pymatgen.analysis.structure_prediction.volume_predictor — pymatgen 2020.7.3 documentation

# 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

    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

        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!")

        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

    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

        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

        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