pymatgen.analysis.structure_prediction.substitution_probability module¶
This module provides classes for representing species substitution probabilities.
-
class
SubstitutionPredictor
(lambda_table=None, alpha=- 5, threshold=0.001)[source]¶ Bases:
object
Predicts likely substitutions either to or from a given composition or species list using the SubstitutionProbability
- Parameters
() (lambda_table) – Input lambda table.
alpha (float) – weight function for never observed substitutions
threshold (float) – Threshold to use to identify high probability structures.
-
composition_prediction
(composition, to_this_composition=True)[source]¶ Returns charged balanced substitutions from a starting or ending composition.
- Parameters
composition – starting or ending composition
to_this_composition – If true, substitutions with this as a final composition will be found. If false, substitutions with this as a starting composition will be found (these are slightly different)
- Returns
List of predictions in the form of dictionaries. If to_this_composition is true, the values of the dictionary will be from the list species. If false, the keys will be from that list.
-
list_prediction
(species, to_this_composition=True)[source]¶ - Parameters
species – list of species
to_this_composition – If true, substitutions with this as a final composition will be found. If false, substitutions with this as a starting composition will be found (these are slightly different)
- Returns
List of predictions in the form of dictionaries. If to_this_composition is true, the values of the dictionary will be from the list species. If false, the keys will be from that list.
-
class
SubstitutionProbability
(*args, **kwargs)[source]¶ Bases:
pymatgen.analysis.structure_prediction.substitution_probability.SubstitutionProbability
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
Pass through… :param args: :param kwargs: :return: