Machine learning for glass science and engineering: A review

Abstract

The design of new glasses is often plagued by poorly efficient Edisonian “trial-and-error” discovery approaches. As an alternative route, the Materials Genome Initiative has largely popularized new approaches relying on artificial intelligence and machine learning for accelerating the discovery and optimization of novel, advanced materials. Here, we review some recent progress in adopting machine learning to accelerate the design of new glasses with tailored properties. Keywords: Composition-property relationship, Structural signature, Molecular dynamics simulation, Artificial neuron network, Bayesian optimization