Source code for nanoCAT.bde.guess_core_dist


A module for estimating ideal values for ``["optional"]["qd"]["bde"]["core_core_dist"]`` .

.. currentmodule:: nanoCAT.bde.guess_core_dist
.. autosummary::

.. autofunction:: guess_core_core_dist


from typing import Union, Tuple

import numpy as np
from scipy.spatial.distance import cdist
from scipy.signal import savgol_filter

from scm.plams import Molecule, MoleculeError

from CAT.mol_utils import to_atnum, to_symbol
from FOX.functions.rdf import get_rdf_lowmem as get_rdf

__all__ = ['guess_core_core_dist']

#: An atomic symbol or number.
AtomSymbol = Union[str, int]

#: A 2-tuple of atomc symbols/numbers.
AtomSymbolTup = Tuple[AtomSymbol, AtomSymbol]

def _get_xyz(mol: Molecule, atom: AtomSymbol) -> np.ndarray:
    """Return the Cartesian coordinates of **mol** belonging to the atom subset of *atom*."""
    atnum = to_atnum(atom)
    xyz = mol.as_array(atom_subset=(at for at in mol if at.atnum == atnum))

    if not xyz.any():
        raise MoleculeError(f"No atoms with atomic symbol {to_symbol(atom)!r} "
                            f"in {mol.get_formula()!r}")
    return xyz

[docs]def guess_core_core_dist(mol: Union[Molecule, np.ndarray], atom: Union[None, AtomSymbol, AtomSymbolTup] = None, dr: float = 0.1, r_max: float = 8.0, window_length: int = 21, polyorder: int = 7) -> float: """Guess a value for the ``["optional"]["qd"]["bde"]["core_core_dist"]`` parameter in **CAT**. The estimation procedure involves finding the first minimum in the radial distribution function (RDF) of **mol**. After smoothing the RDF wth a Savitzky-Golay filer, the gradient of the RDF is explored (starting from the RDFs' global maximum) until a stationary point is found with a positive second derivative (*i.e.* a minimum). Examples -------- .. code:: python >>> from scm.plams import Molecule >>> from nanoCAT.bde.guess_core_dist import guess_core_core_dist >>> atom1 = 'Cl' # equivalent to ('Cl', 'Cl') >>> atom2 = 'Cl', 'Br' >>> mol = Molecule(...) >>> guess_core_core_dist(mol, atom1) >>> guess_core_core_dist(mol, atom2) Parameters ---------- mol : array-like [:class:`float`], shape :math:`(n, 3)` A molecule. atom : :class:`str` or :class:`int`, optional An atomic number or symbol for defining an atom subset within **mol**. The RDF is constructed for this subset. Providing a 2-tuple will construct the RDF between these 2 atom subsets. dr : :class:`float` The RDF integration step-size in Angstrom, *i.e.* the distance between concentric spheres. r_max : :class:`float` The maximum to be evaluated interatomic distance in the RDF. window_length : :class:`int` The length of the filter window (*i.e.* the number of coefficients) for the Savitzky-Golay filter. polyorder : :class:`int` The order of the polynomial used to fit the samples for the Savitzky-Golay filter. Returns ------- :class:`float` The interatomic radius of the first RDF minimum (following the first maximum). Raises ------ MoleculeError Raised if **atom** is not in **mol**. ValueError Raised if no minimum is found in the smoothed RDF. See Also -------- :func:`savgol_filter()<scipy.signal.savgol_filter>` Apply a Savitzky-Golay filter to an array. """ if atom is None: xyz1 = xyz2 = np.asarray(mol) elif isinstance(atom, tuple): if len(atom) != 2: raise ValueError(f"'atom' expected a tuple of length 2; observed length: {len(atom)}") xyz1 = _get_xyz(mol, atom[0]) xyz2 = _get_xyz(mol, atom[1]) else: xyz1 = xyz2 = _get_xyz(mol, atom) # Create a disance matrix dist = cdist(xyz1, xyz2) # Create and smooth the RDF rdf = get_rdf(dist, dr=dr, r_max=r_max) rdf[0] = 0.0 rdf_smooth = savgol_filter(rdf, window_length, polyorder) # Find the global maximum and calculate the gradient of the smoothed RDF idx_max = rdf_smooth.argmax() grad = np.gradient(rdf_smooth[idx_max:]) # Find and return the first point after the global maximum where the gradient is ~0 # And the second derivative is positive for i, (j, k) in enumerate(zip(grad, grad[1:])): if not (j <= 0 and k >= 0): continue # Interpolate and return idx = (0 - j) / (k - j) return dr * (idx_max + i + idx) raise ValueError("No minimum found in the (smoothed) radial distribution function of 'mol'")