Source code for pymatgen.transformations.advanced_transformations

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

"""
This module implements more advanced transformations.
"""
from typing import Optional, Dict

import numpy as np
from fractions import Fraction
from math import gcd
from itertools import groupby, product
from string import ascii_lowercase
from warnings import warn
import logging
import math

import warnings

from monty.dev import requires
from monty.fractions import lcm
from monty.json import MSONable

from pymatgen.core.periodic_table import Element, Specie, get_el_sp, DummySpecie
from pymatgen.io.ase import AseAtomsAdaptor
from pymatgen.transformations.transformation_abc import AbstractTransformation
from pymatgen.transformations.standard_transformations import (
    SubstitutionTransformation,
    OrderDisorderedStructureTransformation,
    SupercellTransformation,
)
from pymatgen.command_line.enumlib_caller import EnumlibAdaptor, EnumError
from pymatgen.analysis.ewald import EwaldSummation
from pymatgen.core.structure import Structure
from pymatgen.symmetry.analyzer import SpacegroupAnalyzer
from pymatgen.analysis.structure_prediction.substitution_probability import (
    SubstitutionPredictor,
)
from pymatgen.analysis.structure_matcher import StructureMatcher, SpinComparator
from pymatgen.analysis.energy_models import SymmetryModel
from pymatgen.analysis.bond_valence import BVAnalyzer
from pymatgen.core.surface import SlabGenerator
from pymatgen.electronic_structure.core import Spin
from pymatgen.analysis.gb.grain import GrainBoundaryGenerator
from pymatgen.analysis.adsorption import AdsorbateSiteFinder
from pymatgen.command_line.mcsqs_caller import run_mcsqs
from pymatgen.analysis.local_env import MinimumDistanceNN

try:
    import hiphive  # type: ignore
except ImportError:
    hiphive = None

__author__ = (
    "Shyue Ping Ong, Stephen Dacek, Anubhav Jain, Matthew Horton, " "Alex Ganose"
)
__copyright__ = "Copyright 2012, The Materials Project"
__version__ = "1.0"
__maintainer__ = "Shyue Ping Ong"
__email__ = "shyuep@gmail.com"
__date__ = "Jul 24, 2012"

logger = logging.getLogger(__name__)


[docs]class ChargeBalanceTransformation(AbstractTransformation): """ This is a transformation that disorders a structure to make it charge balanced, given an oxidation state-decorated structure. """ def __init__(self, charge_balance_sp): """ Args: charge_balance_sp: specie to add or remove. Currently only removal is supported """ self.charge_balance_sp = str(charge_balance_sp)
[docs] def apply_transformation(self, structure): """ Applies the transformation. Args: structure: Input Structure Returns: Charge balanced structure. """ charge = structure.charge specie = get_el_sp(self.charge_balance_sp) num_to_remove = charge / specie.oxi_state num_in_structure = structure.composition[specie] removal_fraction = num_to_remove / num_in_structure if removal_fraction < 0: raise ValueError( "addition of specie not yet supported by " "ChargeBalanceTransformation" ) trans = SubstitutionTransformation( {self.charge_balance_sp: {self.charge_balance_sp: 1 - removal_fraction}} ) return trans.apply_transformation(structure)
def __str__(self): return "Charge Balance Transformation : " + "Species to remove = {}".format( str(self.charge_balance_sp) ) def __repr__(self): return self.__str__() @property def inverse(self): """Returns: None""" return None @property def is_one_to_many(self): """Returns: False""" return False
[docs]class SuperTransformation(AbstractTransformation): """ This is a transformation that is inherently one-to-many. It is constructed from a list of transformations and returns one structure for each transformation. The primary use for this class is extending a transmuter object. """ def __init__(self, transformations, nstructures_per_trans=1): """ Args: transformations ([transformations]): List of transformations to apply to a structure. One transformation is applied to each output structure. nstructures_per_trans (int): If the transformations are one-to-many and, nstructures_per_trans structures from each transformation are added to the full list. Defaults to 1, i.e., only best structure. """ self._transformations = transformations self.nstructures_per_trans = nstructures_per_trans
[docs] def apply_transformation(self, structure, return_ranked_list=False): """ Applies the transformation. Args: structure: Input Structure return_ranked_list: Number of structures to return. Returns: Structures with all transformations applied. """ if not return_ranked_list: raise ValueError( "SuperTransformation has no single best structure" " output. Must use return_ranked_list" ) structures = [] for t in self._transformations: if t.is_one_to_many: for d in t.apply_transformation( structure, return_ranked_list=self.nstructures_per_trans ): d["transformation"] = t structures.append(d) else: structures.append( { "transformation": t, "structure": t.apply_transformation(structure), } ) return structures
def __str__(self): return "Super Transformation : Transformations = " + "{}".format( " ".join([str(t) for t in self._transformations]) ) def __repr__(self): return self.__str__() @property def inverse(self): """Returns: None""" return None @property def is_one_to_many(self): """Returns: True""" return True
[docs]class MultipleSubstitutionTransformation: """ Performs multiple substitutions on a structure. For example, can do a fractional replacement of Ge in LiGePS with a list of species, creating one structure for each substitution. Ordering is done using a dummy element so only one ordering must be done per substitution oxidation state. Charge balancing of the structure is optionally performed. .. note:: There are no checks to make sure that removal fractions are possible and rounding may occur. Currently charge balancing only works for removal of species. """ def __init__( self, sp_to_replace, r_fraction, substitution_dict, charge_balance_species=None, order=True, ): """ Performs multiple fractional substitutions on a transmuter. Args: sp_to_replace: species to be replaced r_fraction: fraction of that specie to replace substitution_dict: dictionary of the format {2: ["Mg", "Ti", "V", "As", "Cr", "Ta", "N", "Nb"], 3: ["Ru", "Fe", "Co", "Ce", "As", "Cr", "Ta", "N", "Nb"], 4: ["Ru", "V", "Cr", "Ta", "N", "Nb"], 5: ["Ru", "W", "Mn"] } The number is the charge used for each of the list of elements (an element can be present in multiple lists) charge_balance_species: If specified, will balance the charge on the structure using that specie. """ self.sp_to_replace = sp_to_replace self.r_fraction = r_fraction self.substitution_dict = substitution_dict self.charge_balance_species = charge_balance_species self.order = order
[docs] def apply_transformation(self, structure, return_ranked_list=False): """ Applies the transformation. Args: structure: Input Structure return_ranked_list: Number of structures to return. Returns: Structures with all substitutions applied. """ if not return_ranked_list: raise ValueError( "MultipleSubstitutionTransformation has no single" " best structure output. Must use" " return_ranked_list." ) outputs = [] for charge, el_list in self.substitution_dict.items(): mapping = {} if charge > 0: sign = "+" else: sign = "-" dummy_sp = "X{}{}".format(str(charge), sign) mapping[self.sp_to_replace] = { self.sp_to_replace: 1 - self.r_fraction, dummy_sp: self.r_fraction, } trans = SubstitutionTransformation(mapping) dummy_structure = trans.apply_transformation(structure) if self.charge_balance_species is not None: cbt = ChargeBalanceTransformation(self.charge_balance_species) dummy_structure = cbt.apply_transformation(dummy_structure) if self.order: trans = OrderDisorderedStructureTransformation() dummy_structure = trans.apply_transformation(dummy_structure) for el in el_list: if charge > 0: sign = "+" else: sign = "-" st = SubstitutionTransformation( {"X{}+".format(str(charge)): "{}{}{}".format(el, charge, sign)} ) new_structure = st.apply_transformation(dummy_structure) outputs.append({"structure": new_structure}) return outputs
def __str__(self): return "Multiple Substitution Transformation : Substitution on " + "{}".format( self.sp_to_replace ) def __repr__(self): return self.__str__() @property def inverse(self): """Returns: None""" return None @property def is_one_to_many(self): """Returns: True""" return True
