Soft magnetic alloys play a critical role in power conversion, magnetic sensing, magnetic storage and electric actuating, which are fundamental components of modern technological innovation. Therefore, the rational design of soft magnetic alloys holds substantial scientific and commercial value. With excellent comprehensive performance, emerging compositionally complex alloys (CCAs) with high chemical complexity have garnered significant interest. The huge composition search space of CCAs provides both challenges and opportunities for discovering new high-performance magnetic materials. The traditional alloy design method relying on scientific intuition and a trial-and-error strategy could be inefficient and costly for magnetic CCAs. Accordingly, with great capacities for nonlinear and adaptive information processing, machine learning (ML) has shown great potential in magnetic CCA studies. This paper reviews magnetic properties of CCAs, examines the various inspiring applications of ML methods in magnetic CCAs, and discusses the future directions for unleashing the full potential of ML methods for applications in magnetic CCAs’ studies.

中文翻译：

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磁性成分复杂合金的机器学习研究：批判性评论


软磁合金在功率转换、磁传感、磁存储和电驱动等现代技术创新的基本组成部分中发挥着关键作用。因此，软磁合金的合理设计具有重要的科学和商业价值。凭借优异的综合性能，具有高化学复杂性的新兴成分复杂合金（CCA）引起了人们的极大兴趣。 CCA巨大的成分搜索空间为发现新型高性能磁性材料提供了挑战和机遇。对于磁性 CCA 来说，依赖科学直觉和试错策略的传统合金设计方法可能效率低下且成本高昂。因此，机器学习（ML）具有强大的非线性和自适应信息处理能力，在磁CCA研究中显示出巨大的潜力。本文回顾了 CCA 的磁性，研究了 ML 方法在磁性 CCA 中的各种启发性应用，并讨论了充分发挥 ML 方法在磁性 CCA 研究中的应用潜力的未来方向。