发表机构
Renmin University of China; Songshan Lake Materials Laboratory; Institute of Physics, Chinese Academy of Sciences; University of Chinese Academy of Sciences（中国人民大学; 松山湖材料实验室; 中国科学院物理研究所; 中国科学院大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究开发生成式多任务深度学习（GMTDL），同步优化铁基软磁合金的成分与多项权衡特性，其性能优于传统机器学习策略，还预测出新型高性能合金并揭示关键元素协同效应。
AI 中文摘要
铁基非晶合金是开发下一代高频、高效器件的极具潜力的软磁材料。然而，由于成分空间广阔且这些特性间存在复杂权衡，优化兼具超高饱和磁通密度（B_s）、超低矫顽力（H_c）与良好玻璃形成能力的铁基合金是一个棘手问题，传统设计方法面临巨大挑战。本文开发了生成式多任务深度学习（GMTDL）以实现成分与权衡特性的同步优化。尽管存在数据集有限且不平衡的限制，GMTDL仍能充分利用并共享不同任务的数据集知识，因此在预测具有多个目标特性的合金时表现出优异性能，优于以往基于机器学习的设计策略。此外，GMTDL还可定制成分，为调控特性及生成所需候选物以供后续实验处理提供了高效途径。通过与近期报道的铁基合金进行基准测试，GMTDL的有效性与可靠性得到严格验证，还预测出部分具有超高B_s与超低H_c的新型合金，同时揭示了关键元素的最佳含量区间及其协同效应，可为实际应用提供指导。因此，本研究建立了一种有效且可靠的范式，用于同步预测与优化兼具多种特性的高性能材料。
英文摘要
Fe-based amorphous alloys are promising soft magnetic materials for developing next-generation devices with high frequency and efficiency. However, optimization of Fe-based alloys with ultra-high saturation magnetic flux density (B_s), ultra-low coercivity (H_c), and good glass-forming ability is a notorious problem, owing to the vast composition space and complex trade-offs of these properties. Thus, conventional design methods encounter great challenges. Here we develop a generative multi-task deep learning (GMTDL) to achieve simultaneous optimization of compositions and trade-off properties. The GMTDL can sufficiently exploit and share the knowledge of datasets across different tasks, despite the limitation and imbalance of these datasets. Therefore, it exhibits superior performance in prediction of alloys with multiple targeted properties, outperforming previous machine learning-based design strategies. Moreover, the GMTDL can also tailor compositions, providing an efficient way to regulate properties and generate desired candidates for further experimental processing. The validity and reliability of GMTDL are rigorously tested by benchmarking with Fe-based alloys reported very recently. Moreover, some new alloys with ultra-high B_s and ultra-low H_c are predicted. The optimal content windows of key elements and their synergistic effects are also unraveled for practical guidance. Thus, our study establishes an effective and reliable paradigm for simultaneous prediction and optimization of high-performance materials with multiple properties.
Comments92 pages, 8 main-text figures, 1 main-text table; Supplementary Materials included
Journal refChin. Phys. B, 2026, 35(7): 070705
