Source code for pymatgen.transformations.site_transformations

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

"""
This module defines site transformations which transforms a structure into
another structure. Site transformations differ from standard transformations
in that they operate in a site-specific manner.
All transformations should inherit the AbstractTransformation ABC.
"""

import math
import itertools
import logging
import time

from monty.json import MSONable

import numpy as np

from pymatgen.symmetry.analyzer import SpacegroupAnalyzer
from pymatgen.transformations.transformation_abc import AbstractTransformation
from pymatgen.analysis.ewald import EwaldSummation, EwaldMinimizer

__author__ = "Shyue Ping Ong, Will Richards"
__copyright__ = "Copyright 2011, The Materials Project"
__version__ = "1.2"
__maintainer__ = "Shyue Ping Ong"
__email__ = "shyuep@gmail.com"
__date__ = "Sep 23, 2011"


[docs]class InsertSitesTransformation(AbstractTransformation): """ This transformation substitutes certain sites with certain species. """ def __init__(self, species, coords, coords_are_cartesian=False, validate_proximity=True): """ Args: species: A list of species. e.g., ["Li", "Fe"] coords: A list of coords corresponding to those species. e.g., [[0,0,0],[0.5,0.5,0.5]]. coords_are_cartesian (bool): Set to True if coords are given in cartesian coords. Defaults to False. validate_proximity (bool): Set to False if you do not wish to ensure that added sites are not too close to other sites. Defaults to True. """ if len(species) != len(coords): raise ValueError("Species and coords must be the same length!") self.species = species self.coords = coords self.coords_are_cartesian = coords_are_cartesian self.validate_proximity = validate_proximity
[docs] def apply_transformation(self, structure): """ Apply the transformation. Arg: structure (Structure): A structurally similar structure in regards to crystal and site positions. Return: Returns a copy of structure with sites inserted. """ s = structure.copy() for i, sp in enumerate(self.species): s.insert(i, sp, self.coords[i], coords_are_cartesian=self.coords_are_cartesian, validate_proximity=self.validate_proximity) return s.get_sorted_structure()
def __str__(self): return "InsertSiteTransformation : " + \ "species {}, coords {}".format(self.species, self.coords) def __repr__(self): return self.__str__() @property def inverse(self): """Return: None""" return None @property def is_one_to_many(self): """Return: False""" return False
[docs]class ReplaceSiteSpeciesTransformation(AbstractTransformation): """ This transformation substitutes certain sites with certain species. """ def __init__(self, indices_species_map): """ Args: indices_species_map: A dict containing the species mapping in int-string pairs. E.g., { 1:"Na"} or {2:"Mn2+"}. Multiple substitutions can be done. Overloaded to accept sp_and_occu dictionary. E.g. {1: {"Ge":0.75, "C":0.25} }, which substitutes a single species with multiple species to generate a disordered structure. """ self.indices_species_map = indices_species_map
[docs] def apply_transformation(self, structure): """ Apply the transformation. Arg: structure (Structure): A structurally similar structure in regards to crystal and site positions. Return: Returns a copy of structure with sites replaced. """ s = structure.copy() for i, sp in self.indices_species_map.items(): s[int(i)] = sp return s
def __str__(self): return "ReplaceSiteSpeciesTransformation :" + \ ", ".join(["{}->{}".format(k, v) + v for k, v in self.indices_species_map.items()]) def __repr__(self): return self.__str__() @property def inverse(self): """Return: None""" return None @property def is_one_to_many(self): """Return: False""" return False
[docs]class RemoveSitesTransformation(AbstractTransformation): """ Remove certain sites in a structure. """ def __init__(self, indices_to_remove): """ Args: indices_to_remove: List of indices to remove. E.g., [0, 1, 2] """ self.indices_to_remove = indices_to_remove
[docs] def apply_transformation(self, structure): """ Apply the transformation. Arg: structure (Structure): A structurally similar structure in regards to crystal and site positions. Return: Returns a copy of structure with sites removed. """ s = structure.copy() s.remove_sites(self.indices_to_remove) return s