[docs]class EnumerateStructureTransformation(AbstractTransformation): """ Order a disordered structure using enumlib. For complete orderings, this generally produces fewer structures that the OrderDisorderedStructure transformation, and at a much faster speed. """ def __init__( self, min_cell_size=1, max_cell_size=1, symm_prec=0.1, refine_structure=False, enum_precision_parameter=0.001, check_ordered_symmetry=True, max_disordered_sites=None, sort_criteria="ewald", timeout=None, ): """ Args: min_cell_size: The minimum cell size wanted. Must be an int. Defaults to 1. max_cell_size: The maximum cell size wanted. Must be an int. Defaults to 1. symm_prec: Tolerance to use for symmetry. refine_structure: This parameter has the same meaning as in enumlib_caller. If you are starting from a structure that has been relaxed via some electronic structure code, it is usually much better to start with symmetry determination and then obtain a refined structure. The refined structure have cell parameters and atomic positions shifted to the expected symmetry positions, which makes it much less sensitive precision issues in enumlib. If you are already starting from an experimental cif, refinment should have already been done and it is not necessary. Defaults to False. enum_precision_parameter (float): Finite precision parameter for enumlib. Default of 0.001 is usually ok, but you might need to tweak it for certain cells. check_ordered_symmetry (bool): Whether to check the symmetry of the ordered sites. If the symmetry of the ordered sites is lower, the lowest symmetry ordered sites is included in the enumeration. This is important if the ordered sites break symmetry in a way that is important getting possible structures. But sometimes including ordered sites slows down enumeration to the point that it cannot be completed. Switch to False in those cases. Defaults to True. max_disordered_sites (int): An alternate parameter to max_cell size. Will sequentially try larger and larger cell sizes until (i) getting a result or (ii) the number of disordered sites in the cell exceeds max_disordered_sites. Must set max_cell_size to None when using this parameter. sort_criteria (str): Sort by Ewald energy ("ewald", must have oxidation states and slow) or by number of sites ("nsites", much faster). timeout (float): timeout in minutes to pass to EnumlibAdaptor """ self.symm_prec = symm_prec self.min_cell_size = min_cell_size self.max_cell_size = max_cell_size self.refine_structure = refine_structure self.enum_precision_parameter = enum_precision_parameter self.check_ordered_symmetry = check_ordered_symmetry self.max_disordered_sites = max_disordered_sites self.sort_criteria = sort_criteria self.timeout = timeout if max_cell_size and max_disordered_sites: raise ValueError( "Cannot set both max_cell_size and " "max_disordered_sites!" )
[docs] def apply_transformation(self, structure, return_ranked_list=False): """ Returns either a single ordered structure or a sequence of all ordered structures. Args: structure: Structure to order. return_ranked_list (bool): Whether or not multiple structures are returned. If return_ranked_list is a number, that number of structures is returned. Returns: Depending on returned_ranked list, either a transformed structure or a list of dictionaries, where each dictionary is of the form {"structure" = .... , "other_arguments"} The list of ordered structures is ranked by ewald energy / atom, if the input structure is an oxidation state decorated structure. Otherwise, it is ranked by number of sites, with smallest number of sites first. """ try: num_to_return = int(return_ranked_list) except ValueError: num_to_return = 1 if self.refine_structure: finder = SpacegroupAnalyzer(structure, self.symm_prec) structure = finder.get_refined_structure() contains_oxidation_state = all( [ hasattr(sp, "oxi_state") and sp.oxi_state != 0 for sp in structure.composition.elements ] ) structures = None if structure.is_ordered: warn( "Enumeration skipped for structure with composition {} " "because it is ordered".format(structure.composition) ) structures = [structure.copy()] if self.max_disordered_sites: ndisordered = sum([1 for site in structure if not site.is_ordered]) if ndisordered > self.max_disordered_sites: raise ValueError( "Too many disordered sites! ({} > {})".format( ndisordered, self.max_disordered_sites ) ) max_cell_sizes = range( self.min_cell_size, int(math.floor(self.max_disordered_sites / ndisordered)) + 1, ) else: max_cell_sizes = [self.max_cell_size] for max_cell_size in max_cell_sizes: adaptor = EnumlibAdaptor( structure, min_cell_size=self.min_cell_size, max_cell_size=max_cell_size, symm_prec=self.symm_prec, refine_structure=False, enum_precision_parameter=self.enum_precision_parameter, check_ordered_symmetry=self.check_ordered_symmetry, timeout=self.timeout, ) try: adaptor.run() except EnumError: warn("Unable to enumerate for max_cell_size = {}".format(max_cell_size)) structures = adaptor.structures if structures: break if structures is None: raise ValueError("Unable to enumerate") original_latt = structure.lattice inv_latt = np.linalg.inv(original_latt.matrix) ewald_matrices = {} all_structures = [] for s in structures: new_latt = s.lattice transformation = np.dot(new_latt.matrix, inv_latt) transformation = tuple( [tuple([int(round(cell)) for cell in row]) for row in transformation] ) if contains_oxidation_state and self.sort_criteria == "ewald": if transformation not in ewald_matrices: s_supercell = structure * transformation ewald = EwaldSummation(s_supercell) ewald_matrices[transformation] = ewald else: ewald = ewald_matrices[transformation] energy = ewald.compute_sub_structure(s) all_structures.append( {"num_sites": len(s), "energy": energy, "structure": s} ) else: all_structures.append({"num_sites": len(s), "structure": s}) def sort_func(s): return ( s["energy"] / s["num_sites"] if contains_oxidation_state and self.sort_criteria == "ewald" else s["num_sites"] ) self._all_structures = sorted(all_structures, key=sort_func) if return_ranked_list: return self._all_structures[0:num_to_return] else: return self._all_structures[0]["structure"]
def __str__(self): return "EnumerateStructureTransformation" def __repr__(self): return self.__str__() @property def inverse(self): """Returns: None""" return None @property def is_one_to_many(self): """Returns: True""" return True
[docs]class SubstitutionPredictorTransformation(AbstractTransformation): """ This transformation takes a structure and uses the structure prediction module to find likely site substitutions. """ def __init__(self, threshold=1e-2, scale_volumes=True, **kwargs): r""" Args: threshold: Threshold for substitution. scale_volumes: Whether to scale volumes after substitution. **kwargs: Args for SubstitutionProbability class lambda_table, alpha """ self.kwargs = kwargs self.threshold = threshold self.scale_volumes = scale_volumes self._substitutor = SubstitutionPredictor(threshold=threshold, **kwargs)
[docs] def apply_transformation(self, structure, return_ranked_list=False): """ Applies the transformation. Args: structure: Input Structure return_ranked_list: Number of structures to return. Returns: Predicted Structures. """ if not return_ranked_list: raise ValueError( "SubstitutionPredictorTransformation doesn't" " support returning 1 structure" ) preds = self._substitutor.composition_prediction( structure.composition, to_this_composition=False ) preds.sort(key=lambda x: x["probability"], reverse=True) outputs = [] for pred in preds: st = SubstitutionTransformation(pred["substitutions"]) output = { "structure": st.apply_transformation(structure), "probability": pred["probability"], "threshold": self.threshold, "substitutions": {}, } # dictionary keys have to be converted to strings for JSON for key, value in pred["substitutions"].items(): output["substitutions"][str(key)] = str(value) outputs.append(output) return outputs
def __str__(self): return "SubstitutionPredictorTransformation" def __repr__(self): return self.__str__() @property def inverse(self): """Returns: None""" return None @property def is_one_to_many(self): """Returns: True""" return True
