Interpretable machine learning-assisted screening of perovskite oxides.

ABSTRACT: Perovskite oxides are extensively utilized in energy storage and conversion. However, they are conventionally screened via time-consuming and cost-intensive experimental approaches and density functional theory. Herein, interpretable machine learning is applied to identify perovskite oxides from virtual perovskite-type combinations by constructing classification and regression models to predict their thermodynamic stability and energy above the convex hull (E_h), respectively, and interpreting the models using SHapley Additive exPlanations. The highest occupied molecular orbital energy and the elastic modulus of the B-site elements of perovskite oxides are the top two features for stability prediction, whereas the Stability Label and features involving the elastic m

SUBMITTER: Zhao J 

PROVIDER: S-EPMC10813820 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature