AI-Driven Precision Annealing for High Performance Fe-Based Amorphous Alloys

Abstract

The magnetic properties of Fe-based amorphous/nanocrystalline alloys are governed by composition, annealing process, and nanostructure. Although AI has advanced the development of soft magnetic amorphous alloys, the predominant focus on optimizing composition has resulted in insufficient understanding of the mechanisms of heat treatment and nanocrystalline precipitation. In this study, we propose an AI-guided non-isothermal annealing strategy that can accurately determine the critical Avrami exponent of 2.5, which corresponds to a transition from 3D growth to spatially confined nanocrystals during the process of crystallization. Applying this approach, we not only validated the performance of our previously reported Fe_85.5B_8.5Si₂P₂C₂ alloy but also achieved a record-high B_s of 1.97 T in Fe₆₉Co₁₆Ni₁Si₃B₁₁. Moreover, even for commercial FINEMET alloy, the B_s could still be enhanced by 6.2%, while simultaneously maintaining H_c below 1 A m⁻¹. Compared to conventional annealing processes, this technique can both endow superior soft magnetic performance and achieve an average 500% improvement in annealing time. This study pioneers an AI strategy for Fe-based amorphous/nanocrystalline alloys and may establish a paradigm for integrating AI with physical theories applicable to diverse material systems.

Keywords

annealing process / crystallization kinetics / machine learning / nanocrystalline / soft magnetic properties

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2026 The Author(s). Energy & Environmental Materials published by John Wiley & Sons Australia, Ltd on behalf of Zhengzhou University.