Interpretable Machine Learning Predicting Coercivity of Sm-Co-Based Alloys

Abstract

This study has developed a physically interpretable machine learning framework for predicting coercivity of Sm-Co-based alloys by integrating principles of permanent magnetic materials. Key features governing coercivity were systematically reconstructed using a developed two-step symbolic regression algorithm combining frequency statistics, and individual contributions of these reconstructed features were elucidated by sensitivity analysis. A high-throughput predictive model was set up for coercivity evaluation with exceptional accuracy enabling data-driven composition design of Sm-Co-based permanent magnetic alloys with high coercivity. Taking SmCo₇-based alloys as an example, ternary doping with Ti, In, and Al was identified as optimal for coercivity enhancement. Guided by these predictions, novel multielement doped nanocrystalline Sm-Co-based alloys were prepared exhibiting record high coercivity. This work established a paradigm shift from empirical optimization to mechanism-guided data-driven design of advanced permanent magnetic materials, demonstrating the potential of interpretable machine learning in materials innovation.