Identifying New Classes of High Temperature Superconductors With Convolutional Neural Networks

Abstract

Applying machine learning to aid the search for high temperature superconductors has recently been a topic of significant interest due to the broad applications of these materials but is challenging due to the lack of a quantitative microscopic model. Here we analyze over 33,000 entries from the Superconducting Materials Database, maintained by the National Institute for Materials Science of Japan, assigning crystal structures to each entry by correlation with Materials project and other structural databases. These augmented inputs are combined with material-specific properties, including critical temperature, to train convolutional neural networks (CNNs) to identify superconductors. Classification models achieve accuracy >95% and regression models trained to predict critical temperature achieve R2 >0.92 and mean absolute error ≈ 5.6 K. A crystal-graph representation whereby an undirected graph encodes atom sites (graph vertices) and their bonding relationships (graph edges), is used to represent materials’ periodic crystal structure to the CNNs. Trained networks are used to search though 130,000 crystal structures in the Materials Project for high temperature superconductor candidates and predict their critical temperature; several materials with model-predicted TC >30 K are proposed, including rediscovery of the recently explored infinite layer nickelates.