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arXiv · 2509.05702

Accelerated Design of Mechanically Hard Magnetically Soft High-entropy Alloys via Multi-objective Bayesian Optimization

Mian Dai·Yixuan Zhang·Weijia He·Chen Shen·Xiaoqing Li·Stephan Schönecker·Liuliu Han·Ruiwen Xie·Tianhang Zhou·Hongbin Zhang
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Abstract

Designing high-entropy alloys (HEAs) that are both mechanically hard and possess soft magnetic properties is inherently challenging, as a trade-off is needed for mechanical and magnetic properties. In this study, we optimize HEA compositions using a multi-objective Bayesian optimization (MOBO) framework to achieve simultaneous optimal mechanical and magnetic properties. An ensemble surrogate model is constructed to enhance the accuracy of machine learning surrogate models, while an efficient sampling strategy combining Monte Carlo sampling and acquisition function is applied to explore the high-dimensional compositional space. The implemented MOBO strategy successfully identifies Pareto-optimal compositions with enhanced mechanical and magnetic properties. The ensemble model provides robust and reliable predictions, and the sampling approach reduces the likelihood of entrapment in local optima. Our findings highlight specific elemental combinations that meet the dual design objectives, offering guidance for the synthesis of next-generation HEAs.

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Mian Dai, Yixuan Zhang, Weijia He, Chen Shen, Xiaoqing Li, Stephan Schönecker, Liuliu Han, Ruiwen Xie, Tianhang Zhou, Hongbin Zhang. 2026-03-31. Accelerated Design of Mechanically Hard Magnetically Soft High-entropy Alloys via Multi-objective Bayesian Optimization. https://arxiv.org/abs/2509.05702

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