Predicting new superconductors and their critical temperatures using machine learning

Abstract

We used the superconductors in the SuperCon database to construct element vectors and then perform machine learning of their critical temperatures (Tc). Only the chemical composition of superconductors was used in this procedure. No physical predictors (neither experimental nor computational) of any kind were used. We achieved the coefficient of determination R2 ≃ 0.93, which is comparable and in some cases higher than similar estimates using other artificial intelligence techniques. Based on this machine learning model, we predicted several new superconductors with high critical temperatures. We also discuss several factors that limit the learning process and suggest possible ways to overcome them.