Stochastic Replica Voting Machine Prediction of Stable Cubic and Double Perovskite Materials and Binary Alloys
Materials Science
2019-06-26 v5
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 () solids. The results of our binary and ternary classifications compared well to those obtained by SVM and neural network algorithms.
Keywords
Cite
@article{arxiv.1705.08491,
title = {Stochastic Replica Voting Machine Prediction of Stable Cubic and Double Perovskite Materials and Binary Alloys},
author = {T. Mazaheri and Bo Sun and J. Scher-Zagier and A. S. Thind and D. Magee and P. Ronhovde and T. Lookman and R. Mishra and Z. Nussinov},
journal= {arXiv preprint arXiv:1705.08491},
year = {2019}
}
Comments
45 pages, 25 figures