[docs]class MagOrderParameterConstraint(MSONable): """ This class can be used to supply MagOrderingTransformation to just a specific subset of species or sites that satisfy the provided constraints. This can be useful for setting an order parameters for, for example, ferrimagnetic structures which might order on certain motifs, with the global order parameter dependent on how many sites satisfy that motif. """ def __init__( self, order_parameter, species_constraints=None, site_constraint_name=None, site_constraints=None, ): """ :param order_parameter (float): any number from 0.0 to 1.0, typically 0.5 (antiferromagnetic) or 1.0 (ferromagnetic) :param species_constraint (list): str or list of strings of Specie symbols that the constraint should apply to :param site_constraint_name (str): name of the site property that the constraint should apply to, e.g. "coordination_no" :param site_constraints (list): list of values of the site property that the constraints should apply to """ # validation if site_constraints and site_constraints != [None] and not site_constraint_name: raise ValueError("Specify the name of the site constraint.") elif not site_constraints and site_constraint_name: raise ValueError("Please specify some site constraints.") if not isinstance(species_constraints, list): species_constraints = [species_constraints] if not isinstance(site_constraints, list): site_constraints = [site_constraints] if order_parameter > 1 or order_parameter < 0: raise ValueError("Order parameter must lie between 0 and 1") elif order_parameter != 0.5: warnings.warn( "Use care when using a non-standard order parameter, " "though it can be useful in some cases it can also " "lead to unintended behavior. Consult documentation." ) self.order_parameter = order_parameter self.species_constraints = species_constraints self.site_constraint_name = site_constraint_name self.site_constraints = site_constraints
[docs] def satisfies_constraint(self, site): """ Checks if a periodic site satisfies the constraint. """ if not site.is_ordered: return False if self.species_constraints and str(site.specie) in self.species_constraints: satisfies_constraints = True else: satisfies_constraints = False if self.site_constraint_name and self.site_constraint_name in site.properties: prop = site.properties[self.site_constraint_name] if prop in self.site_constraints: satisfies_constraints = True else: satisfies_constraints = False return satisfies_constraints
[docs]class MagOrderingTransformation(AbstractTransformation): """ This transformation takes a structure and returns a list of collinear magnetic orderings. For disordered structures, make an ordered approximation first. """ def __init__( self, mag_species_spin, order_parameter=0.5, energy_model=SymmetryModel(), **kwargs ): """ :param mag_species_spin: A mapping of elements/species to their spin magnitudes, e.g. {"Fe3+": 5, "Mn3+": 4} :param order_parameter (float or list): if float, a specifies a global order parameter and can take values from 0.0 to 1.0 (e.g. 0.5 for antiferromagnetic or 1.0 for ferromagnetic), if list has to be a list of :class: `pymatgen.transformations.advanced_transformations.MagOrderParameterConstraint` to specify more complicated orderings, see documentation for MagOrderParameterConstraint more details on usage :param energy_model: Energy model to rank the returned structures, see :mod: `pymatgen.analysis.energy_models` for more information (note that this is not necessarily a physical energy). By default, returned structures use SymmetryModel() which ranks structures from most symmetric to least. :param kwargs: Additional kwargs that are passed to :class:`EnumerateStructureTransformation` such as min_cell_size etc. """ # checking for sensible order_parameter values if isinstance(order_parameter, float): # convert to constraint format order_parameter = [ MagOrderParameterConstraint( order_parameter=order_parameter, species_constraints=list(mag_species_spin.keys()), ) ] elif isinstance(order_parameter, list): ops = [ isinstance(item, MagOrderParameterConstraint) for item in order_parameter ] if not any(ops): raise ValueError("Order parameter not correctly defined.") else: raise ValueError("Order parameter not correctly defined.") self.mag_species_spin = mag_species_spin # store order parameter constraints as dicts to save implementing # to/from dict methods for MSONable compatibility self.order_parameter = [op.as_dict() for op in order_parameter] self.energy_model = energy_model self.enum_kwargs = kwargs
[docs] @staticmethod def determine_min_cell(disordered_structure): """ Determine the smallest supercell that is able to enumerate the provided structure with the given order parameter """ def lcm(n1, n2): """ Find least common multiple of two numbers """ return n1 * n2 / gcd(n1, n2) # assumes all order parameters for a given species are the same mag_species_order_parameter = {} mag_species_occurrences = {} for idx, site in enumerate(disordered_structure): if not site.is_ordered: op = max(site.species.values()) # this very hacky bit of code only works because we know # that on disordered sites in this class, all species are the same # but have different spins, and this is comma-delimited sp = str(list(site.species.keys())[0]).split(",")[0] if sp in mag_species_order_parameter: mag_species_occurrences[sp] += 1 else: mag_species_order_parameter[sp] = op mag_species_occurrences[sp] = 1 smallest_n = [] for sp, order_parameter in mag_species_order_parameter.items(): denom = Fraction(order_parameter).limit_denominator(100).denominator num_atom_per_specie = mag_species_occurrences[sp] n_gcd = gcd(denom, num_atom_per_specie) smallest_n.append(lcm(int(n_gcd), denom) / n_gcd) return max(smallest_n)
@staticmethod def _add_dummy_species(structure, order_parameters): """ :param structure: ordered Structure :param order_parameters: list of MagOrderParameterConstraints :return: A structure decorated with disordered DummySpecies on which to perform the enumeration. Note that the DummySpecies are super-imposed on to the original sites, to make it easier to retrieve the original site after enumeration is performed (this approach is preferred over a simple mapping since multiple species may have the same DummySpecie, depending on the constraints specified). This approach can also preserve site properties even after enumeration. """ dummy_struct = structure.copy() def generate_dummy_specie(): """ Generator which returns DummySpecie symbols Mma, Mmb, etc. """ subscript_length = 1 while True: for subscript in product(ascii_lowercase, repeat=subscript_length): yield "Mm" + "".join(subscript) subscript_length += 1 dummy_species_gen = generate_dummy_specie() # one dummy species for each order parameter constraint dummy_species_symbols = [ next(dummy_species_gen) for i in range(len(order_parameters)) ] dummy_species = [ { DummySpecie( symbol, properties={"spin": Spin.up} ): constraint.order_parameter, DummySpecie(symbol, properties={"spin": Spin.down}): 1 - constraint.order_parameter, } for symbol, constraint in zip(dummy_species_symbols, order_parameters) ] for idx, site in enumerate(dummy_struct): satisfies_constraints = [ c.satisfies_constraint(site) for c in order_parameters ] if satisfies_constraints.count(True) > 1: # site should either not satisfy any constraints, or satisfy # one constraint raise ValueError( "Order parameter constraints conflict for site: {}, {}".format( str(site.specie), site.properties ) ) elif any(satisfies_constraints): dummy_specie_idx = satisfies_constraints.index(True) dummy_struct.append( dummy_species[dummy_specie_idx], site.coords, site.lattice ) return dummy_struct @staticmethod def _remove_dummy_species(structure): """ :return: Structure with dummy species removed, but their corresponding spin properties merged with the original sites. Used after performing enumeration. """ if not structure.is_ordered: raise Exception("Something went wrong with enumeration.") sites_to_remove = [] logger.debug("Dummy species structure:\n{}".format(str(structure))) for idx, site in enumerate(structure): if isinstance(site.specie, DummySpecie): sites_to_remove.append(idx) spin = site.specie._properties.get("spin", None) neighbors = structure.get_neighbors( site, 0.05, # arbitrary threshold, needs to be << any bond length # but >> floating point precision issues include_index=True, ) if len(neighbors) != 1: raise Exception( "This shouldn't happen, found neighbors: {}".format(neighbors) ) orig_site_idx = neighbors[0][2] orig_specie = structure[orig_site_idx].specie new_specie = Specie( orig_specie.symbol, getattr(orig_specie, "oxi_state", None), properties={"spin": spin}, ) structure.replace( orig_site_idx, new_specie, properties=structure[orig_site_idx].properties, ) structure.remove_sites(sites_to_remove) logger.debug("Structure with dummy species removed:\n{}".format(str(structure))) return structure def _add_spin_magnitudes(self, structure): """ Replaces Spin.up/Spin.down with spin magnitudes specified by mag_species_spin. :param structure: :return: """ for idx, site in enumerate(structure): if getattr(site.specie, "_properties", None): spin = site.specie._properties.get("spin", None) sign = int(spin) if spin else 0 if spin: new_properties = site.specie._properties.copy() # this very hacky bit of code only works because we know # that on disordered sites in this class, all species are the same # but have different spins, and this is comma-delimited sp = str(site.specie).split(",")[0] new_properties.update( {"spin": sign * self.mag_species_spin.get(sp, 0)} ) new_specie = Specie( site.specie.symbol, getattr(site.specie, "oxi_state", None), new_properties, ) structure.replace(idx, new_specie, properties=site.properties) logger.debug("Structure with spin magnitudes:\n{}".format(str(structure))) return structure
