Stochastic Replica Voting Machine Prediction of Stable Perovskite, Double Perovskite and Binary Alloys

We introduce a statistical physics inspired supervised machine learning algorithm for classification and regression problems. The algorithm predicts the classification/regression values of new data by combining (via voting) the outputs of these numerous linear expansions in randomly chosen functions. The algorithm has been tested on 10 diverse training data sets of various types and feature space dimensions. It has been shown to consistently exhibit high accuracy and readily allow for optimization of parameters, while simultaneously avoiding pitfalls of existing algorithms such as those associated with class imbalance. We applied this machine learning approach that we term that the “Stochastic Replica Voting Machine” (SRVM) to a binary and a 3-class classification problems in materials science. Here, we employ SRVM to predict candidate compounds capable of forming cubic Perovskite (ABX3) structure, double perovskite structure and further classify binary (AB) solids. The results of our binary and ternary classifications compared well to those obtained by the SVM algorithm.