Source code for nanoCAT.cdft

from math import nan
from typing import Iterable, Any, Type, List, Union

import yaml
import pandas as pd

from qmflows import templates as _templates
from qmflows.packages.SCM import ADF_Result
from scm.plams import Molecule, Settings, ADFJob, ADFResults, Units, Results
from scm.plams.core.basejob import Job
from CAT.workflows import WorkFlow, JOB_SETTINGS_CDFT, MOL
from import job_single_point  # noqa: F401
from CAT.settings_dataframe import SettingsDataFrame

__all__ = ['init_cdft', 'get_global_descriptors', 'cdft']

_CDFT: str = """specific:
        symmetry: nosym
            enabled: yes
            analysislevel: extended
            electronegativity: yes
                enabled: yes
            enabled: yes
            analysislevel: extended
            energy: yes
            core: none
            type: DZP
        numericalquality: good

#: A QMFlows-style template for conceptual DFT calculations.
cdft = Settings(yaml.safe_load(_CDFT))
cdft.specific.adf += _templates.singlepoint.specific.adf.copy()

def init_cdft(ligand_df: SettingsDataFrame) -> None:
    r"""Initialize the ligand conceptual dft (CDFT) workflow.

    ligand_df : |CAT.SettingsDataFrame|
        A DataFrame of ligands.

    workflow = WorkFlow.from_template(ligand_df, name='cdft')

    # Import from the database and start the calculation
    idx = workflow.from_db(ligand_df)
    workflow(start_crs_jobs, ligand_df, index=idx)

    # Sets a nested list with the filenames of .in files
    # This cannot be done with loc is it will try to expand the list into a 2D array
    ligand_df[JOB_SETTINGS_CDFT] = workflow.pop_job_settings(ligand_df[MOL])

    # Export to the database
    job_recipe = workflow.get_recipe()
    workflow.to_db(ligand_df, index=idx, job_recipe=job_recipe)

def start_crs_jobs(mol_list: Iterable[Molecule],
                   jobs: Iterable[Type[Job]], settings: Iterable[Settings],
                   **kwargs: Any) -> List[pd.Series]:
    # Parse the job type
    job, *_ = jobs
    if job is not ADFJob:
        raise NotImplementedError(f"job: {job.__class__.__name__} = {job!r}")

    # Parse and update the input settings
    _s, *_ = settings
    s = Settings(_s)
    s.input += cdft.specific.adf

    ret = []
    for mol in mol_list:
        ret.append(run_cdft_job(mol, job, s))
    return ret

_BACKUP = pd.Series({
    'Electronic chemical potential (mu)': nan,
    'Electronegativity (chi=-mu)': nan,
    'Hardness (eta)': nan,
    'Softness (S)': nan,
    'Hyperhardness (gamma)': nan,
    'Electrophilicity index (w=omega)': nan,
    'Dissocation energy (nucleofuge)': nan,
    'Dissociation energy (electrofuge)': nan,
    'Electrodonating power (w-)': nan,
    'Electroaccepting power(w+)': nan,
    'Net Electrophilicity': nan,
    'Global Dual Descriptor Deltaf+': nan,
    'Global Dual Descriptor Deltaf-': nan,
    'Electronic chemical potential (mu+)': nan,
    'Electronic chemical potential (mu-)': nan
_BACKUP.index = pd.MultiIndex.from_product(
    [['cdft'], _BACKUP.index], names=['index', 'sub index']

def run_cdft_job(mol: Molecule, job: Type[ADFJob], s: Settings) -> pd.Series:
    """Run a conceptual DFT job and extract & return all global descriptors."""
    results = mol.job_single_point(job, s.copy(), name='CDFT',
                                   ret_results=True, read_template=False)

    if results.job.status in {'crashed', 'failed'}:
        return _BACKUP

    ret = get_global_descriptors(results)
    ret.index = pd.MultiIndex.from_product(
        [['cdft'], ret.index], names=['index', 'sub index']
    return ret

[docs]def get_global_descriptors(results: Union[ADFResults, ADF_Result]) -> pd.Series: """Extract a dictionary with all ADF conceptual DFT global descriptors from **results**. Examples -------- .. code:: python >>> import pandas as pd >>> from scm.plams import ADFResults >>> from import get_global_descriptors >>> results = ADFResults(...) >>> series: pd.Series = get_global_descriptors(results) >>> print(dct) Electronic chemical potential (mu) -0.113 Electronegativity (chi=-mu) 0.113 Hardness (eta) 0.090 Softness (S) 11.154 Hyperhardness (gamma) -0.161 Electrophilicity index (w=omega) 0.071 Dissocation energy (nucleofuge) 0.084 Dissociation energy (electrofuge) 6.243 Electrodonating power (w-) 0.205 Electroaccepting power(w+) 0.092 Net Electrophilicity 0.297 Global Dual Descriptor Deltaf+ 0.297 Global Dual Descriptor Deltaf- -0.297 Electronic chemical potential (mu+) -0.068 Electronic chemical potential (mu-) -0.158 Name: global descriptors, dtype: float64 Parameters ---------- results : :class:`plams.ADFResults` or :class:`qmflows.ADF_Result` A PLAMS Results or QMFlows Result instance of an ADF calculation. Returns ------- :class:`pandas.Series` A Series with all ADF global decsriptors as extracted from **results**. """ if not isinstance(results, Results): results = results.results file = results['$JN.out'] with open(file) as f: # Identify the GLOBAL DESCRIPTORS block for item in f: if item == ' GLOBAL DESCRIPTORS\n': next(f) next(f) break else: raise ValueError(f"Failed to identify the 'GLOBAL DESCRIPTORS' block in {file!r}") # Extract the descriptors ret = {} for item in f: item = item.rstrip('\n') if not item: break _key, _value = item.rsplit('=', maxsplit=1) key = _key.strip() try: value = float(_value) except ValueError: value = float(_value.rstrip('(eV)')) * Units.conversion_ratio('ev', 'au') ret[key] = value # Fix the names of "mu+" and "mu-" ret['Electronic chemical potential (mu+)'] = ret.pop('mu+', nan) ret['Electronic chemical potential (mu-)'] = ret.pop('mu-', nan) return pd.Series(ret, name='global descriptors')