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

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 SmCo 7 ‐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.