Element selection for crystalline inorganic solid discovery guided by unsupervised machine learning of experimentally explored chemistry

Abstract

Machine learning has the potential to significantly speed-up the discovery of new materials in synthetic materials chemistry. Here the authors combine unsupervised machine learning and crystal structure prediction to predict a novel quaternary lithium solid electrolyte that is then synthesized.