pymatgen.analysis.structure_prediction.substitutor module

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

class Substitutor(threshold=0.001, symprec: float = 0.1, **kwargs)[source]

Bases: 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

This substitutor uses the substitution probability class to find good substitutions for a given chemistry or structure.

  • threshold – probability threshold for predictions

  • symprec – symmetry precision to determine if two structures are duplicates

  • kwargs – kwargs for the SubstitutionProbability object lambda_table, alpha


Returns: MSONable dict

classmethod from_dict(d)[source]

d (dict) – Dict representation




Returns the species in the domain of the probability function any other specie will not work


Similar to pred_from_list except this method returns a list after checking that compositions are charge balanced.


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:
return output

Instead of that we do a branch and bound.


species_list – list of species in the starting structure


list of dictionaries, each including a substitutions dictionary, and a probability value

pred_from_structures(target_species, structures_list, remove_duplicates=True, remove_existing=False)[source]

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.

  • target_species – a list of species with oxidation states e.g., [Species(‘Li’,1),Species(‘Ni’,2), Species(‘O’,-2)]

  • structures_list – a list of dictionary 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


a list of TransformedStructure objects.