def __str__(self): return "RemoveSitesTransformation :" + ", ".join( map(str, self.indices_to_remove)) def __repr__(self): return self.__str__() @property def inverse(self): """Return: None""" return None @property def is_one_to_many(self): """Return: False""" return False
[docs]class TranslateSitesTransformation(AbstractTransformation): """ This class translates a set of sites by a certain vector. """ def __init__(self, indices_to_move, translation_vector, vector_in_frac_coords=True): """ Args: indices_to_move: The indices of the sites to move translation_vector: Vector to move the sites. If a list of list or numpy array of shape, (len(indices_to_move), 3), is provided then each translation vector is applied to the corresponding site in the indices_to_move. vector_in_frac_coords: Set to True if the translation vector is in fractional coordinates, and False if it is in cartesian coordinations. Defaults to True. """ self.indices_to_move = indices_to_move self.translation_vector = np.array(translation_vector) self.vector_in_frac_coords = vector_in_frac_coords
[docs] def apply_transformation(self, structure): """ Apply the transformation. Arg: structure (Structure): A structurally similar structure in regards to crystal and site positions. Return: Returns a copy of structure with sites translated. """ s = structure.copy() if self.translation_vector.shape == (len(self.indices_to_move), 3): for i, idx in enumerate(self.indices_to_move): s.translate_sites(idx, self.translation_vector[i], self.vector_in_frac_coords) else: s.translate_sites(self.indices_to_move, self.translation_vector, self.vector_in_frac_coords) return s
def __str__(self): return "TranslateSitesTransformation for indices " + \ "{}, vect {} and vect_in_frac_coords = {}".format( self.indices_to_move, self.translation_vector, self.vector_in_frac_coords) def __repr__(self): return self.__str__() @property def inverse(self): """ Returns: TranslateSitesTranformation with the reverse translation. """ return TranslateSitesTransformation( self.indices_to_move, -self.translation_vector, self.vector_in_frac_coords) @property def is_one_to_many(self): """Return: False""" return False
[docs] def as_dict(self): """ Json-serializable dict representation. """ d = MSONable.as_dict(self) d["translation_vector"] = self.translation_vector.tolist() return d
[docs]class PartialRemoveSitesTransformation(AbstractTransformation): """ Remove fraction of specie from a structure. Requires an oxidation state decorated structure for ewald sum to be computed. Given that the solution to selecting the right removals is NP-hard, there are several algorithms provided with varying degrees of accuracy and speed. The options are as follows: ALGO_FAST: This is a highly optimized algorithm to quickly go through the search tree. It is guaranteed to find the optimal solution, but will return only a single lowest energy structure. Typically, you will want to use this. ALGO_COMPLETE: The complete algo ensures that you get all symmetrically distinct orderings, ranked by the estimated Ewald energy. But this can be an extremely time-consuming process if the number of possible orderings is very large. Use this if you really want all possible orderings. If you want just the lowest energy ordering, ALGO_FAST is accurate and faster. ALGO_BEST_FIRST: This algorithm is for ordering the really large cells that defeats even ALGO_FAST. For example, if you have 48 sites of which you want to remove 16 of them, the number of possible orderings is around 2 x 10^12. ALGO_BEST_FIRST shortcircuits the entire search tree by removing the highest energy site first, then followed by the next highest energy site, and so on. It is guaranteed to find a solution in a reasonable time, but it is also likely to be highly inaccurate. ALGO_ENUMERATE: This algorithm uses the EnumerateStructureTransformation to perform ordering. This algo returns *complete* orderings up to a single unit cell size. It is more robust than the ALGO_COMPLETE, but requires Gus Hart's enumlib to be installed. """ ALGO_FAST = 0 ALGO_COMPLETE = 1 ALGO_BEST_FIRST = 2 ALGO_ENUMERATE = 3 def __init__(self, indices, fractions, algo=ALGO_COMPLETE): """ Args: indices: A list of list of indices. e.g. [[0, 1], [2, 3, 4, 5]] fractions: The corresponding fractions to remove. Must be same length as indices. e.g., [0.5, 0.25] algo: This parameter allows you to choose the algorithm to perform ordering. Use one of PartialRemoveSpecieTransformation.ALGO_* variables to set the algo. """ self.indices = indices self.fractions = fractions self.algo = algo self.logger = logging.getLogger(self.