Predictions of New ABO3 Perovskite Compounds by Combining Machine Learning and Density Functional Theory

We apply machine-learning (ML) methods to a training set of 390 syntheisized ABO3 compounds (254 perovskites (22 cubic and 232 non-cubic) and 136 not perovskites. After classifying the 390 compounds, we construct statistical models that predict out of 625 ABO3 compounds not included in the training set, 235 possible new perovskite materials with 20 new cubic perovskites. We find that the new perovskites are most likely to occur when the A and B atoms are a lanthanide or actinide, when the A atom is an alkali, alkali earth, or late transition metal atom, or when the B atom is a p-block atom. We compare the ML predictions of compounds formed in a given structure with the DFT predictions of compounds stable within 100 meV/atom of the convex hull in these structures. We find that DFT convex hull within OQMD predicts only 87 of the new 235 ML-predicted perovskite compounds to be thermodynamically stable, including 6 cubics. We suggest these 87 as the most promising candidates for future synthesis of novel perovskites. This study clarifies the roles of ML vs DFT predictions of new compounds.