Source code for pymatgen.analysis.structure_prediction.volume_predictor

# coding: utf-8
# Copyright (c) Pymatgen Development Team.
# Distributed under the terms of the MIT License.

"""
Predict volumes of crystal structures.
"""

import warnings
import os

import numpy as np

from monty.serialization import loadfn
from pymatgen.analysis.bond_valence import BVAnalyzer
from pymatgen.analysis.structure_matcher import StructureMatcher
from pymatgen.core import Structure

MODULE_DIR = os.path.dirname(os.path.abspath(__file__))
bond_params = loadfn(os.path.join(MODULE_DIR, 'DLS_bond_params.yaml'))


def _is_ox(structure):
    comp = structure.composition
    for k in comp.keys():
        try:
            k.oxi_state
        except AttributeError:
            return False
    return True


[docs]class RLSVolumePredictor: """ Reference lattice scaling (RLS) scheme that predicts the volume of a structure based on a known crystal structure. """ def __init__(self, check_isostructural=True, radii_type="ionic-atomic", use_bv=True): """ Args: check_isostructural: Whether to test that the two structures are isostructural. This algo works best for isostructural compounds. Defaults to True. radii_type (str): Types of radii to use. You can specify "ionic" (only uses ionic radii), "atomic" (only uses atomic radii) or "ionic-atomic" (uses either ionic or atomic radii, with a preference for ionic where possible). use_bv (bool): Whether to use BVAnalyzer to determine oxidation states if not present. """ self.check_isostructural = check_isostructural self.radii_type = radii_type self.use_bv = use_bv
[docs] def predict(self, structure, ref_structure): """ Given a structure, returns the predicted volume. Args: structure (Structure): structure w/unknown volume ref_structure (Structure): A reference structure with a similar structure but different species. Returns: a float value of the predicted volume """ if self.check_isostructural: m = StructureMatcher() mapping = m.get_best_electronegativity_anonymous_mapping( structure, ref_structure) if mapping is None: raise ValueError("Input structures do not match!") if "ionic" in self.radii_type: try: # Use BV analyzer to determine oxidation states only if the # oxidation states are not already specified in the structure # and use_bv is true. if (not _is_ox(structure)) and self.use_bv: a = BVAnalyzer() structure = a.get_oxi_state_decorated_structure(structure) if (not _is_ox(ref_structure)) and self.use_bv: a = BVAnalyzer() ref_structure = a.get_oxi_state_decorated_structure( ref_structure) comp = structure.composition ref_comp = ref_structure.composition # Check if all the associated ionic radii are available. if any([k.ionic_radius is None for k in list(comp.keys())]) or \ any([k.ionic_radius is None for k in list(ref_comp.keys())]): raise ValueError("Not all the ionic radii are available!") numerator = 0 denominator = 0 # Here, the 1/3 factor on the composition accounts for atomic # packing. We want the number per unit length. for k, v in comp.items(): numerator += k.ionic_radius * v ** (1 / 3) for k, v in ref_comp.items(): denominator += k.ionic_radius * v ** (1 / 3) return ref_structure.volume * (numerator / denominator) ** 3 except Exception: warnings.warn("Exception occured. Will attempt atomic radii.") # If error occurs during use of ionic radii scheme, pass # and see if we can resolve it using atomic radii. pass if "atomic" in self.radii_type: comp = structure.composition ref_comp = ref_structure.composition # Here, the 1/3 factor on the composition accounts for atomic # packing. We want the number per unit length. numerator = 0 denominator = 0 for k, v in comp.items(): numerator += k.atomic_radius * v ** (1 / 3) for k, v in ref_comp.items(): denominator += k.atomic_radius * v ** (1 / 3) return ref_structure.volume * (numerator / denominator) ** 3 raise ValueError("Cannot find volume scaling based on radii choices " "specified!")
[docs] def get_predicted_structure(self, structure, ref_structure): """ Given a structure, returns back the structure scaled to predicted volume. Args: structure (Structure): structure w/unknown volume ref_structure (Structure): A reference structure with a similar structure but different species. Returns: a Structure object with predicted volume """ new_structure = structure.copy() new_structure.scale_lattice(self.predict(structure, ref_structure)) return new_structure
[docs]class DLSVolumePredictor: """ Data-mined lattice scaling (DLS) scheme that relies on data-mined bond lengths to predict the crystal volume of a given structure. As of 2/12/19, we suggest this method be used in conjunction with min_scaling and max_scaling to prevent instances of very large, unphysical predicted volumes found in a small subset of structures. """ def __init__(self, cutoff=4.0, min_scaling=0.5, max_scaling=1.5): """ Args: cutoff (float): cutoff radius added to site radius for finding site pairs. Necessary to increase only if your initial structure guess is extremely bad (atoms way too far apart). In all other instances, increasing cutoff gives same answer but takes more time. min_scaling (float): if not None, this will ensure that the new volume is at least this fraction of the original (preventing too-small volumes) max_scaling (float): if not None, this will ensure that the new volume is at most this fraction of the original (preventing too-large volumes) """ self.cutoff = cutoff self.min_scaling = min_scaling self.max_scaling = max_scaling
[docs] def predict(self, structure, icsd_vol=False): """ Given a structure, returns the predicted volume. Args: structure (Structure) : a crystal structure with an unknown volume. icsd_vol (bool) : True if the input structure's volume comes from ICSD. Returns: a float value of the predicted volume. """ # Get standard deviation of electronnegativity in the structure. std_x = np.std([site.specie.X for site in structure]) # Sites that have atomic radii sub_sites = [] # Record the "DLS estimated radius" from bond_params. bp_dict = {} for sp in list(structure.composition.keys()): if sp.atomic_radius: sub_sites.extend([site for site in structure if site.specie == sp]) else: warnings.warn("VolumePredictor: no atomic radius data for " "{}".format(sp)) if sp.symbol not in bond_params: warnings.warn("VolumePredictor: bond parameters not found, " "used atomic radii for {}".format(sp)) else: r, k = bond_params[sp.symbol]["r"], bond_params[sp.symbol]["k"] bp_dict[sp] = float(r) + float(k) * std_x # Structure object that include only sites with known atomic radii. reduced_structure = Structure.from_sites(sub_sites) smallest_ratio = None for site1 in reduced_structure: sp1 = site1.specie neighbors = reduced_structure.get_neighbors(site1, sp1.atomic_radius + self.cutoff) for nn in neighbors: sp2 = nn.specie if sp1 in bp_dict and sp2 in bp_dict: expected_dist = bp_dict[sp1] + bp_dict[sp2] else: expected_dist = sp1.atomic_radius + sp2.atomic_radius if not smallest_ratio or nn.nn_distance / expected_dist < smallest_ratio: smallest_ratio = nn.nn_distance / expected_dist if not smallest_ratio: raise ValueError("Could not find any bonds within the given cutoff " "in this structure.") volume_factor = (1 / smallest_ratio) ** 3 # icsd volume fudge factor if icsd_vol: volume_factor *= 1.05 if self.min_scaling: volume_factor = max(self.min_scaling, volume_factor) if self.max_scaling: volume_factor = min(self.max_scaling, volume_factor) return structure.volume * volume_factor
[docs] def get_predicted_structure(self, structure, icsd_vol=False): """ Given a structure, returns back the structure scaled to predicted volume. Args: structure (Structure): structure w/unknown volume Returns: a Structure object with predicted volume """ new_structure = structure.copy() new_structure.scale_lattice(self.predict(structure, icsd_vol=icsd_vol)) return new_structure