Inferring energy–composition relationships with Bayesian optimization enhances exploration of inorganic materials

Computational exploration of the compositional spaces of materials can provide guidance for synthetic research and thus accelerate the discovery of novel materials. Most approaches employ high-throughput sampling and focus on reducing the time for energy evaluation for individual compositions, often...

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Bibliographic Details
Published in:The Journal of chemical physics 2024-02, Vol.160 (5)
Main Authors: Vasylenko, Andrij, Asher, Benjamin M., Collins, Christopher M., Gaultois, Michael W., Darling, George R., Dyer, Matthew S., Rosseinsky, Matthew J.
Format: Article
Language:English
Subjects:
Algorithms
Composition
Inorganic materials
Machine learning
Sampling
Stoichiometry
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Summary:Computational exploration of the compositional spaces of materials can provide guidance for synthetic research and thus accelerate the discovery of novel materials. Most approaches employ high-throughput sampling and focus on reducing the time for energy evaluation for individual compositions, often at the cost of accuracy. Here, we present an alternative approach focusing on effective sampling of the compositional space. The learning algorithm PhaseBO optimizes the stoichiometry of the potential target material while improving the probability of and accelerating its discovery without compromising the accuracy of energy evaluation.
ISSN:0021-9606
1089-7690
DOI:10.1063/5.0180818