Accelerated design and discovery of perovskites with high conductivity for energy applications through machine learning

Abstract

We use machine learning tools for the design and discovery of ABO₃-type perovskite oxides for various energy applications, using over 7000 data points from the literature. We demonstrate a robust learning framework for efficient and accurate prediction of total conductivity of perovskites and their classification based on the type of charge carrier at different conditions of temperature and environment. After evaluating a set of >100 features, we identify average ionic radius, minimum electronegativity, minimum atomic mass, minimum formation energy of oxides for all B-site, and B-site dopant ions of the perovskite as the crucial and relevant predictors for determining conductivity and the type of charge carriers. The models are validated by predicting the conductivity of compounds absent in the training set. We screen 1793 undoped and 95,832 A-site and B-site doped perovskites to report the perovskites with high conductivities, which can be used for different energy applications, depending on the type of the charge carriers.