emmet.core.tasks import AnalysisDoc, _get_state from emmet.core.vasp.calculation import Calculation test_object = get_test_object(object_name) with DataArchive.extract( test_dir / "vasp" / f"{test_object.folder}.json.gz" ) as dir_name: calcs_reversed = [] for task_name, files in test_object.task_files.items(): doc, _ = Calculation.from_vasp_files(dir_name, task_name, **files) calcs_reversed.append(doc) # The 2 tasks of double-relaxation have been reversed in # "/atomate2/tests/vasp/schemas/conftest.py" for "SiOptimizeDouble" # task_files are in the order of {"relax2","relax1"} test_doc = AnalysisDoc.from_vasp_calc_docs(calcs_reversed) assert _get_state(calcs_reversed, test_doc) == TaskState.SUCCESS valid_doc = test_object.task_doc["analysis"] assert_schemas_equal(test_doc, valid_doc) # test document can be jsanitized d = jsanitize(test_doc, strict=True, enum_values=True, allow_bson=True) # and decoded MontyDecoder().process_decoded(d) @pytest.mark.parametrize( ("object_name", "task_name"), [ pytest.param("SiOptimizeDouble", "relax1", id="SiOptimizeDouble"), pytest.param("SiStatic", "standard", id="SiStatic"), pytest.param("SiNonSCFUniform", "standard", id="SiNonSCFUniform"), ], ) def test_input_summary(test_dir, object_name, task_name): from monty.json import MontyDecoder, jsanitize from emmet.core.vasp.calculation import Calculation test_object = get_test_object(object_name) files = test_object.task_files[task_name] with DataArchive.extract( test_dir / "vasp" / f"{test_object.folder}.json.gz" ) as dir_name: calc_doc, _ = Calculation.from_vasp_files(dir_name, task_name, **files) test_doc = calc_doc.input valid_doc = test_object.task_doc["input"] assert_schemas_equal(test_doc, valid_doc) # test document can be jsanitized d = jsanitize(test_doc, strict=True, enum_values=True, allow_bson=True) # and decoded MontyDecoder().process_decoded(d) @pytest.mark.parametrize( ("object_name", "task_name"), [ pytest.param("SiOptimizeDouble", "relax2", id="SiOptimizeDouble"), pytest.param("SiStatic", "standard", id="SiStatic"), pytest.param("SiNonSCFUniform", "standard", id="SiNonSCFUniform"), ], ) def test_output_summary(test_dir, object_name, task_name): from monty.json import MontyDecoder, jsanitize from emmet.core.tasks import OutputDoc from emmet.core.vasp.calculation import Calculation test_object = get_test_object(object_name) files = test_object.task_files[task_name] with DataArchive.extract( test_dir / "vasp" / f"{test_object.folder}.json.gz" ) as dir_name: calc_doc, _ = Calculation.from_vasp_files(dir_name, task_name, **files) test_doc = OutputDoc.from_vasp_calc_doc(calc_doc) valid_doc = test_object.task_doc["output"] assert_schemas_equal(test_doc, valid_doc) # test document can be jsanitized d = jsanitize(test_doc, strict=True, enum_values=True, allow_bson=True) # and decoded MontyDecoder().process_decoded(d) @pytest.mark.parametrize( "object_name", [ pytest.param("SiOptimizeDouble", id="SiOptimizeDouble"), pytest.param("SiStatic", id="SiStatic"), pytest.param("SiNonSCFUniform", id="SiNonSCFUniform"), ], ) def test_task_doc(test_dir, object_name, tmpdir): import os import shutil from monty.json import jsanitize from monty.serialization import dumpfn from emmet.core.io.pymatgen import ( ComputedEntry, DeformStructureTransformation, TransformedStructure, ) test_object = get_test_object(object_name) with DataArchive.extract( test_dir / "vasp" / f"{test_object.folder}.json.gz" ) as dir_name: test_doc = TaskDoc.from_directory(dir_name) assert_schemas_equal(test_doc, test_object.task_doc) # test document can be jsanitized jsanitize(test_doc, strict=True, enum_values=True, allow_bson=True) # This is currently an issue as older versions of dumped custodian VaspJob objects are in the # test files. This needs to be updated to properly test decoding. # MontyDecoder().process_decoded(dct) # Test that additional_fields works test_doc = TaskDoc.from_directory(dir_name, additional_fields={"foo": "bar"}) assert test_doc.model_dump()["foo"] == "bar" assert len(test_doc.calcs_reversed) == len(test_object.task_files) assert test_doc.state == TaskState.SUCCESS # ensure that number of electronic steps are correctly populated for cr in test_doc.calcs_reversed: assert len(cr.output.ionic_steps) == len(cr.output.num_electronic_steps) assert cr.output.num_electronic_steps == [ len(ionic_step.electronic_steps) for ionic_step in cr.output.ionic_steps ] # ensure that run stats are not all identically zero (i.e., they are parsed correctly) for run_stats in test_doc.run_stats.values(): assert any(abs(time) > 1e-6 for time in run_stats.model_dump().values()) # Check that entry is populated when calcs_reversed is not None if test_doc.calcs_reversed: assert isinstance( test_doc.entry, ComputedEntry ), f"Unexpected entry {test_doc.entry} for {object_name}" # Test that transformations field works, using hydrostatic compression as example ts = TransformedStructure( test_doc.output.structure, transformations=[ DeformStructureTransformation( deformation=[ [0.9 if i == j else 0.0 for j in range(3)] for i in range(3) ] ) ], ) ts_json = jsanitize(ts.as_dict()) dumpfn(ts, f"{tmpdir}/transformations.json") for f in os.listdir(dir_name): if os.path.isfile(os.path.join(dir_name, f)): shutil.copy(os.path.join(dir_name, f), tmpdir) test_doc = TaskDoc.from_directory(tmpdir) # if other_parameters == {}, this is popped from the TaskDoc.transformations field # seems like @version is added by monty serialization # jsanitize needed because pymatgen.core.Structure.pbc is a tuple assert all( test_doc.transformations[k] == v for k, v in ts_json.items() if k not in ( "other_parameters", "@version", "last_modified", ) ) assert isinstance(test_doc.transformations, dict) # now test case when transformations are serialized, relevant for atomate2 test_doc = TaskDoc( **{ "transformations": ts, **{ k: v for k, v in test_doc.model_dump().items() if k != "transformations" }, } ) assert all( test_doc.transformations[k] == v for k, v in ts.as_dict().items() if k != "last_modified" ) def test_lda_and_pseudo_format(test_dir, tmpdir): from emmet.core.tasks import TaskDoc with DataArchive.extract( test_dir / "vasp" / "lda_calc.json.gz", ) as dir_name: task = TaskDoc.from_directory(dir_name) assert task.run_type.name == "LDA" assert all(task.input.incar.get(k) is None for k in ("GGA", "METAGGA")) expected_pseudo = { "pot_type": "PAW", "functional": "LDA", "symbols": ["Si"], } assert all( getattr(task.input.pseudo_potentials, k) == v for k, v