[docs] def apply_transformation(self, structure, return_ranked_list=False): """ Apply MagOrderTransformation to an input structure. :param structure: Any ordered structure. :param return_ranked_list: As in other Transformations. :return: """ if not structure.is_ordered: raise ValueError( "Create an ordered approximation of " "your input structure first." ) # retrieve order parameters order_parameters = [ MagOrderParameterConstraint.from_dict(op_dict) for op_dict in self.order_parameter ] # add dummy species on which to perform enumeration structure = self._add_dummy_species(structure, order_parameters) # trivial case if structure.is_ordered: structure = self._remove_dummy_species(structure) return [structure] if return_ranked_list > 1 else structure enum_kwargs = self.enum_kwargs.copy() enum_kwargs["min_cell_size"] = max( int(self.determine_min_cell(structure)), enum_kwargs.get("min_cell_size", 1) ) if enum_kwargs.get("max_cell_size", None): if enum_kwargs["min_cell_size"] > enum_kwargs["max_cell_size"]: warnings.warn( "Specified max cell size ({}) is smaller " "than the minimum enumerable cell size ({}), " "changing max cell size to {}".format( enum_kwargs["max_cell_size"], enum_kwargs["min_cell_size"], enum_kwargs["min_cell_size"], ) ) enum_kwargs["max_cell_size"] = enum_kwargs["min_cell_size"] else: enum_kwargs["max_cell_size"] = enum_kwargs["min_cell_size"] t = EnumerateStructureTransformation(**enum_kwargs) alls = t.apply_transformation(structure, return_ranked_list=return_ranked_list) # handle the fact that EnumerateStructureTransformation can either # return a single Structure or a list if isinstance(alls, Structure): # remove dummy species and replace Spin.up or Spin.down # with spin magnitudes given in mag_species_spin arg alls = self._remove_dummy_species(alls) alls = self._add_spin_magnitudes(alls) else: for idx, _ in enumerate(alls): alls[idx]["structure"] = self._remove_dummy_species( alls[idx]["structure"] ) alls[idx]["structure"] = self._add_spin_magnitudes( alls[idx]["structure"] ) try: num_to_return = int(return_ranked_list) except ValueError: num_to_return = 1 if num_to_return == 1 or not return_ranked_list: return alls[0]["structure"] if num_to_return else alls # remove duplicate structures and group according to energy model m = StructureMatcher(comparator=SpinComparator()) def key(x): return SpacegroupAnalyzer(x, 0.1).get_space_group_number() out = [] for _, g in groupby(sorted([d["structure"] for d in alls], key=key), key): g = list(g) grouped = m.group_structures(g) out.extend( [ {"structure": g[0], "energy": self.energy_model.get_energy(g[0])} for g in grouped ] ) self._all_structures = sorted(out, key=lambda d: d["energy"]) return self._all_structures[0:num_to_return]
def __str__(self): return "MagOrderingTransformation" def __repr__(self): return self.__str__() @property def inverse(self): """Returns: None""" return None @property def is_one_to_many(self): """Returns: True""" return True
def _find_codopant(target, oxidation_state, allowed_elements=None): """ Finds the element from "allowed elements" that (i) possesses the desired "oxidation state" and (ii) is closest in ionic radius to the target specie Args: target: (Specie) provides target ionic radius. oxidation_state: (float) codopant oxidation state. allowed_elements: ([str]) List of allowed elements. If None, all elements are tried. Returns: (Specie) with oxidation_state that has ionic radius closest to target. """ ref_radius = target.ionic_radius candidates = [] symbols = allowed_elements or [el.symbol for el in Element] for sym in symbols: try: with warnings.catch_warnings(): warnings.simplefilter("ignore") sp = Specie(sym, oxidation_state) r = sp.ionic_radius if r is not None: candidates.append((r, sp)) except Exception: pass return min(candidates, key=lambda l: abs(l[0] / ref_radius - 1))[1]
[docs]class DopingTransformation(AbstractTransformation): """ A transformation that performs doping of a structure. """ def __init__( self, dopant, ionic_radius_tol=float("inf"), min_length=10, alio_tol=0, codopant=False, max_structures_per_enum=100, allowed_doping_species=None, **kwargs ): r""" Args: dopant (Specie-like): E.g., Al3+. Must have oxidation state. ionic_radius_tol (float): E.g., Fractional allowable ionic radii mismatch for dopant to fit into a site. Default of inf means that any dopant with the right oxidation state is allowed. min_Length (float): Min. lattice parameter between periodic images of dopant. Defaults to 10A for now. alio_tol (int): If this is not 0, attempt will be made to dope sites with oxidation_states +- alio_tol of the dopant. E.g., 1 means that the ions like Ca2+ and Ti4+ are considered as potential doping sites for Al3+. codopant (bool): If True, doping will be carried out with a codopant to maintain charge neutrality. Otherwise, vacancies will be used. max_structures_per_enum (float): Maximum number of structures to return per enumeration. Note that there can be more than one candidate doping site, and each site enumeration will return at max max_structures_per_enum structures. Defaults to 100. allowed_doping_species (list): Species that are allowed to be doping sites. This is an inclusionary list. If specified, any sites which are not **kwargs: Same keyword args as :class:`EnumerateStructureTransformation`, i.e., min_cell_size, etc. """ self.dopant = get_el_sp(dopant) self.ionic_radius_tol = ionic_radius_tol self.min_length = min_length self.alio_tol = alio_tol self.codopant = codopant self.max_structures_per_enum = max_structures_per_enum self.allowed_doping_species = allowed_doping_species self.kwargs = kwargs
[docs] def apply_transformation(self, structure, return_ranked_list=False): """ Args: structure (Structure): Input structure to dope Returns: [{"structure": Structure, "energy": float}] """ comp = structure.composition logger.info("Composition: %s" % comp) for sp in comp: try: sp.oxi_state except AttributeError: analyzer = BVAnalyzer() structure = analyzer.get_oxi_state_decorated_structure(structure) comp = structure.composition break ox = self.dopant.oxi_state radius = self.dopant.ionic_radius compatible_species = [ sp for sp in comp if sp.oxi_state == ox and abs(sp.ionic_radius / radius - 1) < self.ionic_radius_tol ] if (not compatible_species) and self.alio_tol: # We only consider aliovalent doping if there are no compatible # isovalent species. compatible_species = [ sp for sp in comp if abs(sp.oxi_state - ox) <= self.alio_tol and abs(sp.ionic_radius / radius - 1) < self.ionic_radius_tol and sp.oxi_state * ox >= 0 ] if self.allowed_doping_species is not None: # Only keep allowed doping species. compatible_species = [ sp for sp in compatible_species if sp in [get_el_sp(s) for s in self.allowed_doping_species] ] logger.info("Compatible species: %s" % compatible_species) lengths = structure.lattice.abc scaling = [max(1, int(round(math.ceil(self.min_length / x)))) for x in lengths] logger.info("Lengths are %s" % str(lengths)) logger.info("Scaling = %s" % str(scaling)) all_structures = [] t = EnumerateStructureTransformation(**self.kwargs) for sp in compatible_species: supercell = structure * scaling nsp = supercell.composition[sp] if sp.oxi_state == ox: supercell.replace_species( {sp: {sp: (nsp - 1) / nsp, self.dopant: 1 / nsp}} ) logger.info( "Doping %s for %s at level %.3f" % (sp, self.dopant, 1 / nsp) ) elif self.codopant: codopant = _find_codopant(sp, 2 * sp.oxi_state - ox) supercell.replace_species( {sp: {sp: (nsp - 2) / nsp, self.dopant: 1 / nsp, codopant: 1 / nsp}} ) logger.info( "Doping %s for %s + %s at level %.3f" % (sp, self.dopant, codopant, 1 / nsp) ) elif abs(sp.oxi_state) < abs(ox): # Strategy: replace the target species with a # combination of dopant and vacancy. # We will choose the lowest oxidation state species as a # vacancy compensation species as it is likely to be lower in # energy sp_to_remove = min( [s for s in comp if s.oxi_state * ox > 0], key=lambda ss: abs(ss.oxi_state), ) if sp_to_remove == sp: common_charge = lcm(int(abs(sp.oxi_state)), int(abs(ox))) ndopant = common_charge / abs(ox) nsp_to_remove = common_charge / abs(sp.oxi_state) logger.info( "Doping %d %s with %d %s." % (nsp_to_remove, sp, ndopant, self.dopant) ) supercell.replace_species( { sp: { sp: (nsp - nsp_to_remove) / nsp, self.dopant: ndopant / nsp, } } ) else: ox_diff = int(abs(round(sp.oxi_state - ox))) vac_ox = int(abs(sp_to_remove.oxi_state)) common_charge = lcm(vac_ox, ox_diff) ndopant = common_charge / ox_diff nx_to_remove = common_charge / vac_ox nx = supercell.composition[sp_to_remove] logger.info( "Doping %d %s with %s and removing %d %s." % (ndopant, sp, self.dopant, nx_to_remove, sp_to_remove) ) supercell.replace_species( { sp: {sp: (nsp - ndopant) / nsp, self.dopant: ndopant / nsp}, sp_to_remove: {sp_to_remove: (nx - nx_to_remove) / nx}, } ) elif abs(sp.oxi_state) > abs(ox): # Strategy: replace the target species with dopant and also # remove some opposite charged species for charge neutrality if ox > 0: sp_to_remove = max( supercell.composition.keys(), key=lambda el: el.X ) else: sp_to_remove = min( supercell.composition.keys(), key=lambda el: el.X ) # Confirm species are of opposite oxidation states. assert sp_to_remove.oxi_state * sp.oxi_state < 0 ox_diff = int(abs(round(sp.oxi_state - ox))) anion_ox = int(abs(sp_to_remove.oxi_state)) nx = supercell.composition[sp_to_remove] common_charge = lcm(anion_ox, ox_diff) ndopant = common_charge / ox_diff nx_to_remove = common_charge / anion_ox logger.info( "Doping %d %s with %s and removing %d %s." % (ndopant, sp, self.dopant, nx_to_remove, sp_to_remove) ) supercell.replace_species( { sp: {sp: (nsp - ndopant) / nsp, self.dopant: ndopant / nsp}, sp_to_remove: {sp_to_remove: (nx - nx_to_remove) / nx}, } ) ss = t.apply_transformation( supercell, return_ranked_list=self.max_structures_per_enum ) logger.info("%s distinct structures" % len(ss)) all_structures.extend(ss) logger.info("Total %s doped structures" % len(all_structures)) if return_ranked_list: return all_structures[:return_ranked_list] return all_structures[0]["structure"]
@property def inverse(self): """Returns: None""" return None @property def is_one_to_many(self): """Returns: True""" return True
[docs]class SlabTransformation(AbstractTransformation): """ A transformation that creates a slab from a structure. """ def __init__( self, miller_index, min_slab_size, min_vacuum_size, lll_reduce=False, center_slab=False, in_unit_planes=False, primitive=True, max_normal_search=None, shift=0, tol=0.1, ): """ Args: miller_index (3-tuple or list): miller index of slab min_slab_size (float): minimum slab size in angstroms min_vacuum_size (float): minimum size of vacuum lll_reduce (bool): whether to apply LLL reduction center_slab (bool): whether to center the slab primitive (bool): whether to reduce slabs to most primitive cell max_normal_search (int): maximum index to include in linear combinations of indices to find c lattice vector orthogonal to slab surface shift (float): shift to get termination tol (float): tolerance for primitive cell finding """ self.miller_index = miller_index self.min_slab_size = min_slab_size self.min_vacuum_size = min_vacuum_size self.lll_reduce = lll_reduce self.center_slab = center_slab self.in_unit_planes = in_unit_planes self.primitive = primitive self.max_normal_search = max_normal_search self.shift = shift self.tol = tol
[docs] def apply_transformation(self, structure): """ Applies the transformation. Args: structure: Input Structure Returns: Slab Structures. """ sg = SlabGenerator( structure, self.miller_index, self.min_slab_size, self.min_vacuum_size, self.lll_reduce, self.center_slab, self.in_unit_planes, self.primitive, self.max_normal_search, ) slab = sg.get_slab(self.shift, self.tol) return slab
@property def inverse(self): """Returns: None""" return None @property def is_one_to_many(self): """Returns: False""" return False
[docs]class DisorderOrderedTransformation(AbstractTransformation): """ Not to be confused with OrderDisorderedTransformation, this transformation attempts to obtain a *disordered* structure from an input ordered structure. This may or may not be physically plausible, further inspection of the returned structures is advised. The main purpose for this transformation is for structure matching to crystal prototypes for structures that have been derived from a parent prototype structure by substitutions or alloying additions. """ def __init__(self, max_sites_to_merge=2): """ Args: max_sites_to_merge: only merge this number of sites together """ self.max_sites_to_merge = max_sites_to_merge
[docs] def apply_transformation(self, structure, return_ranked_list=False): """ Args: structure: ordered structure return_ranked_list: as in other pymatgen Transformations Returns: Transformed disordered structure(s) """ if not structure.is_ordered: raise ValueError("This transformation is for disordered structures only.") partitions = self._partition_species( structure.composition, max_components=self.max_sites_to_merge ) disorder_mappings = self._get_disorder_mappings( structure.composition, partitions ) disordered_structures = [] for mapping in disorder_mappings: disordered_structure = structure.copy() disordered_structure.replace_species(mapping) disordered_structures.append( {"structure": disordered_structure, "mapping": mapping} ) if len(disordered_structures) == 0: return None elif not return_ranked_list: return disordered_structures[0]["structure"] else: if len(disordered_structures) > return_ranked_list: disordered_structures = disordered_structures[0:return_ranked_list] return disordered_structures
@property def inverse(self): """Returns: None""" return None @property def is_one_to_many(self): """Returns: True""" return True @staticmethod def _partition_species(composition, max_components=2): """ Private method to split a list of species into various partitions. """ def _partition(collection): # thanks https://stackoverflow.com/a/30134039 if len(collection) == 1: yield [collection] return first = collection[0] for smaller in _partition(collection[1:]): # insert `first` in each of the subpartition's subsets for n, subset in enumerate(smaller): yield smaller[:n] + [[first] + subset] + smaller[n + 1:] # put `first` in its own subset yield [[first]] + smaller def _sort_partitions(partitions_to_sort): """ Sort partitions by those we want to check first (typically, merging two sites into one is the one to try first). """ partition_indices = [ (idx, [len(p) for p in partition]) for idx, partition in enumerate(partitions_to_sort) ] # sort by maximum length of partition first (try smallest maximums first) # and secondarily by number of partitions (most partitions first, i.e. # create the 'least disordered' structures first) partition_indices = sorted( partition_indices, key=lambda x: (max(x[1]), -len(x[1])) ) # merge at most max_component sites, # e.g. merge at most 2 species into 1 disordered site partition_indices = [ x for x in partition_indices if max(x[1]) <= max_components ] partition_indices.pop(0) # this is just the input structure sorted_partitions = [partitions_to_sort[x[0]] for x in partition_indices] return sorted_partitions collection = list(composition.keys()) partitions = list(_partition(collection)) partitions = _sort_partitions(partitions) return partitions @staticmethod def _get_disorder_mappings(composition, partitions): """ Private method to obtain the mapping to create a disordered structure from a given partition. """ def _get_replacement_dict_from_partition(partition): d = {} # to be passed to Structure.replace_species() for sp_list in partition: if len(sp_list) > 1: total_occ = sum([composition[sp] for sp in sp_list]) merged_comp = {sp: composition[sp] / total_occ for sp in sp_list} for sp in sp_list: d[sp] = merged_comp return d disorder_mapping = [_get_replacement_dict_from_partition(p) for p in partitions] return disorder_mapping
