Machine Learning for Superconductor Discovery: From Data-Driven Insights to Accelerated Design

Superconducting materials, exhibiting zero resistance and perfect diamagnetism, play a crucial role in electromagnetic applications. The critical transition temperature (T_c) is a key parameter in determining the practical utility of superconducting materials. However, experimental observations and theoretical calculations face challenges such as lengthy trial-and-error cycles and the lack of unified theoretical frameworks. The data-driven scientific paradigm has established machine learning (ML) as a powerful tool in superconducting materials research, enabling data correlation analysis, candidate identification, and T_c prediction. This review provides an overview of recent advancements in applying ML to superconducting materials research, focusing on T_c prediction, exploring potential superconducting candidates, and applying advanced algorithms. Additionally, it highlights experimental and theoretical validations of machine learning outcomes. The features of superconducting materials and their data representation methods are introduced, encompassing both experimental and computationally generated data sets. The review further summarizes inverse design strategies driven by machine learning, demonstrating their potential for discovering new high-T_c superconductors. Finally, challenges such as data scarcity, limited model generalizability, and insufficient cross-scale prediction capabilities are discussed, alongside proposed future directions to advance machine learning-driven superconducting materials research.