Machine Learning Prediction of Superconducting Critical Temperature through the Structural Descriptor

Abstract

Superconductivity allows electric conductance with no energy losses when the ambient temperature drops below a critical value (Tc). Currently, the machine learning (ML)-based prediction of potential superconductors has been limited to chemical formulas without explicit treatment of material structures. Herein, we implement an efficient structural descriptor, the smooth overlap of atomic position (SOAP), into the ML models to predict the Tc values with explicit atomic structural information. Using a data set containing 5713 compounds, our ML models with the SOAP descriptor achieved a 92.9% prediction accuracy of coefficient of determination (R2) score via rigorous multialgorithm cross-verification procedures, exceeding the 86.3% accuracy record without atomic structure information. Several new high-temperature superconductors with Tc values over 90 K were predicted using the SOAP-assisted ML model. This study provides insights into the structure–property relationship of high-temperature superconductors.