Inferring energy-composition relationships with Bayesian optimization enhances exploration of inorganic materials.
J Chem Phys
; 160(5)2024 Feb 07.
Article
en En
| MEDLINE
| ID: mdl-38341704
ABSTRACT
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.
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J Chem Phys
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2024
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