__class__.__name__) def _best_first_ordering(self, structure, num_remove_dict): self.logger.debug("Performing best first ordering") starttime = time.time() self.logger.debug("Performing initial ewald sum...") ewaldsum = EwaldSummation(structure) self.logger.debug("Ewald sum took {} seconds." .format(time.time() - starttime)) starttime = time.time() ematrix = ewaldsum.total_energy_matrix to_delete = [] totalremovals = sum(num_remove_dict.values()) removed = {k: 0 for k in num_remove_dict.keys()} for i in range(totalremovals): maxindex = None maxe = float("-inf") maxindices = None for indices in num_remove_dict.keys(): if removed[indices] < num_remove_dict[indices]: for ind in indices: if ind not in to_delete: energy = sum(ematrix[:, ind]) + \ sum(ematrix[:, ind]) - ematrix[ind, ind] if energy > maxe: maxindex = ind maxe = energy maxindices = indices removed[maxindices] += 1 to_delete.append(maxindex) ematrix[:, maxindex] = 0 ematrix[maxindex, :] = 0 s = structure.copy() s.remove_sites(to_delete) self.logger.debug("Minimizing Ewald took {} seconds." .format(time.time() - starttime)) return [{"energy": sum(sum(ematrix)), "structure": s.get_sorted_structure()}] def _complete_ordering(self, structure, num_remove_dict): self.logger.debug("Performing complete ordering...") all_structures = [] symprec = 0.2 s = SpacegroupAnalyzer(structure, symprec=symprec) self.logger.debug("Symmetry of structure is determined to be {}." .format(s.get_space_group_symbol())) sg = s.get_space_group_operations() tested_sites = [] starttime = time.time() self.logger.debug("Performing initial ewald sum...") ewaldsum = EwaldSummation(structure) self.logger.debug("Ewald sum took {} seconds." .format(time.time() - starttime)) starttime = time.time() allcombis = [] for ind, num in num_remove_dict.items(): allcombis.append(itertools.combinations(ind, num)) count = 0 for allindices in itertools.product(*allcombis): sites_to_remove = [] indices_list = [] for indices in allindices: sites_to_remove.extend([structure[i] for i in indices]) indices_list.extend(indices) s_new = structure.copy() s_new.remove_sites(indices_list) energy = ewaldsum.compute_partial_energy(indices_list) already_tested = False for i, tsites in enumerate(tested_sites): tenergy = all_structures[i]["energy"] if abs((energy - tenergy) / len(s_new)) < 1e-5 and \ sg.are_symmetrically_equivalent(sites_to_remove, tsites, symm_prec=symprec): already_tested = True if not already_tested: tested_sites.append(sites_to_remove) all_structures.append({"structure": s_new, "energy": energy}) count += 1 if count % 10 == 0: timenow = time.time() self.logger.debug("{} structures, {:.2f} seconds." .format(count, timenow - starttime)) self.logger.debug("Average time per combi = {} seconds" .format((timenow - starttime) / count)) self.logger.debug("{} symmetrically distinct structures found." .format(len(all_structures))) self.logger.debug("Total symmetrically distinct structures found = {}" .format(len(all_structures))) all_structures = sorted(all_structures, key=lambda s: s["energy"]) return all_structures def _fast_ordering(self, structure, num_remove_dict, num_to_return=1): """ This method uses the matrix form of ewaldsum to calculate the ewald sums of the potential structures. This is on the order of 4 orders of magnitude faster when there are large numbers of permutations to consider. There are further optimizations possible (doing a smarter search of permutations for example), but this wont make a difference until the number of permutations is on the order of 30,000. """ self.logger.debug("Performing fast ordering") starttime = time.time() self.logger.debug("Performing initial ewald sum...") ewaldmatrix = EwaldSummation(structure).total_energy_matrix self.logger.debug("Ewald sum took {} seconds." .format(time.time() - starttime)) starttime = time.time() m_list = [] for indices, num in num_remove_dict.items(): m_list.append([0, num, list(indices), None]) self.logger.debug("Calling EwaldMinimizer...") minimizer = EwaldMinimizer(ewaldmatrix, m_list, num_to_return, PartialRemoveSitesTransformation.ALGO_FAST) self.logger.debug("Minimizing Ewald took {} seconds." .format(time.time() - starttime)) all_structures = [] lowest_energy = minimizer.output_lists[0][0] num_atoms = sum(structure.composition.values()) for output in minimizer.output_lists: s = structure.copy() del_indices = [] for manipulation in output[1]: if manipulation[1] is None: del_indices.append(manipulation[0]) else: s.replace(manipulation[0], manipulation[1]) s.remove_sites(del_indices) struct = s.get_sorted_structure() all_structures.append( {"energy": output[0], "energy_above_minimum": (output[0] - lowest_energy) / num_atoms, "structure": struct}) return all_structures def _enumerate_ordering(self, structure): # Generate the disordered structure first. s = structure.copy() for indices, fraction in zip(self.indices, self.fractions): for ind in indices: new_sp = {sp: occu * fraction for sp, occu in structure[ind].species.items()} s[ind] = new_sp # Perform enumeration from pymatgen.transformations.advanced_transformations import \ EnumerateStructureTransformation trans = EnumerateStructureTransformation() return trans.apply_transformation(s, 10000)
[docs] def apply_transformation(self, structure, return_ranked_list=False): """ Apply the transformation. Args: structure: input structure 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 key "transformation" is reserved for the transformation that was actually applied to the structure. This transformation is parsed by the alchemy classes for generating a more specific transformation history. Any other information will be stored in the transformation_parameters dictionary in the transmuted structure class. """ num_remove_dict = {} total_combis = 0 for indices, frac in zip(self.indices, self.fractions): num_to_remove = len(indices) * frac if abs(num_to_remove - int(round(num_to_remove))) > 1e-3: raise ValueError("Fraction to remove must be consistent with " "integer amounts in structure.") else: num_to_remove = int(round(num_to_remove)) num_remove_dict[tuple(indices)] = num_to_remove n = len(indices) total_combis += int(round(math.factorial(n) / math.factorial(num_to_remove) / math.factorial(n - num_to_remove))) self.logger.debug("Total combinations = {}".format(total_combis)) try: num_to_return = int(return_ranked_list) except ValueError: num_to_return = 1 num_to_return = max(1, num_to_return) self.logger.debug("Will return {} best structures." .format(num_to_return)) if self.algo == PartialRemoveSitesTransformation.ALGO_FAST: all_structures = self._fast_ordering(structure, num_remove_dict, num_to_return) elif self.algo == PartialRemoveSitesTransformation.ALGO_COMPLETE: all_structures = self._complete_ordering(structure, num_remove_dict) elif self.algo == PartialRemoveSitesTransformation.ALGO_BEST_FIRST: all_structures = self._best_first_ordering(structure, num_remove_dict) elif self.algo == PartialRemoveSitesTransformation.ALGO_ENUMERATE: all_structures = self._enumerate_ordering(structure) else: raise ValueError("Invalid algo.") opt_s = all_structures[0]["structure"] return opt_s if not return_ranked_list \ else all_structures[0:num_to_return]
def __str__(self): return "PartialRemoveSitesTransformation : Indices and fraction" + \ " to remove = {}, ALGO = {}".format(self.indices, self.algo) def __repr__(self): return self.__str__() @property def inverse(self): """Return: None""" return None @property def is_one_to_many(self): """Return: True""" return True
[docs]class AddSitePropertyTransformation(AbstractTransformation): """ Simple transformation to add site properties to a given structure """ def __init__(self, site_properties): """ Args: site_properties (dict): site properties to be added to a structure """ self.site_properties = site_properties
[docs] def apply_transformation(self, structure): """ Apply the transformation. Arg: structure (Structure): A structurally similar structure in regards to crystal and site positions. Return: Returns a copy of structure with sites properties added. """ new_structure = structure.copy() for prop in self.site_properties.keys(): new_structure.add_site_property(prop, self.site_properties[prop]) return new_structure
@property def inverse(self): """Return: None""" return None @property def is_one_to_many(self): """Return: False""" return False