[docs]class GrainBoundaryTransformation(AbstractTransformation): """ A transformation that creates a gb from a bulk structure. """ def __init__( self, rotation_axis, rotation_angle, expand_times=4, vacuum_thickness=0.0, ab_shift=None, normal=False, ratio=True, plane=None, max_search=20, tol_coi=1.0e-8, rm_ratio=0.7, quick_gen=False, ): """ Args: rotation_axis (list): Rotation axis of GB in the form of a list of integer e.g.: [1, 1, 0] rotation_angle (float, in unit of degree): rotation angle used to generate GB. Make sure the angle is accurate enough. You can use the enum* functions in this class to extract the accurate angle. e.g.: The rotation angle of sigma 3 twist GB with the rotation axis [1, 1, 1] and GB plane (1, 1, 1) can be 60.000000000 degree. If you do not know the rotation angle, but know the sigma value, we have provide the function get_rotation_angle_from_sigma which is able to return all the rotation angles of sigma value you provided. expand_times (int): The multiple times used to expand one unit grain to larger grain. This is used to tune the grain length of GB to warrant that the two GBs in one cell do not interact with each other. Default set to 4. vacuum_thickness (float): The thickness of vacuum that you want to insert between two grains of the GB. Default to 0. ab_shift (list of float, in unit of a, b vectors of Gb): in plane shift of two grains normal (logic): determine if need to require the c axis of top grain (first transformation matrix) perperdicular to the surface or not. default to false. ratio (list of integers): lattice axial ratio. If True, will try to determine automatically from structure. For cubic system, ratio is not needed and can be set to None. For tetragonal system, ratio = [mu, mv], list of two integers, that is, mu/mv = c2/a2. If it is irrational, set it to None. For orthorhombic system, ratio = [mu, lam, mv], list of three integers, that is, mu:lam:mv = c2:b2:a2. If irrational for one axis, set it to None. e.g. mu:lam:mv = c2,None,a2, means b2 is irrational. For rhombohedral system, ratio = [mu, mv], list of two integers, that is, mu/mv is the ratio of (1+2*cos(alpha))/cos(alpha). If irrational, set it to None. For hexagonal system, ratio = [mu, mv], list of two integers, that is, mu/mv = c2/a2. If it is irrational, set it to none. plane (list): Grain boundary plane in the form of a list of integers e.g.: [1, 2, 3]. If none, we set it as twist GB. The plane will be perpendicular to the rotation axis. max_search (int): max search for the GB lattice vectors that give the smallest GB lattice. If normal is true, also max search the GB c vector that perpendicular to the plane. For complex GB, if you want to speed up, you can reduce this value. But too small of this value may lead to error. tol_coi (float): tolerance to find the coincidence sites. When making approximations to the ratio needed to generate the GB, you probably need to increase this tolerance to obtain the correct number of coincidence sites. To check the number of coincidence sites are correct or not, you can compare the generated Gb object's sigma with enum* sigma values (what user expected by input). rm_ratio (float): the criteria to remove the atoms which are too close with each other. rm_ratio * bond_length of bulk system is the criteria of bond length, below which the atom will be removed. Default to 0.7. quick_gen (bool): whether to quickly generate a supercell, if set to true, no need to find the smallest cell. Returns: Grain boundary structure (gb (Structure) object). """ self.rotation_axis = rotation_axis self.rotation_angle = rotation_angle self.expand_times = expand_times self.vacuum_thickness = vacuum_thickness self.ab_shift = ab_shift or [0, 0] self.normal = normal self.ratio = ratio self.plane = plane self.max_search = max_search self.tol_coi = tol_coi self.rm_ratio = rm_ratio self.quick_gen = quick_gen
[docs] def apply_transformation(self, structure): """ Applies the transformation. Args: structure: Input Structure return_ranked_list: Number of structures to return. Returns: Grain boundary Structures. """ gbg = GrainBoundaryGenerator(structure) gb_struct = gbg.gb_from_parameters( self.rotation_axis, self.rotation_angle, expand_times=self.expand_times, vacuum_thickness=self.vacuum_thickness, ab_shift=self.ab_shift, normal=self.normal, ratio=gbg.get_ratio() if self.ratio is True else self.ratio, plane=self.plane, max_search=self.max_search, tol_coi=self.tol_coi, rm_ratio=self.rm_ratio, quick_gen=self.quick_gen, ) return gb_struct
@property def inverse(self): """Returns: None""" return None @property def is_one_to_many(self): """Returns: False""" return False
[docs]class CubicSupercellTransformation(AbstractTransformation): """ A transformation that aims to generate a nearly cubic supercell structure from a structure. The algorithm solves for a transformation matrix that makes the supercell cubic. The matrix must have integer entries, so entries are rounded (in such a way that forces the matrix to be nonsingular). From the supercell resulting from this transformation matrix, vector projections are used to determine the side length of the largest cube that can fit inside the supercell. The algorithm will iteratively increase the size of the supercell until the largest inscribed cube's side length is at least 'min_length' and the number of atoms in the supercell falls in the range ``min_atoms < n < max_atoms``. """ def __init__( self, min_atoms: Optional[int] = None, max_atoms: Optional[int] = None, min_length: float = 15.0, force_diagonal: bool = False, ): """ Args: max_atoms: Maximum number of atoms allowed in the supercell. min_atoms: Minimum number of atoms allowed in the supercell. min_length: Minimum length of the smallest supercell lattice vector. force_diagonal: If True, return a transformation with a diagonal transformation matrix. """ self.min_atoms = min_atoms if min_atoms else -np.Inf self.max_atoms = max_atoms if max_atoms else np.Inf self.min_length = min_length self.force_diagonal = force_diagonal self.transformation_matrix = None
[docs] def apply_transformation(self, structure: Structure) -> Structure: """ The algorithm solves for a transformation matrix that makes the supercell cubic. The matrix must have integer entries, so entries are rounded (in such a way that forces the matrix to be nonsingular). From the supercell resulting from this transformation matrix, vector projections are used to determine the side length of the largest cube that can fit inside the supercell. The algorithm will iteratively increase the size of the supercell until the largest inscribed cube's side length is at least 'num_nn_dists' times the nearest neighbor distance and the number of atoms in the supercell falls in the range defined by min_atoms and max_atoms. Returns: supercell: Transformed supercell. """ lat_vecs = structure.lattice.matrix # boolean for if a sufficiently large supercell has been created sc_not_found = True if self.force_diagonal: scale = self.min_length / np.array(structure.lattice.abc) self.transformation_matrix = np.diag(np.ceil(scale).astype(int)) st = SupercellTransformation(self.transformation_matrix) return st.apply_transformation(structure) # target_threshold is used as the desired cubic side lengths target_sc_size = self.min_length while sc_not_found: target_sc_lat_vecs = np.eye(3, 3) * target_sc_size self.transformation_matrix = target_sc_lat_vecs @ np.linalg.inv(lat_vecs) # round the entries of T and force T to be nonsingular self.transformation_matrix = _round_and_make_arr_singular( self.transformation_matrix ) proposed_sc_lat_vecs = self.transformation_matrix @ lat_vecs # Find the shortest dimension length and direction a = proposed_sc_lat_vecs[0] b = proposed_sc_lat_vecs[1] c = proposed_sc_lat_vecs[2] length1_vec = c - _proj(c, a) # a-c plane length2_vec = a - _proj(a, c) length3_vec = b - _proj(b, a) # b-a plane length4_vec = a - _proj(a, b) length5_vec = b - _proj(b, c) # b-c plane length6_vec = c - _proj(c, b) length_vecs = np.array( [ length1_vec, length2_vec, length3_vec, length4_vec, length5_vec, length6_vec, ] ) # Get number of atoms st = SupercellTransformation(self.transformation_matrix) superstructure = st.apply_transformation(structure) num_at = superstructure.num_sites # Check if constraints are satisfied if ( np.min(np.linalg.norm(length_vecs, axis=1)) >= self.min_length and self.min_atoms <= num_at <= self.max_atoms ): return superstructure else: # Increase threshold until proposed supercell meets requirements target_sc_size += 0.1 if num_at > self.max_atoms: raise AttributeError( "While trying to solve for the supercell, the max " "number of atoms was exceeded. Try lowering the number" "of nearest neighbor distances." ) raise AttributeError("Unable to find cubic supercell")
@property def inverse(self): """ Returns: None """ return None @property def is_one_to_many(self): """ Returns: False """ return False
