发表机构
Merrimack College; The University of Chicago（梅里马克学院; 芝加哥大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出矫顽力感知的机器学习框架，结合成分分组评估与不确定性筛选，并辅以第一性原理验证，高效筛选无稀土软磁合金，识别出Fe-Co富集候选成分。
AI 中文摘要
开发了矫顽力感知的机器学习方法，以居里温度和矫顽力为耦合设计目标，筛选无稀土软磁合金。利用成分分组划分对编译的实验磁性材料数据集进行评估，以避免训练集和测试集中相同成分的重叠，并检验元素分数和成分加权元素描述符对这两种磁性预测的效果。随后，结合基于残差的预测区间和距训练集距离过滤器，筛选Fe-Co-Ni-Mn-Al-Si成分。成分分组评估显示预测精度低于传统的随机划分，证明了评估对未见成分迁移的重要性。将加权元素描述符与元素分数结合可改善居里温度预测，而额外描述符并未改善矫顽力预测，这与矫顽力对加工和微观结构的强依赖性一致。不确定性感知筛选大幅缩小了候选空间，并确定了Fe-Co富集成分以供进一步评估。第一性原理计算独立显示代表性Fe-Co结构中存在强磁极化和磁体积效应，而有限温度原子自旋模拟将Fe-Co基准系统置于高磁有序温度尺度上。该组合方法提供了一个计算框架，用于优先考虑无稀土软磁化学成分，同时区分成分级筛选与候选物特定的物理验证。
英文摘要
Coercivity-aware machine learning is developed to screen rare-earth-free soft magnetic alloys using Curie temperature and coercivity as coupled design targets. A compiled experimental magnetic-material dataset is evaluated using composition-grouped partitioning to avoid overlap of identical compositions between training and testing, and elemental fractions and composition-weighted elemental descriptors are examined for prediction of the two magnetic properties. Residual-based prediction intervals and distance-to-training filters are then incorporated to screen Fe-Co-Ni-Mn-Al-Si compositions. Composition-grouped evaluation shows lower predictive accuracy than conventional random partitioning, demonstrating the importance of evaluating transfer to previously unseen compositions. Combining weighted elemental descriptors with elemental fractions improves Curie-temperature prediction, whereas the additional descriptors do not improve coercivity prediction, consistent with the strong dependence of coercivity on processing and microstructure. Uncertainty-aware screening substantially narrows the candidate space and identifies Fe-Co-rich compositions for further evaluation. First-principles calculations independently show strong magnetic polarization and magnetovolume effects in representative Fe-Co structures, while finite-temperature atomistic spin simulations place the Fe-Co benchmark systems on high magnetic-ordering-temperature scales. The combined approach provides a computational framework for prioritizing rare-earth-free soft-magnetic chemistries while distinguishing composition-level screening from candidate-specific physical validation.
