This module provides classes for representing species substitution probabilities.
Predicts likely substitutions either to or from a given composition or species list using the SubstitutionProbability
Returns charged balanced substitutions from a starting or ending composition.
This class finds substitution probabilities given lists of atoms to substitute. The inputs make more sense if you look through the from_defaults static method.
The substitution prediction algorithm is 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 module provides classes for predicting new structures from existing ones.
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.
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: output.append(dict(zip(species_list,p))) return output
Instead of that we do a branch and bound.
performs a structure prediction targeting compounds containing the target_species and 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