Source code for pymatgen.analysis.structure_prediction.substitutor

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

This module provides classes for predicting new structures from existing ones.

import itertools
import logging
from operator import mul
import functools
from monty.json import MSONable

from pymatgen.core.periodic_table import get_el_sp
from pymatgen.analysis.structure_prediction.substitution_probability \
    import SubstitutionProbability
from pymatgen.transformations.standard_transformations \
    import SubstitutionTransformation
from pymatgen.alchemy.transmuters import StandardTransmuter
from pymatgen.alchemy.materials import TransformedStructure
from pymatgen.alchemy.filters import RemoveDuplicatesFilter, \

__author__ = "Will Richards, Geoffroy Hautier"
__copyright__ = "Copyright 2012, The Materials Project"
__version__ = "1.2"
__maintainer__ = "Will Richards"
__email__ = ""
__date__ = "Aug 31, 2012"

[docs]class Substitutor(MSONable): """ This object uses a data mined ionic substitution approach to propose compounds likely to be stable. It relies on an algorithm presented in Hautier, G., Fischer, C., Ehrlacher, V., Jain, A., and Ceder, G. (2011). Data Mined Ionic Substitutions for the Discovery of New Compounds. Inorganic Chemistry, 50(2), 656-663. doi:10.1021/ic102031h """ def __init__(self, threshold=1e-3, symprec=0.1, **kwargs): """ This substitutor uses the substitution probability class to find good substitutions for a given chemistry or structure. Args: threshold: probability threshold for predictions symprec: symmetry precision to determine if two structures are duplicates kwargs: kwargs for the SubstitutionProbability object lambda_table, alpha """ self._kwargs = kwargs self._sp = SubstitutionProbability(**kwargs) self._threshold = threshold self._symprec = symprec
[docs] def get_allowed_species(self): """ returns the species in the domain of the probability function any other specie will not work """ return self._sp.species
[docs] def pred_from_structures(self, target_species, structures_list, remove_duplicates=True, remove_existing=False): """ performs a structure prediction targeting compounds containing all of the target_species, based on a list of structure (those structures can for instance come from a database like the ICSD). It will return all the structures formed by ionic substitutions with a probability higher than the threshold Notes: If the default probability model is used, input structures must be oxidation state decorated. See AutoOxiStateDecorationTransformation This method does not change the number of species in a structure. i.e if the number of target species is 3, only input structures containing 3 species will be considered. Args: target_species: a list of species with oxidation states e.g., [Specie('Li',1),Specie('Ni',2), Specie('O',-2)] structures_list: a list of dictionnary of the form {'structure':Structure object ,'id':some id where it comes from} the id can for instance refer to an ICSD id. remove_duplicates: if True, the duplicates in the predicted structures will be removed remove_existing: if True, the predicted structures that already exist in the structures_list will be removed Returns: a list of TransformedStructure objects. """ target_species = get_el_sp(target_species) result = [] transmuter = StandardTransmuter([]) if len(list(set(target_species) & set(self.get_allowed_species()))) \ != len(target_species): raise ValueError("the species in target_species are not allowed " + "for the probability model you are using") for permut in itertools.permutations(target_species): for s in structures_list: # check if: species are in the domain, # and the probability of subst. is above the threshold els = s['structure'].composition.elements if len(els) == len(permut) and len(list(set(els) & set(self.get_allowed_species()))) == \ len(els) and self._sp.cond_prob_list(permut, els) > self._threshold: clean_subst = {els[i]: permut[i] for i in range(0, len(els)) if els[i] != permut[i]} if len(clean_subst) == 0: continue transf = SubstitutionTransformation(clean_subst) if Substitutor._is_charge_balanced( transf.apply_transformation(s['structure'])): ts = TransformedStructure( s['structure'], [transf], history=[{"source": s['id']}], other_parameters={ 'type': 'structure_prediction', 'proba': self._sp.cond_prob_list(permut, els)} ) result.append(ts) transmuter.append_transformed_structures([ts]) if remove_duplicates: transmuter.apply_filter(RemoveDuplicatesFilter( symprec=self._symprec)) if remove_existing: # Make the list of structures from structures_list that corresponds to the # target species chemsys = list(set([sp.symbol for sp in target_species])) structures_list_target = [st['structure'] for st in structures_list if Substitutor._is_from_chemical_system( chemsys, st['structure'])] transmuter.apply_filter(RemoveExistingFilter(structures_list_target, symprec=self._symprec)) return transmuter.transformed_structures
@staticmethod def _is_charge_balanced(struct): """ checks if the structure object is charge balanced """ if sum([s.specie.oxi_state for s in struct.sites]) == 0.0: return True else: return False @staticmethod def _is_from_chemical_system(chemical_system, struct): """ checks if the structure object is from the given chemical system """ chemsys = list(set([sp.symbol for sp in struct.composition])) if len(chemsys) != len(chemical_system): return False for el in chemsys: if el not in chemical_system: return False return True
[docs] def pred_from_list(self, species_list): """ There are an exceptionally large number of substitutions to look at (260^n), where n is the number of species in the list. We need a more efficient than brute force way of going through these possibilities. The brute force method would be:: output = [] for p in itertools.product(self._sp.species_list , repeat = len(species_list)): if self._sp.conditional_probability_list(p, species_list) > self._threshold: output.append(dict(zip(species_list,p))) return output Instead of that we do a branch and bound. Args: species_list: list of species in the starting structure Returns: list of dictionaries, each including a substitutions dictionary, and a probability value """ species_list = get_el_sp(species_list) # calculate the highest probabilities to help us stop the recursion max_probabilities = [] for s2 in species_list: max_p = 0 for s1 in self._sp.species: max_p = max([self._sp.cond_prob(s1, s2), max_p]) max_probabilities.append(max_p) output = [] def _recurse(output_prob, output_species): best_case_prob = list(max_probabilities) best_case_prob[:len(output_prob)] = output_prob if functools.reduce(mul, best_case_prob) > self._threshold: if len(output_species) == len(species_list): odict = { 'substitutions': dict(zip(species_list, output_species)), 'probability': functools.reduce(mul, best_case_prob)} output.append(odict) return for sp in self._sp.species: i = len(output_prob) prob = self._sp.cond_prob(sp, species_list[i]) _recurse(output_prob + [prob], output_species + [sp]) _recurse([], [])'{} substitutions found'.format(len(output))) return output
[docs] def pred_from_comp(self, composition): """ Similar to pred_from_list except this method returns a list after checking that compositions are charge balanced. """ output = [] predictions = self.pred_from_list(composition.elements) for p in predictions: subs = p['substitutions'] charge = 0 for i_el in composition.elements: f_el = subs[i_el] charge += f_el.oxi_state * composition[i_el] if charge == 0: output.append(p)'{} charge balanced ' 'compositions found'.format(len(output))) return output
[docs] def as_dict(self): """ Returns: MSONable dict """ return {"name": self.__class__.__name__, "version": __version__, "kwargs": self._kwargs, "threshold": self._threshold, "@module": self.__class__.__module__, "@class": self.__class__.__name__}
[docs] @classmethod def from_dict(cls, d): """ Args: d (dict): Dict representation Returns: Class """ t = d['threshold'] kwargs = d['kwargs'] return cls(threshold=t, **kwargs)