Stochastic replica voting machine prediction of stable cubic and double perovskite materials and binary alloys

A machine-learning approach that we term the "stochastic replica voting machine" (SRVM) algorithm is presented and applied to a binary and a three-class classification problem in materials science. Here, we employ SRVM to predict candidate compounds capable of forming stable perovskites and double perovskites and further classify binary (AB) solids. The results of our binary and ternary classifications compared well to those obtained by SVM and neural network algorithms.