in expected_pseudo.items() ) def test_orig_inp_parsing(tmp_dir): """Test parsing of VASP input with variable suffix, like `.orig`.""" from pathlib import Path from emmet.core.io.pymatgen import Incar, Kpoints, Poscar, Structure from emmet.core.tasks import _parse_orig_inputs # demo simple cubic Copper structure structure = Structure( [[3 if i != j else 0.0 for j in range(3)] for i in range(3)], ["Cu"], [[0.0, 0.0, 0.0]], ) incar = Incar.from_dict( { "ALGO": "Normal", "ENCUT": 600, } ) kpoints = Kpoints() for suffix in (".orig", ".image", ".mangoes"): structure.to(f"POSCAR{suffix}") incar.write_file(f"INCAR{suffix}") kpoints.write_file(f"KPOINTS{suffix}") vi = _parse_orig_inputs(Path("."), suffix=suffix) assert all( getattr(vi, k) is not None for k in ("incar", "kpoints", "structure") ) assert isinstance(vi.poscar, Poscar) assert isinstance(vi.poscar.structure, Structure) assert len(vi.model_dump(exclude_defaults=True, exclude_none=True)) == 3 @pytest.mark.parametrize( "object_name", [ pytest.param("SiOptimizeDouble", id="SiOptimizeDouble"), pytest.param("SiStatic", id="SiStatic"), pytest.param("SiNonSCFUniform", id="SiNonSCFUniform"), ], ) @pytest.mark.skipif( not ARROW_COMPATIBLE, reason="pyarrow must be installed to run this test." ) def test_arrow(test_dir, object_name, tmpdir): test_object = get_test_object(object_name) with DataArchive.extract( test_dir / "vasp" / f"{test_object.folder}.json.gz" ) as dir_name: doc = TaskDoc.from_directory(dir_name) arrow_struct = pa.scalar( doc.model_dump(context={"format": "arrow"}), type=arrowize(TaskDoc) ) # Avoiding comparisons of round tripped arrow doc vs. original doc # due to a few field types that get changed during json serialization # e.g., orig_inputs/input.kpoints.tet_weight is a float, but if the # val is 0 json dumps to int -> 0, 0 != 0.0 when comparing doc models # TBD on the value of getting this strict comparison correct assert TaskDoc(**arrow_struct.as_py(maps_as_pydicts="strict")) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8322716 emmet_core-0.87.2/tests/test_thermo.py0000644000175100017510000001300515240717720017452 0ustar00runnerrunnerimport pytest from monty.serialization import loadfn from pydantic import TypeAdapter from emmet.core import ARROW_COMPATIBLE from emmet.core.thermo import ThermoDoc if ARROW_COMPATIBLE: import pyarrow as pa from emmet.core.arrow import arrowize from emmet.core.types.pymatgen_types.computed_entries_adapter import ( ComputedStructureEntryType, ) @pytest.fixture(scope="session") def Fe3O4_structure(test_dir): structure = loadfn(test_dir / "thermo/Fe3O4_structure.json.gz") return structure @pytest.fixture(scope="session") def Fe2O3a_structure(test_dir): structure = loadfn(test_dir / "thermo/Fe2O3a_structure.json.gz") return structure @pytest.fixture(scope="session") def Fe2O3b_structure(test_dir): structure = loadfn(test_dir / "thermo/Fe2O3b_structure.json.gz") return structure @pytest.fixture(scope="session") def Fe_structure(test_dir): structure = loadfn(test_dir / "thermo/Fe_structure.json.gz") return structure @pytest.fixture(scope="session") def O_structure(test_dir): structure = loadfn(test_dir / "thermo/O_structure.json.gz") return structure @pytest.fixture def entries( Fe3O4_structure, Fe2O3a_structure, Fe2O3b_structure, Fe_structure, O_structure ): return TypeAdapter(list[ComputedStructureEntryType]).validate_python( [ { "@module": "pymatgen.entries.computed_entries", "@class": "ComputedStructureEntry", "correction": 0.0, "structure": Fe3O4_structure.as_dict(), "entry_id": "mp-1-UNKNOWN", "energy": -382.146593528, "composition": {"Fe": 24.0, "O": 32.0}, "name": "Fe3O4", "parameters": {}, "data": { "material_id": "mp-1", "run_type": "Unknown", "task_id": "mp-10", }, "attribute": None, "@version": "2020.4.29", }, { "@module": "pymatgen.entries.computed_entries", "@class": "ComputedStructureEntry", "correction": 0.0, "structure": Fe2O3a_structure.as_dict(), "entry_id": "mp-2-UNKNOWN", "energy": -270.38765404, "composition": {"Fe": 16.0, "O": 24.0}, "name": "Fe2O3", "parameters": {}, "data": { "material_id": "mp-2", "run_type": "Unknown", "task_id": "mp-20", }, "attribute": None, "@version": "2020.4.29", }, { "@module": "pymatgen.entries.computed_entries", "@class": "ComputedStructureEntry", "correction": 0.0, "structure": O_structure.as_dict(), "entry_id": "mp-3-UNKNOWN", "energy": -92.274692568, "composition": {"O": 24.0}, "name": "O", "parameters": {}, "data": { "material_id": "mp-3", "run_type": "Unknown", "task_id": "mp-30", }, "attribute": None, "@version": "2020.4.29", }, { "@module": "pymatgen.entries.computed_entries", "@class": "ComputedStructureEntry", "correction": 0.0, "structure": Fe_structure.as_dict(), "entry_id": "mp-4-UNKNOWN", "energy": -13.00419661, "composition": {"Fe": 2.0}, "name": "Fe", "parameters": {}, "data": { "material_id": "mp-4", "run_type": "Unknown", "task_id": "mp-40", }, "attribute": None, "@version": "2020.4.29", }, { "@module": "pymatgen.entries.computed_entries", "@class": "ComputedStructureEntry", "correction": 0.0, "structure": Fe2O3b_structure.as_dict(), "entry_id": "mp-5-UNKNOWN", "energy": -1080.82678592, "composition": {"Fe": 64.0, "O": 96.0}, "name": "Fe2O3", "attribute": None, "parameters": {}, "data": { "material_id": "mp-5", "run_type": "Unknown", "task_id": "mp-50", }, "@version": "2020.4.29", }, ] ) def test_from_entries(entries): docs = ThermoDoc.from_entries(entries, thermo_type="UNKNOWN", deprecated=False) assert len(docs) == len(entries) assert all([d.energy_type == "Unknown" for d in docs]) unstable_doc = next(d for d in docs if d.material_id == "mp-5") assert unstable_doc.is_stable is False assert all([d.is_stable for d in docs if d != unstable_doc]) @pytest.mark.skipif( not ARROW_COMPATIBLE, reason="pyarrow must be installed to run this test." ) def test_arrow(entries): doc = ThermoDoc.from_entries(entries, thermo_type="UNKNOWN", deprecated=False)[0] arrow_struct = pa.scalar( doc.model_dump(context={"format": "arrow"}), type=arrowize(ThermoDoc) ) test_arrow_doc = ThermoDoc(**arrow_struct.as_py(maps_as_pydicts="strict")) assert doc.model_dump() == test_arrow_doc.model_dump() ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8322716 emmet_core-0.87.2/tests/test_trajectory.py0000644000175100017510000001421315240717720020344 0ustar00runnerrunnerimport numpy as np from pytest import approx, fixture from monty.serialization import loadfn from emmet.core.io.pymatgen import Element, Structure, Molecule from