Learning Superconductivity from Ordered and Disordered Material Structures

Abstract

Superconductivity is a fascinating phenomenon observed in certain materials under certain conditions. However, some critical aspects of it, such as the relationship between superconductivity and materials’ chemical/structural features, still need to be understood. Recent successes of data-driven approaches in material science strongly inspire researchers to study this relationship with them, but a corresponding dataset is still lacking. Hence, we present a new dataset for data-driven approaches, namely SuperCon3D, containing both 3D crystal structures and experimental superconducting transition temperature (T c ) for the first time. Based on SuperCon3D, we propose two deep learning methods for designing high T c superconductors. The first is SODNet, a novel equivariant graph attention model for screening known structures, which differs from existing models in incorporating both ordered and disordered geometric content. The second is a diffusion generative model DiffCSP-SC for creating new structures, which enables high T c -targeted generation. Extensive experiments demonstrate that both our proposed dataset and models are advantageous for designing new high T c superconducting candidates.