[docs]class AddAdsorbateTransformation(AbstractTransformation): """ Create absorbate structures. """ def __init__( self, adsorbate, selective_dynamics=False, height=0.9, mi_vec=None, repeat=None, min_lw=5.0, translate=True, reorient=True, find_args=None, ): """ Use AdsorbateSiteFinder to add an absorbate to a slab. Args: adsorbate (Molecule): molecule to add as adsorbate selective_dynamics (bool): flag for whether to assign non-surface sites as fixed for selective dynamics height (float): height criteria for selection of surface sites mi_vec : vector corresponding to the vector concurrent with the miller index, this enables use with slabs that have been reoriented, but the miller vector must be supplied manually repeat (3-tuple or list): repeat argument for supercell generation min_lw (float): minimum length and width of the slab, only used if repeat is None translate (bool): flag on whether to translate the molecule so that its CoM is at the origin prior to adding it to the surface reorient (bool): flag on whether or not to reorient adsorbate along the miller index find_args (dict): dictionary of arguments to be passed to the call to self.find_adsorption_sites, e.g. {"distance":2.0} """ self.adsorbate = adsorbate self.selective_dynamics = selective_dynamics self.height = height self.mi_vec = mi_vec self.repeat = repeat self.min_lw = min_lw self.translate = translate self.reorient = reorient self.find_args = find_args
[docs] def apply_transformation(self, structure, return_ranked_list=False): """ Args: structure: Must be a Slab structure return_ranked_list: Whether or not multiple structures are returned. If return_ranked_list is a number, up to that number of structures is returned. Returns: Slab with adsorbate """ sitefinder = AdsorbateSiteFinder( structure, selective_dynamics=self.selective_dynamics, height=self.height, mi_vec=self.mi_vec, ) structures = sitefinder.generate_adsorption_structures( self.adsorbate, repeat=self.repeat, min_lw=self.min_lw, translate=self.translate, reorient=self.reorient, find_args=self.find_args, ) if not return_ranked_list: return structures[0] else: return [ {"structure": structure} for structure in structures[:return_ranked_list] ]
@property def inverse(self): """Returns: None""" return None @property def is_one_to_many(self): """Returns: True""" return True
def _round_and_make_arr_singular(arr: np.ndarray) -> np.ndarray: """ This function rounds all elements of a matrix to the nearest integer, unless the rounding scheme causes the matrix to be singular, in which case elements of zero rows or columns in the rounded matrix with the largest absolute valued magnitude in the unrounded matrix will be rounded to the next integer away from zero rather than to the nearest integer. The transformation is as follows. First, all entries in 'arr' will be rounded to the nearest integer to yield 'arr_rounded'. If 'arr_rounded' has any zero rows, then one element in each zero row of 'arr_rounded' corresponding to the element in 'arr' of that row with the largest absolute valued magnitude will be rounded to the next integer away from zero (see the '_round_away_from_zero(x)' function) rather than the nearest integer. This process is then repeated for zero columns. Also note that if 'arr' already has zero rows or columns, then this function will not change those rows/columns. Args: arr: Input matrix Returns: Transformed matrix. """ def round_away_from_zero(x): """ Returns 'x' rounded to the next integer away from 0. If 'x' is zero, then returns zero. E.g. -1.2 rounds to -2.0. 1.2 rounds to 2.0. """ abs_x = abs(x) return math.ceil(abs_x) * (abs_x / x) if x != 0 else 0 arr_rounded = np.around(arr) # Zero rows in 'arr_rounded' make the array singular, so force zero rows to # be nonzero if (~arr_rounded.any(axis=1)).any(): # Check for zero rows in T_rounded # indices of zero rows zero_row_idxs = np.where(~arr_rounded.any(axis=1))[0] for zero_row_idx in zero_row_idxs: # loop over zero rows zero_row = arr[zero_row_idx, :] # Find the element of the zero row with the largest absolute # magnitude in the original (non-rounded) array (i.e. 'arr') matches = np.absolute(zero_row) == np.amax(np.absolute(zero_row)) col_idx_to_fix = np.where(matches)[0] # Break ties for the largest absolute magnitude r_idx = np.random.randint(len(col_idx_to_fix)) col_idx_to_fix = col_idx_to_fix[r_idx] # Round the chosen element away from zero arr_rounded[zero_row_idx, col_idx_to_fix] = round_away_from_zero( arr[zero_row_idx, col_idx_to_fix] ) # Repeat process for zero columns if (~arr_rounded.any(axis=0)).any(): # Check for zero columns in T_rounded zero_col_idxs = np.where(~arr_rounded.any(axis=0))[0] for zero_col_idx in zero_col_idxs: zero_col = arr[:, zero_col_idx] matches = np.absolute(zero_col) == np.amax(np.absolute(zero_col)) row_idx_to_fix = np.where(matches)[0] for i in row_idx_to_fix: arr_rounded[i, zero_col_idx] = round_away_from_zero( arr[i, zero_col_idx] ) return arr_rounded.astype(int)
[docs]class SubstituteSurfaceSiteTransformation(AbstractTransformation): """ Use AdsorptionSiteFinder to perform substitution-type doping on the surface and returns all possible configurations where one dopant is substituted per surface. Can substitute one surface or both. """ def __init__( self, atom, selective_dynamics=False, height=0.9, mi_vec=None, target_species=None, sub_both_sides=False, range_tol=1e-2, dist_from_surf=0, ): """ Args: atom (str): atom corresponding to substitutional dopant selective_dynamics (bool): flag for whether to assign non-surface sites as fixed for selective dynamics height (float): height criteria for selection of surface sites mi_vec : vector corresponding to the vector concurrent with the miller index, this enables use with slabs that have been reoriented, but the miller vector must be supplied manually target_species: List of specific species to substitute sub_both_sides (bool): If true, substitute an equivalent site on the other surface range_tol (float): Find viable substitution sites at a specific distance from the surface +- this tolerance dist_from_surf (float): Distance from the surface to find viable substitution sites, defaults to 0 to substitute at the surface """ self.atom = atom self.selective_dynamics = selective_dynamics self.height = height self.mi_vec = mi_vec self.target_species = target_species self.sub_both_sides = sub_both_sides self.range_tol = range_tol self.dist_from_surf = dist_from_surf
[docs] def apply_transformation(self, structure, return_ranked_list=False): """ Args: structure: Must be a Slab structure return_ranked_list: Whether or not multiple structures are returned. If return_ranked_list is a number, up to that number of structures is returned. Returns: Slab with sites substituted """ sitefinder = AdsorbateSiteFinder( structure, selective_dynamics=self.selective_dynamics, height=self.height, mi_vec=self.mi_vec, ) structures = sitefinder.generate_substitution_structures( self.atom, target_species=self.target_species, sub_both_sides=self.sub_both_sides, range_tol=self.range_tol, dist_from_surf=self.dist_from_surf, ) if not return_ranked_list: return structures[0] else: return [ {"structure": structure} for structure in structures[:return_ranked_list] ]
@property def inverse(self): """Returns: None""" return None @property def is_one_to_many(self): """Returns: True""" return True
def _proj(b, a): """ Returns vector projection (np.ndarray) of vector b (np.ndarray) onto vector a (np.ndarray) """ return (b.T @ (a / np.linalg.norm(a))) * (a / np.linalg.norm(a))
[docs]class SQSTransformation(AbstractTransformation): """ A transformation that creates a special quasirandom structure (SQS) from a structure with partial occupancies. """ def __init__( self, scaling, cluster_size_and_shell=None, search_time=60, directory=None, instances=None, temperature=1, wr=1, wn=1, wd=0.5, tol=1e-3, best_only=True, remove_duplicate_structures=True, reduction_algo="LLL", ): """ Args: structure (Structure): Disordered pymatgen Structure object scaling (int or list): Scaling factor to determine supercell. Two options are possible: a. (preferred) Scales number of atoms, e.g., for a structure with 8 atoms, scaling=4 would lead to a 32 atom supercell b. A sequence of three scaling factors, e.g., [2, 1, 1], which specifies that the supercell should have dimensions 2a x b x c cluster_size_and_shell (Optional[Dict[int, int]]): Dictionary of cluster interactions with entries in the form number of atoms: nearest neighbor shell Keyword Args: search_time (float): Time spent looking for the ideal SQS in minutes (default: 60) directory (str): Directory to run mcsqs calculation and store files (default: None runs calculations in a temp directory) instances (int): Specifies the number of parallel instances of mcsqs to run (default: number of cpu cores detected by Python) temperature (int or float): Monte Carlo temperature (default: 1), "T" in atat code wr (int or float): Weight assigned to range of perfect correlation match in objective function (default = 1) wn (int or float): Multiplicative decrease in weight per additional point in cluster (default: 1) wd (int or float): Exponent of decay in weight as function of cluster diameter (default: 0) tol (int or float): Tolerance for matching correlations (default: 1e-3) best_only (bool): only return structures with lowest objective function