pymatgen.core.trajectory import Trajectory as PmgTraj from emmet.core.tasks import TaskDoc from emmet.core.testing_utils import DataArchive from emmet.core.trajectory import Trajectory, RelaxTrajectory @fixture(scope="module") def si_task(test_dir): with DataArchive.extract( test_dir / "vasp" / "Si_old_double_relax.json.gz" ) as dir_name: return TaskDoc.from_directory(dir_name) @fixture(scope="module") def si_traj(si_task): return si_task.trajectories def test_reorder_sites(): # Test that site ordering works correctly lattice = [[5.0, 0, 0], [4, 0, 0], [4.5, 0, 0]] coords = [ [0, 0, 0], [0.0, 0.25, 0.0], [0.25, 0.0, 0.25], [0.0, 0.0, 0.25], [0.25, 0.0, 0.0], [0.25, 0.25, 0.25], [0.5, 0.5, 0.5], [0.75, 0.75, 0.75], ] elements = ["Si", "Si", "Ti", "N", "Ga", "As", "N", "Cu"] ref_z = [Element(ele).Z for ele in elements] # try a number of times to ensure ordering is always OK for _ in range(2 * len(elements)): new_z = [z for z in ref_z] np.random.shuffle(new_z) structure = Structure(lattice, [Element.from_Z(z) for z in new_z], coords) reordered_struct, reordered_idx, _ = Trajectory.reorder_sites(structure, ref_z) assert all(site.specie.Z == ref_z[i] for i, site in enumerate(reordered_struct)) assert all( np.abs(np.linalg.norm(reordered_struct[i].coords - structure[old_i].coords)) < 1e-6 for i, old_i in enumerate(reordered_idx) ) # ensure that these also work for Molecules mol = Molecule.from_sites(structure) reordered_mol, reordered_idx, _ = Trajectory.reorder_sites(structure, ref_z) assert all(site.specie.Z == ref_z[i] for i, site in enumerate(reordered_mol)) assert all( np.abs(np.linalg.norm(reordered_mol[i].coords - mol[old_i].coords)) < 1e-6 for i, old_i in enumerate(reordered_idx) ) def test_task_doc(si_task, si_traj): traj = si_traj[0] assert traj.num_ionic_steps == sum( len(cr.output.ionic_steps) for cr in si_task.calcs_reversed ) istep = -1 for cr in si_task.calcs_reversed: for ionic_step in cr.output.ionic_steps: assert traj.energy[istep] == approx(ionic_step.e_0_energy) istep -= 1 def test_parquet(si_traj, tmp_dir): parqet_file = "test.parquet" traj = si_traj[0] traj.to(parqet_file, compression="GZIP") new_traj = RelaxTrajectory.from_parquet(parqet_file) assert hash(new_traj) == hash(traj) def test_pmg(si_traj): traj = si_traj[0] pmg_traj = traj.to(fmt="PMG") assert isinstance(pmg_traj, PmgTraj) assert len(pmg_traj) == traj.num_ionic_steps assert len(pmg_traj.frame_properties) == traj.num_ionic_steps # some float-rounding that happens here on round trip roundtrip = Trajectory.from_pmg(pmg_traj) for k in Trajectory.model_fields: if k == "ionic_step_properties": continue if ( k in ( "num_ionic_steps", "electronic_steps", "task_type", "run_type", ) or getattr(traj, k, None) is None ): # Can't easily test for equality of electronic steps without bespoke code continue assert all( np.all(np.abs(np.array(new_val) - np.array(getattr(traj, k)[i])) < 1e-6) for i, new_val in enumerate(getattr(roundtrip, k)) ) # Check that requesting a subset of a single index works traj_subset_from_int = traj.to(fmt="PMG", indices=1) traj_subset_from_list = traj.to(fmt="PMG", indices=[1]) assert all(len(x) == 1 for x in (traj_subset_from_int, traj_subset_from_list)) other = traj_subset_from_list.as_dict() assert all( np.all(v == other.get(k)) for k, v in traj_subset_from_int.as_dict().items() ) # Check that the output frames are always in order subset_props = ( "energy", "stress", ) traj_subset = traj.to(fmt="PMG", indices=[1, 2, 3], frame_props=subset_props) unsrt_traj_subset = traj.to( fmt="PMG", indices=[3, 1, 2], frame_props=subset_props ).as_dict() assert all( np.all(v == unsrt_traj_subset.get(k)) for k, v in traj_subset.as_dict().items() ) # check that only requested frame_properties are returned assert all( frame.get(k) is not None if k in subset_props else frame.get(k) is None for k in traj.ionic_step_properties for frame in traj_subset.frame_properties ) # Test returning only structures just_structs = traj.to(fmt="PMG", frame_props=tuple()) assert len(just_structs) == traj.num_ionic_steps assert all( frame.get(k) is None for k in traj.ionic_step_properties for frame in just_structs.frame_properties ) def test_mixed_calc_type(test_dir): # Test that Trajectory correctly creates new Trajectories for every # sequential calculation of different CalcType three_cr_task_dict = loadfn(test_dir / "mp-1120260_cr.json.gz") trajs = TaskDoc(**three_cr_task_dict).trajectories assert len(trajs) == 2 # GGA static followed by two SCAN relaxes assert trajs[0].task_type.value == "Static" assert trajs[0].run_type.value == "GGA" assert trajs[1].task_type.value == "Structure Optimization" assert trajs[1].run_type.value == "SCAN" # now exchange order of calcs_reversed to get three trajectories three_cr_task_dict["calcs_reversed"] = [ three_cr_task_dict["calcs_reversed"][idx] for idx in (0, 2, 1) ] trajs = TaskDoc(**three_cr_task_dict).trajectories assert len(trajs) == 3 # SCAN relax -> GGA static -> SCAN relax assert trajs[1].task_type.value == "Static" assert trajs[1].run_type.value == "GGA" for idx in ( 0, 2, ): assert trajs[idx].task_type.value == "Structure Optimization" assert trajs[idx].run_type.value == "SCAN" ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8332715 emmet_core-0.87.2/tests/test_utils.py0000644000175100017510000001026715240717720017323 0ustar00runnerrunnerimport datetime import json from pathlib import Path import numpy as np import pytest from bson.objectid import ObjectId from monty.json import MSONable from monty.serialization import loadfn from emmet.core.tasks import TaskDoc from emmet.core.utils import ( convert_datetime, dynamic_import, get_flat_models_from_model, get_hash_blocked, jsanitize, utcnow, ) def test_dt(): ref_dt = utcnow() for test_dt in ( jsanitize(ref_dt), {"$date": jsanitize(ref_dt)}, ): assert convert_datetime(test_dt) == ref_dt def test_jsanitize(): """ Tests emmet Jsanitize which converts MSONable classes into dicts """ # clean_json should have no effect on None types. d = {"hello": 1, "world": None} clean = jsanitize(d) assert clean["world"] is None assert json.loads(json.dumps(d)) == json.loads(json.dumps(clean)) d = {"hello": GoodMSONClass(1, 2, 3)} with pytest.raises(TypeError): json.dumps(d) clean = jsanitize(d) assert isinstance(clean["hello"], dict) clean_strict = jsanitize(d, strict=True) assert clean_strict["hello"]["a"] == 1 assert clean_strict["hello"]["b"] == 2 d = {"dt": datetime.datetime.now()} clean = jsanitize(d) assert isinstance(clean["dt"], str) clean = jsanitize(d, allow_bson=True) assert isinstance(clean["dt"], datetime.datetime) d = { "a": ["b", np.array([1, 2, 3])], "b": ObjectId.from_datetime(datetime.datetime.now()), } clean = jsanitize(d) assert clean["a"] == ["b", [1, 2, 3]] assert isinstance(clean["b"], str) rnd_bin = bytes(np.random.rand(10)) d = {"a": bytes(rnd_bin)} clean = jsanitize(d, allow_bson=True) assert clean["a"] == bytes(rnd_bin) assert isinstance(clean["a"], bytes) class GoodMSONClass(MSONable): def __init__(self, a, b, c, d=1, **kwargs): self.a = a self.b = b self._c = c self._d = d self.kwargs = kwargs def __eq__(self, other): return ( self.a == other.a and self.b == other.b and self._c == other._c and self._d == other._d and self.kwargs == other.kwargs ) def test_blocked_hash(tmp_dir): import blake3 file_text = ( "Lorem ipsum dolor sit amet, consectetur adipiscing elit, " "sed do eiusmod tempor incididunt ut labore et dolore magna aliqua." ).encode() with open("test_md5.gz", "wb") as f: f.write(file_text) assert get_hash_blocked("test_md5.gz") == blake3.blake3(file_text).hexdigest() def test_model_flatten(): sub_models = get_flat_models_from_model(TaskDoc) assert {model.__name__ for model in sub_models} == { "AnalysisDoc", "BaderAnalysis", "Calculation", "CalculationInput", "CalculationOutput", "CustodianDoc", "DielectricProperties", "ElectronPhononDisplacedStructures", "ElectronicStep", "FrequencyDependentDielectric", "IonicStep", "OutputDoc", "PotcarSpec", "RunStatistics", "SymmetryData", "TaskDoc", } def test_import(): assert dynamic_import("emmet.core.tasks.TaskDoc") == TaskDoc def test_data_archive(tmp_dir): from monty.io import zopen from emmet.core.testing_utils import DataArchive def listify_file(file_name): with zopen(file_name, "rt") as f: return list(f.read()) test_str = "the quick brown fox jumped over the lazy dog" test_files = {"test_file", "test_gzipped.gz"} for p in test_files: with zopen(p, "wt") as f: f.write(test_str) archive = DataArchive.from_directory(".") archive_path = Path("test.json.gz") archive.to_json(archive_path) assert archive_path.is_file() loaded = loadfn(archive_path) assert set(loaded) == test_files assert loaded["test_file"]["0"] == test_str.encode().hex() with DataArchive.extract(archive_path) as temp_dir: assert {p.name for p in temp_dir.glob("*")} == test_files for fname in test_files: assert DataArchive.extract_obj(archive_path, fname, listify_file) == list( test_str ) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8332715 emmet_core-0.87.2/tests/test_xas.py0000644000175100017510000001372715240717720016762 0ustar00runnerrunner"""Test basic features of XASDoc.""" import pytest from monty.serialization import loadfn from emmet.core.io.pymatgen import XAS, Element from emmet.core import ARROW_COMPATIBLE from emmet.core.types.enums import XasEdge, XasType from emmet.core.types.typing import validate_compound_identifier from emmet.core.utils import jsanitize from emmet.core.xas import XASDoc if ARROW_COMPATIBLE: import pyarrow as pa from emmet.core.arrow import arrowize @pytest.fixture(scope="module") def xas_dict(test_dir): return loadfn(test_dir / "xasdoc_nonpos_mp_626735.json.gz", cls=None) def test_xas_doc(xas_dict): # First show that there are non-positive intensities non_pos_idx = [idx for idx, v in enumerate(xas_dict["spectrum"]["y"]) if v <= 0.0] assert len(non_pos_idx) > 0 # Now show that XASDoc removes non-positive intensities and correctly serializes xas = XASDoc(**xas_dict) assert xas.spectrum_id == validate_compound_identifier( f"{xas.task_id}-" + "-".join( getattr(xas, k).value for k in ("spectrum_type", "absorbing_element", "edge") ), suffixes=(XasType, Element, XasEdge), separator="-", use_prefix=False, ) assert isinstance(xas.spectrum, XAS) assert len(xas.spectrum.y[xas.spectrum.y <= 0.0]) == 0 assert all( xas.spectrum.y[idx] > 0.0 and xas.spectrum.y[idx] == pytest.approx(0.0) for idx in non_pos_idx ) assert isinstance(xas.absorbing_element, Element) xas_no_task_id = XASDoc(**{k: v for k, v in xas_dict.items() if k != "task_id"}) with pytest.raises( ValueError, match="Cannot determine `spectrum_id` without a `task_id`" ): xas_no_task_id.spectrum_id @pytest.mark.skipif( not ARROW_COMPATIBLE, reason="pyarrow must be installed to run this test." ) def test_arrow(xas_dict): doc = XASDoc(**xas_dict) arrow_struct = pa.scalar( doc.model_dump(context={"format": "arrow"}), type=arrowize(XASDoc) ) test_arrow_doc = XASDoc(**arrow_struct.as_py(maps_as_pydicts="strict")) assert jsanitize(doc.model_dump(), allow_bson=True) == jsanitize( test_arrow_doc.model_dump(), allow_bson=True ) # --------------------------------------------------------------------------- # format_spectrum_id — XAS-spectrum-id-specific renderer # # Spectrum ids are composite identifiers: task id + (XasType, Element, XasEdge) # joined with "-". They follow the same prefix-dropping rule as task ids: # - Legacy: `mp-779827-XANES-O-K` # - Alpha: `aaabsjpj-XANES-O-K` (no `mp-` prefix on the leading id portion) # --------------------------------------------------------------------------- @pytest.mark.parametrize( "spectrum_id", ["mp-779827-XANES-O-K", "aaabsjpj-XANES-O-K"], ids=["legacy", "alpha"], ) def test_format_spectrum_id_legacy_view_always_returns_mp_prefix(spectrum_id): from emmet.core.xas import format_spectrum_id # Regardless of incoming shape, the legacy view is `mp--`. assert format_spectrum_id(spectrum_id, legacy=True) == "mp-779827-XANES-O-K" @pytest.mark.parametrize( "spectrum_id", ["mp-779827-XANES-O-K", "aaabsjpj-XANES-O-K"], ids=["legacy", "alpha"], ) def test_format_spectrum_id_alpha_view_drops_prefix(spectrum_id): from emmet.core.xas import format_spectrum_id # Regardless of incoming shape, the alpha view is bare padded alpha + # suffix, with no `mp-` prefix on the leading id portion. assert format_spectrum_id(spectrum_id, legacy=False) == "aaabsjpj-XANES-O-K" def test_format_spectrum_id_round_trip_alpha_to_legacy_to_alpha(): from emmet.core.xas import format_spectrum_id original = "aaabsjpj-XANES-O-K" legacy = format_spectrum_id(original, legacy=True) assert format_spectrum_id(legacy, legacy=False) == original def test_format_spectrum_id_round_trip_legacy_to_alpha_to_legacy(): from emmet.core.xas import format_spectrum_id original = "mp-779827-XANES-O-K" alpha = format_spectrum_id(original, legacy=False) assert format_spectrum_id(alpha, legacy=True) == original def test_format_spectrum_id_preserves_suffix_components(): from emmet.core.xas import