def test_mini_lambda_table(self): sp = SubstitutionProbability(lambda_table=get_table(), alpha=-5.0) o2 = Species("O", -2) s2 = Species("S", -2) li1 = Species("Li", 1) na1 = Species("Na", 1) self.assertAlmostEqual(sp.prob(s2, o2), 0.124342317272, 5, "probability isn't correct") self.assertAlmostEqual(sp.pair_corr(li1, na1), 1.65425296864, 5, "correlation isn't correct") prob = sp.cond_prob_list([o2, li1], [na1, li1]) self.assertAlmostEqual(prob, 0.00102673915742, 5, "probability isn't correct")
def test_mini_lambda_table(self): sp = SubstitutionProbability(lambda_table=get_table(), alpha= -5.) o2 = Specie('O', -2) s2 = Specie('S', -2) li1 = Specie('Li', 1) na1 = Specie('Na', 1) self.assertAlmostEqual(sp.prob(s2, o2), 0.124342317272, 5 , "probability isn't correct") self.assertAlmostEqual(sp.pair_corr(li1, na1), 1.65425296864, 5 , "correlation isn't correct") prob = sp.cond_prob_list([o2, li1], [na1, li1]) self.assertAlmostEqual(prob, 0.00102673915742, 5 , "probability isn't correct")
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
class ProbabilityBenchmarker: """Benchmarking tests for pymatgen SubstitutionProbability.""" @timeit def run_tests(self): """Run all tests.""" self.__sp_setup() self.__pair_corr() @timeit def __sp_setup(self): """Set up SubstitutionProbability.""" self.sp = SubstitutionProbability() @timeit def __pair_corr(self): """Get pair correlation.""" pairs = cwr(self.sp.species, 2) for s1, s2 in pairs: self.sp.pair_corr(s1, s2)
def test_full_lambda_table(self): """ This test tests specific values in the data folder. If the json is updated, these tests will have to be as well """ sp = SubstitutionProbability(alpha= -5.) sp1 = Specie('Fe', 4) sp3 = Specie('Mn', 3) prob1 = sp.prob(sp1, sp3) self.assertAlmostEqual(prob1, 1.69243954552e-05, 5 , "probability isn't correct") sp2 = Specie('Pt', 4) sp4 = Specie('Pd', 4) prob2 = sp.prob(sp2, sp4) self.assertAlmostEqual(prob2, 4.7174906021e-05, 5 , "probability isn't correct") corr = sp.pair_corr(Specie("Cu", 2), Specie("Fe", 2)) self.assertAlmostEqual(corr, 6.82496631637, 5 , "probability isn't correct") prob3 = sp.cond_prob_list([sp1, sp2], [sp3, sp4]) self.assertAlmostEqual(prob3, 0.000300298841302, 6 , "probability isn't correct")
def setUpClass(cls): """Set up the test initial structure and mutator.""" cls.test_struct = SmactStructure.from_file(TEST_POSCAR) cls.test_mutator = CationMutator.from_json(lambda_json=TEST_LAMBDA_JSON) cls.test_pymatgen_mutator = CationMutator.from_json( lambda_json=None, alpha=lambda x, y: -5 ) # 5 random test species -> 5! test pairs cls.test_species = sample(cls.test_pymatgen_mutator.specs, 5) cls.test_pairs = list(itertools.combinations_with_replacement(cls.test_species, 2)) cls.pymatgen_sp = SubstitutionProbability(lambda_table=None, alpha=-5)
def test_full_lambda_table(self): """ This test tests specific values in the data folder. If the json is updated, these tests will have to be as well """ sp = SubstitutionProbability(alpha=-5.0) sp1 = Species("Fe", 4) sp3 = Species("Mn", 3) prob1 = sp.prob(sp1, sp3) self.assertAlmostEqual(prob1, 1.69243954552e-05, 5, "probability isn't correct") sp2 = Species("Pt", 4) sp4 = Species("Pd", 4) prob2 = sp.prob(sp2, sp4) self.assertAlmostEqual(prob2, 4.7174906021e-05, 5, "probability isn't correct") corr = sp.pair_corr(Species("Cu", 2), Species("Fe", 2)) self.assertAlmostEqual(corr, 6.82496631637, 5, "probability isn't correct") prob3 = sp.cond_prob_list([sp1, sp2], [sp3, sp4]) self.assertAlmostEqual(prob3, 0.000300298841302, 6, "probability isn't correct")
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 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 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 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([], []) logging.info('{} substitutions found'.format(len(output))) return output 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) logging.info('{} charge balanced ' 'compositions found'.format(len(output))) return output 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__ } @classmethod def from_dict(cls, d): """ Args: d (dict): Dict representation Returns: Class """ t = d['threshold'] kwargs = d['kwargs'] return cls(threshold=t, **kwargs)
def __sp_setup(self): """Set up SubstitutionProbability.""" self.sp = SubstitutionProbability()
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 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 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 not el in chemical_system: return False return True 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([], []) logging.info('{} substitutions found'.format(len(output))) return output 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) logging.info('{} charge balanced ' 'compositions found'.format(len(output))) return output def as_dict(self): return {"name": self.__class__.__name__, "version": __version__, "kwargs": self._kwargs, "threshold": self._threshold, "@module": self.__class__.__module__, "@class": self.__class__.__name__} @classmethod def from_dict(cls, d): t = d['threshold'] kwargs = d['kwargs'] return cls(threshold=t, **kwargs)