Interpretable machine learning-assisted screening of perovskite oxides.

Perovskite oxides are extensively utilized in energy storage and conversion. However, they are conventionally screened 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 (), respectively, and interpreting the models using SHapley Additive exPlanations.