format_spectrum_id # EXAFS spectrum type, L2 edge, different absorbing element. sid = "mp-149-EXAFS-Fe-L2" assert format_spectrum_id(sid, legacy=True) == sid alpha = format_spectrum_id(sid, legacy=False) assert alpha.endswith("-EXAFS-Fe-L2") assert not alpha.startswith("mp-") @pytest.mark.parametrize("value", ["", None]) def test_format_spectrum_id_empty_input_passes_through(value): from emmet.core.xas import format_spectrum_id assert format_spectrum_id(value, legacy=True) is value assert format_spectrum_id(value, legacy=False) is value def test_format_spectrum_id_unparseable_input_passes_through(): from emmet.core.xas import format_spectrum_id # Defensive: display helpers never raise. validate_xas_spectrum_id raises # ValueError on malformed input; we swallow and return the input as-is. assert format_spectrum_id("BAD-ID!!", legacy=True) == "BAD-ID!!" assert format_spectrum_id("not-a-spectrum", legacy=False) == "not-a-spectrum" def test_format_spectrum_id_accepts_mpid_typed_input(): """Regression: MPID instances passed through must not trip the empty- string guard. The original guard ``spectrum_id is None or spectrum_id == ""`` triggered ``MPID.__eq__("")`` which raises ValueError because ``MPID("")`` is invalid. Passing an MPID-typed value used to crash the helper. Note: AlphaID does not accept the uppercase-letter portions of a spectrum_id (e.g. "XANES", "O", "K"), so composite spectrum ids that flow through pydantic-typed fields arrive as ``MPID`` instances. """ from emmet.core.mpid import MPID from emmet.core.xas import format_spectrum_id sid = MPID("mp-779827-XANES-O-K") assert format_spectrum_id(sid, legacy=True) == "mp-779827-XANES-O-K" assert format_spectrum_id(sid, legacy=False) == "aaabsjpj-XANES-O-K" ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8332715 emmet_core-0.87.2/tests/test_xrd.py0000644000175100017510000000371315240717720016756 0ustar00runnerrunnerimport pytest from emmet.core.io.pymatgen import WAVELENGTHS, Element, Lattice, Structure from emmet.core import ARROW_COMPATIBLE from emmet.core.utils import jsanitize from emmet.core.xrd import Edge, XRDDoc if ARROW_COMPATIBLE: import pyarrow as pa from emmet.core.arrow import arrowize @pytest.fixture def structure(): test_latt = Lattice.cubic(3.0) test_struc = Structure(lattice=test_latt, species=["Fe"], coords=[[0, 0, 0]]) return test_struc @pytest.mark.parametrize("target", list(WAVELENGTHS.keys())) def test_target_detection(structure, target): doc = XRDDoc.from_structure( structure=structure, spectrum_id="test-1", material_id="test-1", wavelength=WAVELENGTHS[target], ) target_element = Element(target[:2]) target_edge = Edge(target[2:]) assert doc.target == target_element assert doc.edge == target_edge @pytest.mark.parametrize("target", list(WAVELENGTHS.keys())) def test_from_target(structure, target): target_element = Element(target[:2]) target_edge = Edge(target[2:]) doc = XRDDoc.from_target( structure=structure, material_id="test-1", target=target_element, edge=target_edge, ) assert doc.target == target_element assert doc.edge == target_edge def test_schema(): XRDDoc.model_json_schema() @pytest.mark.skipif( not ARROW_COMPATIBLE, reason="pyarrow must be installed to run this test." ) def test_arrow(structure): doc = XRDDoc.from_structure( structure=structure, spectrum_id="test-1", material_id="test-1", wavelength=WAVELENGTHS[next(iter(WAVELENGTHS))], ) arrow_struct = pa.scalar( doc.model_dump(context={"format": "arrow"}), type=arrowize(XRDDoc) ) test_arrow_doc = XRDDoc(**arrow_struct.as_py(maps_as_pydicts="strict")) assert jsanitize(doc.model_dump(), allow_bson=True) == jsanitize( test_arrow_doc.model_dump(), allow_bson=True ) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011028.9994104 emmet_core-0.87.2/tests/types/0000755000175100017510000000000015240717725015715 5ustar00runnerrunner././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8332715 emmet_core-0.87.2/tests/types/test_enums.py0000644000175100017510000000112615240717720020450 0ustar00runnerrunnerfrom pathlib import Path from monty.serialization import dumpfn from emmet.core.types.enums import ( DocEnum, ValueEnum, ) def test_value_enum(tmp_path): class TempEnum(ValueEnum): A = "A" B = "B" assert str(TempEnum.A) == "A" assert str(TempEnum.B) == "B" dumpfn(TempEnum, tmp_path / "temp.json") assert Path(tmp_path, "temp.json").is_file() def test_doc_enum(): class TestEnum(DocEnum): A = "A", "Describes A" B = "B", "Might describe B" assert str(TestEnum.A) == "A" assert TestEnum.B.__doc__ == "Might describe B" ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8339596 emmet_core-0.87.2/tests/types/test_typing.py0000644000175100017510000002201215240717720020630 0ustar00runnerrunnerimport pytest from pydantic import BaseModel from emmet.core.mpid import MPID, AlphaID from emmet.core.types.typing import ( ID_PADLEN, ID_PREFIX, MaterialIdentifierType, format_compound_identifier, format_identifier, format_task_id, ) def test_identifier_type(): class TestClass(BaseModel): ID: MaterialIdentifierType tc = TestClass(ID="dogs") # ensure that extant MPIDs are deserialized to MPID, # and return AlphaID strings on model dump assert isinstance(tc.ID, MPID) assert tc.model_dump()["ID"].split("-")[-1].isalpha() assert not tc.model_dump()["ID"].isnumeric() tc = TestClass(ID=AlphaID._cut_point + 1) # ensure that new MPIDs are deserialized to MPID, # and return AlphaID strings on model dump assert isinstance(tc.ID, AlphaID) assert tc.model_dump()["ID"].split("-")[-1].isalpha() assert not tc.model_dump()["ID"].isnumeric() # --------------------------------------------------------------------------- # format_identifier — display-format helper for plain MP identifiers # --------------------------------------------------------------------------- def test_format_identifier_legacy_form_below_cutoff(): assert format_identifier("mp-149", legacy=True) == "mp-149" def test_format_identifier_alpha_form_below_cutoff_is_padded(): # AlphaID display form is padded to ID_PADLEN (8 by default). assert format_identifier("mp-149", legacy=False) == "mp-aaaaaaft" def test_format_identifier_round_trip_legacy_to_alpha_to_legacy(): alpha = format_identifier("mp-149", legacy=False) assert format_identifier(alpha, legacy=True) == "mp-149" def test_format_identifier_round_trip_alpha_to_legacy_to_alpha(): legacy = format_identifier("mp-aaaaaaft", legacy=True) assert format_identifier(legacy, legacy=False) == "mp-aaaaaaft" def test_format_identifier_unpadded_alpha_input_produces_padded_output(): # `mp-ft` is the unpadded AlphaID for 149; should normalize to the padded form. assert format_identifier("mp-ft", legacy=False) == "mp-aaaaaaft" def test_format_identifier_above_cutoff_returns_alpha_for_both_modes(): # Values above the legacy cutoff have no meaningful legacy form; # AlphaID.string returns the alpha form in that case. big = "mp-zzzzzz" assert format_identifier(big, legacy=True) == big # The alpha form is the padded version of the same value. padded = format_identifier(big, legacy=False) assert padded.startswith(f"{ID_PREFIX}-") # Prefix "mp-" plus a padded identifier of length ID_PADLEN. assert len(padded) == len(ID_PREFIX) + 1 + ID_PADLEN @pytest.mark.parametrize("value", ["", None]) def test_format_identifier_empty_input_passes_through(value): # Empty / None inputs are returned unchanged so display code is safe to # call on absent values. assert format_identifier(value, legacy=True) is value assert format_identifier(value, legacy=False) is value def test_format_identifier_unparseable_input_returns_str(): # Invalid identifiers should be coerced to str and returned unchanged # (defensive: never raise from a display helper). assert format_identifier("not-an-mpid!!", legacy=True) == "not-an-mpid!!" def test_format_identifier_respects_custom_padlen(): # Padding is configurable; verify a non-default padlen propagates. assert format_identifier("mp-149", legacy=False, padlen=4) == f"{ID_PREFIX}-aaft" def test_format_identifier_respects_custom_prefix(): assert format_identifier(149, legacy=False, prefix="task", padlen=4) == "task-aaft" # --------------------------------------------------------------------------- # format_compound_identifier — composite-id display formatter # --------------------------------------------------------------------------- def test_format_compound_identifier_plain_mpid_passes_through_to_format_identifier(): assert format_compound_identifier("mp-149", legacy=True) == "mp-149" assert format_compound_identifier("mp-149", legacy=False) == "mp-aaaaaaft" def test_format_compound_identifier_battery_id_legacy(): # battery_id = "mp-2658_Al" (composite: mpid + working-ion suffix on "_") assert format_compound_identifier("mp-2658_Al", legacy=True) == "mp-2658_Al" def test_format_compound_identifier_battery_id_alpha_preserves_suffix(): out = format_compound_identifier("mp-2658_Al", legacy=False) assert out.endswith("_Al") assert out.startswith(f"{ID_PREFIX}-") # The mp-prefix portion should be the alpha form of mp-2658. alpha_mpid = format_identifier("mp-2658", legacy=False) assert out == f"{alpha_mpid}_Al" def test_format_compound_identifier_battery_id_round_trip(): original = "mp-2658_Al" alpha = format_compound_identifier(original, legacy=False) assert format_compound_identifier(alpha, legacy=True) == original @pytest.mark.parametrize("value", ["", None]) def test_format_compound_identifier_empty_input_passes_through(value): assert format_compound_identifier(value, legacy=True) is value assert format_compound_identifier(value, legacy=False) is value def test_format_compound_identifier_unparseable_input_passes_through(): # If the leading segment can't be parsed as an MP id, return unchanged. # Note: AlphaID accepts arbitrary lowercase strings with valid separators # (e.g. "junk_suffix" is parsed as prefix="junk", id="suffix"). We use a # value with characters disallowed by AlphaID's alphabet to force the # unparseable fallback path. assert format_compound_identifier("BAD-ID!!_xx", legacy=False) == "BAD-ID!!_xx" # --------------------------------------------------------------------------- # format_task_id — task-id-specific renderer # # Task ids have a different shape convention than other MP identifiers: # - Legacy: `mp-149` (prefixed) # - Alpha: `aaaaaaft` (bare padded alpha, no prefix, no suffix) # --------------------------------------------------------------------------- @pytest.mark.parametrize( "task_id", ["mp-149", "aaaaaaft", "mp-aaaaaaft", "mp-ft", 149], ids=["legacy", "alpha-bare", "alpha-prefixed", "alpha-unpadded", "bare-int"], ) def test_format_task_id_legacy_view_always_returns_mp_prefix(task_id): # Regardless of incoming shape, the legacy view is `mp-`. assert format_task_id(task_id, legacy=True) == "mp-149" @pytest.mark.parametrize( "task_id", ["mp-149", "aaaaaaft", "mp-aaaaaaft", "mp-ft", 149], ids=["legacy", "alpha-bare", "alpha-prefixed", "alpha-unpadded", "bare-int"], ) def test_format_task_id_alpha_view_drops_prefix(task_id): # Regardless of incoming shape, the alpha view is the bare padded form # with no `mp-` prefix. assert format_task_id(task_id, legacy=False) == "aaaaaaft" def test_format_task_id_round_trip_alpha_to_legacy_to_alpha(): legacy = format_task_id("aaaaaaft", legacy=True) assert format_task_id(legacy, legacy=False) == "aaaaaaft" def test_format_task_id_round_trip_legacy_to_alpha_to_legacy(): alpha = format_task_id("mp-149", legacy=False) assert format_task_id(alpha, legacy=True) == "mp-149" @pytest.mark.parametrize("value", ["", None]) def test_format_task_id_empty_input_passes_through(value): assert format_task_id(value, legacy=True) is value assert format_task_id(value, legacy=False) is value def test_format_task_id_unparseable_input_passes_through(): # Defensive: display helpers never raise. assert format_task_id("BAD-ID!!", legacy=True) == "BAD-ID!!" assert format_task_id("BAD-ID!!", legacy=False) == "BAD-ID!!" def test_format_task_id_respects_custom_prefix(): # The legacy prefix is configurable for callers that need a non-default # prefix (e.g. a hypothetical "task-149" rendering). assert format_task_id("aaaaaaft", legacy=True, prefix="task") == "task-149" def test_format_task_id_respects_custom_padlen(): assert format_task_id("mp-149", legacy=False, padlen=4) == "aaft" # --------------------------------------------------------------------------- # Regression: MPID / AlphaID instances must be accepted as input to the # format helpers. The empty-string guard previously triggered # ``MPID.