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 (Eh), 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 modulus and ionic radius are crucial for Eh regression. A classification model, which displays an accuracy of 0.919, precision of 0.937, F1-score of 0.932, and recall of 0.935, screens 682 143 stable perovskite oxides from 1 126 668 virtual perovskite-type combinations. The Eh values of the predicted stable perovskites are forecasted by a regression model with a coefficient of determination of 0.916, and root mean square error of 24.2 meV atom-1. Good agreement is observed between the regression model predicted and density functional theory-calculated Eh values.