Crystal structure graph neural networks for high-performance superconducting critical temperature prediction

Abstract

The utilization of machine learning methods to predict the superconducting critical temperature (T_c) traditionally necessitates manually constructing elemental features, which challenges both the provision of meaningful chemical insights and the accuracy of predictions. In this work, we introduced crystal structure graph neural networks to extract structure-based features for T_c prediction. Our results indicated that these structure-based models outperformed all previously reported models, achieving an impressive coefficient of determination (R²) of 0.962 and a root mean square error (RMSE) of 6.192 K. From the existing Inorganic Crystal Structure Database (ICSD), our model successfully identified 76 potential high-temperature superconducting compounds with T_c ⩾ 77 K. Among these, Tl₅Ba₆Ca₆Cu₉O₂₉ and TlYBa₂Cu₂O₇ exhibit remarkably high T_c values of 108.4 and 101.8 K, respectively. This work provides a perspective on the structure-property relationship for reliable T_c prediction.