__eq__("")`` which raises ValueError, so passing an MPID-typed value # would crash the page that called it (see # RelatedMaterialsSection.update_related_materials_section in mp_web). # --------------------------------------------------------------------------- def test_format_identifier_accepts_MPID_instance(): assert format_identifier(MPID("mp-149"), legacy=True) == "mp-149" assert format_identifier(MPID("mp-149"), legacy=False) == "mp-aaaaaaft" def test_format_identifier_accepts_AlphaID_instance(): assert format_identifier(AlphaID("mp-149"), legacy=True) == "mp-149" assert format_identifier(AlphaID("mp-149"), legacy=False) == "mp-aaaaaaft" def test_format_compound_identifier_accepts_MPID_instance(): assert format_compound_identifier(MPID("mp-149"), legacy=True) == "mp-149" def test_format_task_id_accepts_MPID_instance(): assert format_task_id(MPID("mp-149"), legacy=True) == "mp-149" assert format_task_id(MPID("mp-149"), legacy=False) == "aaaaaaft" ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011029.0002098 emmet_core-0.87.2/tests/vasp/0000755000175100017510000000000015240717725015522 5ustar00runnerrunner././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8339596 emmet_core-0.87.2/tests/vasp/conftest.py0000644000175100017510000000057615240717720017724 0ustar00runnerrunnerimport gzip import json from pathlib import Path import pytest from emmet.core.tasks import TaskDoc @pytest.fixture(scope="module") def si_tasks(test_dir: Path) -> list[TaskDoc]: with gzip.open(test_dir / "test_si_tasks.json.gz", "rt") as f: data = json.load(f) for task in data: task.update({"is_valid": True}) return [TaskDoc(**d) for d in data] ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8340917 emmet_core-0.87.2/tests/vasp/test_calc_types.py0000644000175100017510000000200115240717720021245 0ustar00runnerrunnerfrom importlib.resources import files as import_resource_file from ruamel.yaml import YAML with open( import_resource_file("emmet.core.vasp.calc_types") / "calc_types.yaml", "r" ) as f: config = YAML().load(f) _REFERENCE_MEMBER_COUNT = { "RunType": 2 * sum(len(rtypes) for rtypes in config["RUN_TYPES"].values()), "TaskType": len(config["TASK_TYPES"]), } _REFERENCE_MEMBER_COUNT["CalcType"] = ( _REFERENCE_MEMBER_COUNT["RunType"] * _REFERENCE_MEMBER_COUNT["TaskType"] ) def test_enums(): from emmet.core.vasp.calc_types import enums as vasp_enums for k, count in _REFERENCE_MEMBER_COUNT.items(): # Test that we have expected number of enum members curr_enum = getattr(vasp_enums, k, None) assert len(curr_enum) == count if k == "RunType": # Test that RunType enum is not case sensitive assert all( curr_enum(member.value.upper()) == curr_enum(member.value.lower()) for member in curr_enum ) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8340917 emmet_core-0.87.2/tests/vasp/test_materials.py0000644000175100017510000000277215240717720021117 0ustar00runnerrunnerimport pytest from emmet.core import ARROW_COMPATIBLE from emmet.core.vasp.calc_types import TaskType from emmet.core.vasp.material import MaterialsDoc if ARROW_COMPATIBLE: import pyarrow as pa from emmet.core.arrow import arrowize def test_make_mat(si_tasks): material = MaterialsDoc.from_tasks(si_tasks) assert material.formula_pretty == "Si" assert len(material.task_ids) == 4 assert len(material.entries.model_dump(exclude_none=True)) == 1 bad_task_group = [ task for task in si_tasks if task.task_type != TaskType.Structure_Optimization ] with pytest.raises(Exception): MaterialsDoc.from_tasks(bad_task_group, use_statics=False) def test_make_deprecated_mat(si_tasks): bad_task_group = [ task for task in si_tasks if task.task_type != TaskType.Structure_Optimization ] material = MaterialsDoc.construct_deprecated_material(bad_task_group) assert material.deprecated assert material.formula_pretty == "Si" assert len(material.task_ids) == 3 assert material.entries is None def test_schema(): MaterialsDoc.schema() @pytest.mark.skipif( not ARROW_COMPATIBLE, reason="pyarrow must be installed to run this test." ) def test_arrow(si_tasks): doc = MaterialsDoc.from_tasks(si_tasks) arrow_struct = pa.scalar( doc.model_dump(context={"format": "arrow"}), type=arrowize(MaterialsDoc) ) test_arrow_doc = MaterialsDoc(**arrow_struct.as_py(maps_as_pydicts="strict")) assert doc == test_arrow_doc ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8340917 emmet_core-0.87.2/tests/vasp/test_utils.py0000644000175100017510000001021015240717720020260 0ustar00runnerrunner"""Test VASP parsing utilities.""" from tempfile import TemporaryDirectory from pathlib import Path import pytest from emmet.core.vasp.utils import ( recursive_discover_vasp_files, discover_and_sort_vasp_files, FileMetadata, ) def test_file_meta(tmp_dir): import blake3 incar_bytes = """ ALGO = Normal ENCUT = 500 EDIFF = 0.0001 IBRION = -1 """.encode() with open(file_name := "INCAR.bz2", "wb") as f: f.write(incar_bytes) file_meta = FileMetadata(name="INCAR.bz2", path=file_name) file_meta.compute_hash() assert Path(file_meta.path).exists() assert file_meta.hash == blake3.blake3(incar_bytes).hexdigest() def test_file_discovery(): directory_structure = { "./neb_calc/00/": ["INCAR.gz", "KPOINTS", "POSCAR.gz"], "./neb_calc/01/": [ "INCAR.gz", "CHGCAR", "CONTCAR.gz", "KPOINTS", "OUTCAR", "POSCAR.gz", "POTCAR.bz2", "vasprun.xml", ], "./neb_calc/02/": ["INCAR.gz", "KPOINTS", "POSCAR.gz"], "neb_calc": ["INCAR.gz", "KPOINTS", "POSCAR.gz", "POTCAR.lzma"], "block_2025_02_30/launcher_2025_02_31/launcher_2025_02_31_0001": [ "AECCAR0.bz2", "LOCPOT.gz", "CONTCAR.relax1", "OUTCAR.relax1", "vasprun.xml.relax1", "POSCAR.T=300.gz", "POSCAR.T=1000.gz", ], } for idx in range(1, 3): directory_structure[ "block_2025_02_30/launcher_2025_02_31/launcher_2025_02_31_0001" ].extend( [ f"INCAR.relax{idx}", f"INCAR.orig.relax{idx}", f"POSCAR.relax{idx}", f"POSCAR.orig.relax{idx}", f"POTCAR.relax{idx}", f"POTCAR.orig.relax{idx}", ] ) with TemporaryDirectory() as _tmp_dir: tmp_dir = Path(_tmp_dir).resolve() for calc_dir, files in directory_structure.items(): p = tmp_dir / calc_dir if not p.exists(): p.mkdir(parents=True, exist_ok=True) for f in files: (p / f).touch() vasp_files = recursive_discover_vasp_files(tmp_dir) valid_vasp_files = recursive_discover_vasp_files(tmp_dir, only_valid=True) assert len(recursive_discover_vasp_files(tmp_dir, max_depth=2)) == 4 assert len(recursive_discover_vasp_files(tmp_dir, max_depth=1)) == 1 files_by_calc_suffix = discover_and_sort_vasp_files( tmp_dir / "block_2025_02_30/launcher_2025_02_31/launcher_2025_02_31_0001" ) for max_depth in (-1, 1.5): with pytest.raises(ValueError, match="non-negative integer"): _ = recursive_discover_vasp_files(tmp_dir, max_depth=max_depth) assert files_by_calc_suffix["relax1"]["contcar_file"].name == "CONTCAR.relax1" assert files_by_calc_suffix["relax1"]["vasprun_file"].name == "vasprun.xml.relax1" assert files_by_calc_suffix["relax1"]["outcar_file"].name == "OUTCAR.relax1" assert {b.name for b in files_by_calc_suffix["standard"]["volumetric_files"]} == { "AECCAR0.bz2", "LOCPOT.gz", } assert {b.name for b in files_by_calc_suffix["standard"]["elph_poscars"]} == { "POSCAR.T=300.gz", "POSCAR.T=1000.gz", } # should find all of the defined calculation directories + suffixes within those assert len(vasp_files) == 7 found_files = set() ref_files = set() for calc_dir, files in directory_structure.items(): ref_files.update({tmp_dir / calc_dir / file for file in files}) for file_metas in vasp_files.values(): found_files.update({file_meta.path for file_meta in file_metas}) assert found_files == ref_files # Should only find two valid calculation directory / suffix pairs valid_calc_dirs = ( "neb_calc/01/", "block_2025_02_30/launcher_2025_02_31/launcher_2025_02_31_0001", ) assert len(valid_vasp_files) == 2 found_valid_dirs = {calc_loc.path for calc_loc in valid_vasp_files} assert {tmp_dir / valid_dir for valid_dir in valid_calc_dirs} == found_valid_dirs ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1787011023.8340917 emmet_core-0.87.2/tests/vasp/test_vasp.py0000644000175100017510000001404415240717720020102 0ustar00runnerrunnerimport json import pytest from monty.io import zopen from emmet.core.mpid import AlphaID from emmet.core.tasks import TaskDoc from emmet.core.vasp.calc_types import RunType, TaskType, run_type, task_type from emmet.core.vasp.validation import ValidationDoc from emmet.core.vasp.validation_legacy import ValidationDoc as LegacyValidationDoc from emmet.core.vasp.task_valid import TaskDocument from emmet.core.testing_utils import DataArchive from emmet.core.vasp.utils import discover_vasp_files # As the validator itself is a separate package with its own tests, # only check that its integration within emmet-core works. def test_task_type(): # TODO: Switch this to actual inputs? input_types = [ ("NSCF Line", {"incar": {"ICHARG": 11}, "kpoints": {"labels": ["A"]}}), ("NSCF Uniform", {"incar": {"ICHARG": 11}}), ("Dielectric", {"incar": {"LEPSILON": True}}), ("DFPT Dielectric", {"incar": {"LEPSILON": True, "IBRION": 7}}), ("DFPT Dielectric", {"incar": {"LEPSILON": True, "IBRION": 8}}), ("DFPT", {"incar": {"IBRION": 7}}), ("DFPT", {"incar": {"IBRION": 8}}), ("Static", {"incar": {"NSW": 0}}), ] for _type, inputs in input_types: assert task_type(inputs) == TaskType(_type) def test_run_type(): params_sets = [ ("GGA", {"GGA": "--"}), ("GGA+U", {"GGA": "--", "LDAU": True}), ("SCAN", {"METAGGA": "Scan"}), ("SCAN+U", {"METAGGA": "Scan", "LDAU": True}), ("r2SCAN", {"METAGGA": "R2SCAN"}), ("r2SCAN+U", {"METAGGA": "R2SCAN", "LDAU": True}), ("HFCus", {"LHFCALC": True}), ] for _type, params in params_sets: assert run_type(params) == RunType(_type) def test_legacy_validator(si_tasks): validation_docs = [LegacyValidationDoc.from_task_doc(task) for task in si_tasks] assert len(validation_docs) == len(si_tasks) assert all([doc.valid for doc in validation_docs]) def test_legacy_validator_magmom(test_dir): # Task with Cr in structure - this is only element with MAGMOM check def cr_task_dict(): # Model validation for TaskDoc serializes some fields. # Need to refresh reference unserialized dict to have # serialization for TaskDocument also work. with zopen(test_dir / "task_doc_mp-2766060.json.gz", "rt") as f: _cr_task_dict = json.load(f) return _cr_task_dict taskdoc = TaskDoc(**cr_task_dict()) assert LegacyValidationDoc.from_task_doc(taskdoc).valid # test backwards compatibility taskdocument = TaskDocument( **{k: v for k, v in cr_task_dict().items() if k != "last_updated"} ) assert LegacyValidationDoc.from_task_doc(taskdocument).valid # Change MAGMOM on Cr to fail magmom test td_bad_mag = TaskDoc(**cr_task_dict()) td_bad_mag.calcs_reversed[0].output.outcar["magnetization"] = [ {"tot": 6.0} if td_bad_mag.structure[ientry].species_string == "Cr" else entry for ientry, entry in enumerate( td_bad_mag.calcs_reversed[0].output.outcar["magnetization"] ) ] assert not (valid_doc := LegacyValidationDoc.from_task_doc(td_bad_mag)).valid assert any("MAG" in repr(reason) for reason in valid_doc.reasons) # Remove magnetization tag to simulate spin-unpolarized (ISPIN = 1) calculation td_no_mag = TaskDoc(**cr_task_dict()) del td_no_mag.calcs_reversed[0].output.outcar["magnetization"] assert LegacyValidationDoc.from_task_doc(td_no_mag).valid def test_validator_failed_symmetry(test_dir): with zopen(test_dir / "failed_elastic_task.json.gz", "rt") as f: failed_task = json.load(f) taskdoc = TaskDoc(**failed_task) validation = LegacyValidationDoc.from_task_doc(taskdoc) assert any("SYMMETRY" in repr(reason) for reason in validation.reasons) def test_computed_entry(si_tasks): entries = [task.entry for task in si_tasks] ids = {e.entry_id for e in entries} assert ids == set( [ f"{AlphaID(id_str)}-GGA" for id_str in {"mp-ddzda", "mp-dryyt", "mp-cmxxl", "mp-ft"} ] ) @pytest.fixture(scope="session") def task_ldau(test_dir): with zopen(test_dir / "test_task.json.gz", "rt") as f: data = json.load(f) return TaskDoc(**data) def test_ldau(task_ldau): task_ldau.input.is_hubbard = True assert task_ldau.run_type == RunType.GGA_U assert not LegacyValidationDoc.from_task_doc(task_ldau).valid def test_ldau_validation(test_dir): with zopen(test_dir / "old_aflow_ggau_task.json.gz", "rt") as f: data = json.load(f) task = TaskDoc(**data) assert task.run_type == "GGA+U" valid = LegacyValidationDoc.from_task_doc(task) assert valid.valid def test_validator_integration(test_dir): calc_dirs = { "Si_old_double_relax": ["VASP VERSION"], "Si_uniform": [], "Si_static": [], } for calc_dir, ref_reasons in calc_dirs.items(): with DataArchive.extract(test_dir / "vasp" / f"{calc_dir}.json.gz") as tmp_dir: task_doc = TaskDoc.from_directory(tmp_dir) valid_doc = ValidationDoc.from_task_doc(task_doc, check_potcar=False) if len(ref_reasons) == 0: assert valid_doc.valid else: assert not valid_doc.valid assert all( any(reason_match in reason for reason in valid_doc.reasons) for reason_match in ref_reasons ) def test_from_file_meta_and_dir(test_dir): with DataArchive.extract(test_dir / "vasp" / "Si_static.json.gz") as tmp_dir: files_by_suffix = discover_vasp_files(tmp_dir) valid_doc_from_meta = ValidationDoc.from_file_metadata( files_by_suffix["standard"], check_potcar=False ) valid_doc_from_dir = ValidationDoc.from_directory(tmp_dir, check_potcar=False) assert valid_doc_from_meta.valid for k in ValidationDoc.model_fields: if k in {"last_updated", "builder_meta"}: # these contain time-stamped fields that won't match continue assert getattr(valid_doc_from_meta, k) == getattr(valid_doc_from_dir, k)