# Stochastic Replica Voting Machine Prediction of Stable Cubic and Double Perovskite Materials and Binary Alloys

## TL;DR

This paper introduces the Stochastic Replica Voting Machine (SRVM), a machine learning algorithm for predicting stable perovskite materials and classifying binary solids, showing comparable performance to SVMs and neural networks.

## Contribution

The paper presents the SRVM algorithm, a novel machine learning method for materials classification, demonstrating its effectiveness in predicting stable perovskites and binary compounds.

## Key findings

- SRVM achieves classification accuracy comparable to SVM and neural networks.
- SRVM successfully predicts candidate stable perovskite compounds.
- The method applies to binary and ternary classification problems in materials science.

## Abstract

A machine learning approach that we term the `Stochastic Replica Voting Machine' (SRVM) algorithm is presented and applied to a binary and a 3-class classification problems 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.