Integrating ultra-high saturation magnetization intensity and extreme-low coercivity in FeCoBSiCu alloy assisted by machine learning

The relentless pursuit of miniaturization, high frequency, and efficiency in electronic equipment has set new and stringent benchmarks for soft magnetic materials (SMMs). Key among these are the demands for higher saturation magnetic induction intensity (B_s) and lower coercivity (H_c). Yet, a longstanding trade-off has persisted: higher B_s typically increases H_c. For over a century, this dilemma has remained unsolved, with no SMM exceeding a B_s of 1.9 T and maintaining a H_c below 2 A/m, significantly constraining the capabilities of modern electronic devices. Facing the vast materials composition space, traditional trial-and-error approaches to develop SMMs have proven inefficient and labor-intensive. To overcome these limitations, this study employs machine learning techniques to construct artificial neural networks, forecasting with approaching 95% precision. From a compositional space of 486,000 permutations and combinations, the optimal FeCoBSiCu alloy was identified and synthesized, achieving an unprecedented B_s of 1.96 T and an extreme-low H_c of 1.2 A/m. This performance breaks the B_s-H_c trade-off and surpasses all previously reported SMMs. The magnetic domain evolution and unique heterostructure were applied to the soft magnetic mechanism. This work may pave the way for a paradigm shift in the compositional design of SMMs, accelerating the advancement of electronic devices.