remove_duplicate_structures (bool): only return unique structures reduction_algo (str): The lattice reduction algorithm to use. Currently supported options are "niggli" or "LLL". "False" does not reduce structure. """ self.scaling = scaling self.search_time = search_time self.cluster_size_and_shell = cluster_size_and_shell self.directory = directory self.instances = instances self.temperature = temperature self.wr = wr self.wn = wn self.wd = wd self.tol = tol self.best_only = best_only self.remove_duplicate_structures = remove_duplicate_structures self.reduction_algo = reduction_algo @staticmethod def _get_max_neighbor_distance(struc, shell): """ Calculate maximum nearest neighbor distance Args: struc: pymatgen Structure object shell: nearest neighbor shell, such that shell=1 is the first nearest neighbor, etc. Returns: maximum nearest neighbor distance, in angstroms """ mdnn = MinimumDistanceNN() distances = [] for site_num, site in enumerate(struc): shell_info = mdnn.get_nn_shell_info(struc, site_num, shell) for entry in shell_info: image = entry["image"] distance = site.distance(struc[entry["site_index"]], jimage=image) distances.append(distance) return max(distances) @staticmethod def _get_disordered_substructure(struc_disordered): """ Converts disordered structure into a substructure consisting of only disordered sites Args: struc_disordered: pymatgen disordered Structure object Returns: pymatgen Structure object representing a substructure of disordered sites """ disordered_substructure = struc_disordered.copy() idx_to_remove = [] for idx, site in enumerate(disordered_substructure.sites): if site.is_ordered: idx_to_remove.append(idx) disordered_substructure.remove_sites(idx_to_remove) return disordered_substructure @staticmethod def _sqs_cluster_estimate( struc_disordered, cluster_size_and_shell: Optional[Dict[int, int]] = None ): """ Set up an ATAT cluster.out file for a given structure and set of constraints Args: struc_disordered: disordered pymatgen Structure object cluster_size_and_shell: dict of integers {cluster: shell} Returns: dict of {cluster size: distance in angstroms} for mcsqs calculation """ cluster_size_and_shell = cluster_size_and_shell or {2: 3, 3: 2, 4: 1} disordered_substructure = SQSTransformation._get_disordered_substructure( struc_disordered ) clusters = {} for cluster_size, shell in cluster_size_and_shell.items(): max_distance = SQSTransformation._get_max_neighbor_distance( disordered_substructure, shell ) clusters[cluster_size] = max_distance + 0.01 # add small tolerance return clusters
[docs] def apply_transformation(self, structure, return_ranked_list=False): """ Applies SQS transformation Args: structure (pymatgen Structure): pymatgen Structure with partial occupancies return_ranked_list (bool): number of structures to return Returns: pymatgen Structure which is an SQS of the input structure """ if return_ranked_list and self.instances is None: raise ValueError("mcsqs has no instances, so cannot return a ranked list") if ( isinstance(return_ranked_list, int) and isinstance(self.instances, int) and return_ranked_list > self.instances ): raise ValueError( "return_ranked_list cannot be less that number of instances" ) clusters = self._sqs_cluster_estimate(structure, self.cluster_size_and_shell) # useful for debugging and understanding self._last_used_clusters = clusters sqs = run_mcsqs( structure=structure, clusters=clusters, scaling=self.scaling, search_time=self.search_time, directory=self.directory, instances=self.instances, temperature=self.temperature, wr=self.wr, wn=self.wn, wd=self.wd, tol=self.tol, ) return self._get_unique_bestsqs_strucs( sqs, best_only=self.best_only, return_ranked_list=return_ranked_list, remove_duplicate_structures=self.remove_duplicate_structures, reduction_algo=self.reduction_algo, )
@staticmethod def _get_unique_bestsqs_strucs( sqs, best_only, return_ranked_list, remove_duplicate_structures, reduction_algo ): """ Gets unique sqs structures with lowest objective function. Requires an mcsqs output that has been run in parallel, otherwise returns Sqs.bestsqs Args: sqs (Sqs): Sqs class object. best_only (bool): only return structures with lowest objective function. return_ranked_list (bool): Number of structures to return. remove_duplicate_structures (bool): only return unique structures. reduction_algo (str): The lattice reduction algorithm to use. Currently supported options are "niggli" or "LLL". "False" does not reduce structure. Returns: list of dicts of the form {'structure': Structure, 'objective_function': ...}, unless run in serial (returns a single structure Sqs.bestsqs) """ if not return_ranked_list: # return just the structure return sqs.bestsqs else: strucs = [] for d in sqs.allsqs: # filter for best structures only if enabled, else use full sqs.all_sqs list if (not best_only) or ( best_only and d["objective_function"] == sqs.objective_function ): struc = d["structure"] # add temporary objective_function attribute to access objective_function after grouping struc.objective_function = d["objective_function"] strucs.append(struc) if remove_duplicate_structures: matcher = StructureMatcher() # sort by unique structures ... can take a while for a long list of strucs unique_strucs_grouped = matcher.group_structures(strucs) # get unique structures only strucs = [group[0] for group in unique_strucs_grouped] # sort structures by objective function strucs.sort( key=lambda x: x.objective_function if isinstance(x.objective_function, float) else -np.inf ) to_return = [ {"structure": struc, "objective_function": struc.objective_function} for struc in strucs ] for d in to_return: # delete temporary objective_function attribute del d["structure"].objective_function # reduce structure if reduction_algo: d["structure"] = d["structure"].get_reduced_structure( reduction_algo=reduction_algo ) if isinstance(return_ranked_list, int): return to_return[:return_ranked_list] else: return to_return @property def inverse(self): """Returns: None""" return None @property def is_one_to_many(self): """Returns: True""" return True
[docs]class MonteCarloRattleTransformation(AbstractTransformation): r""" Uses a Monte Carlo rattle procedure to randomly perturb the sites in a structure. This class requires the hiPhive package to be installed. Rattling atom `i` is carried out as a Monte Carlo move that is accepted with a probability determined from the minimum interatomic distance :math:`d_{ij}`. If :math:`\\min(d_{ij})` is smaller than :math:`d_{min}` the move is only accepted with a low probability. This process is repeated for each atom a number of times meaning the magnitude of the final displacements is not *directly* connected to `rattle_std`. """ @requires(hiphive, "hiphive is required for MonteCarloRattleTransformation") def __init__( self, rattle_std: float, min_distance: float, seed: Optional[int] = None, **kwargs ): """ Args: rattle_std: Rattle amplitude (standard deviation in normal distribution). Note: this value is not *directly* connected to the final average displacement for the structures min_distance: Interatomic distance used for computing the probability for each rattle move. seed: Seed for setting up NumPy random state from which random numbers are generated. If ``None``, a random seed will be generated (default). This option allows the output of this transformation to be deterministic. **kwargs: Additional keyword arguments to be passed to the hiPhive mc_rattle function. """ self.rattle_std = rattle_std self.min_distance = min_distance self.seed = seed if not seed: # if seed is None, use a random RandomState seed but make sure # we store that the original seed was None seed = np.random.randint(1, 1000000000) self.random_state = np.random.RandomState(seed) self.kwargs = kwargs
[docs] def apply_transformation(self, structure: Structure) -> Structure: """ Apply the transformation. Args: structure: Input Structure Returns: Structure with sites perturbed. """ from hiphive.structure_generation.rattle import ( # type: ignore mc_rattle, ) atoms = AseAtomsAdaptor.get_atoms(structure) seed = self.random_state.randint(1, 1000000000) displacements = mc_rattle( atoms, self.rattle_std, self.min_distance, seed=seed, **self.kwargs ) transformed_structure = Structure( structure.lattice, structure.species, structure.cart_coords + displacements, coords_are_cartesian=True, ) return transformed_structure
def __str__(self): return "{} : rattle_std = {}".format(__name__, self.rattle_std) def __repr__(self): return self.__str__() @property def inverse(self): """ Returns: None """ return None @property def is_one_to_many(self): """ Returns: False """ return False