Machine Learning of ABO$_3$ Crystalline Compounds

We apply two advanced machine learning methods to a database of experimentally known AB$O_3$ materials to predict the existence of possible new perovskite materials and possible new cubic perovskites. Constructing a list of 625 possible new materials from charge conserving combinations of A and B atoms in known stable ABO$_3$ materials, we predict about 440 new perovskites. These new perovskites are predicted most likely to occur when the A and B atoms are a lanthanide or actinide, when the A atom is a alkali, alkali earth, or late transition metal, and a when the B atom is a $p$-block atom. These results are in basic agreement with the recent materials discovery by substitution analysis of Hautier et al. [Inorg. Chem. 50, 656 (2011)] who data-mined the entire ICSD data base to develop the probability that in any crystal structure atom X could be substituted for by atom Y. The results of our analysis has several points of disagreement with a recent high throughput DFT study of ABO$_3$ crystalline compounds by Emery et al. [Chem. Mat. 28, 5621 (2016)] who predict few, if any, new perovskites whose A and B atoms are both a lanthanide. They also predict far more new cubic perovskites than we do: We predict few, if any, with a high degree of probability.