发表机构
Materials Science and Technology Division, Oak Ridge National Laboratory; Department of Applied Mathematics, University of Arizona（橡树岭国家实验室材料科学与技术分部; 亚利桑那大学应用数学系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究开发了机器学习辅助工作流程，结合预训练材料模型、Materials Project数据及磁化预测模型，从6万余种CaCu₅型结构中筛选出439种候选物，经第一性原理计算得到159种具正轴向磁各向异性的化合物，为永磁材料筛选提供了实用策略。
AI 中文摘要
我们开发了一种机器学习辅助的工作流程，用于快速筛选CaCu₅结构家族内的永磁候选材料。我们通过取代生成了超过60000种结构，这些成分通过预训练材料模型、Materials Project数据和磁化预测机器学习模型的组合进行筛选。四个标准——金属性、负形成能、凸包能≤50 meV/原子，以及预测磁化强度高于采用的≥0.75 T阈值——将初始化学空间缩减至439种候选物，用于第一性原理计算。密度泛函理论计算显示，159种化合物表现出正轴向磁各向异性能，相较于测试的面内方向更倾向于[001]方向。所得数据集揭示了CaCu₅成分空间中磁各向异性的化学趋势，并提供了一组待进一步研究的候选物。更广泛而言，本研究展示了一种实用策略，将现有机器学习模型与第一性原理计算相结合，以系统地将大型化学空间缩减为可处理的集合，用于计算密集型材料筛选。
英文摘要
We develop a machine learning assisted workflow for the rapid screening of permanent magnet candidates within the CaCu$_5$ structure family. We generated more than 60,000 structures through substitutions. These compositions were screened using a combination of pretrained materials models, Materials Project data, and a machine-learning model for magnetization prediction. Four criteria, namely, metallicity, negative formation energy, energy above the convex hull $\leq 50$~meV/atom, and predicted magnetization above the adopted $\geq 0.75$~T threshold reduced the initial chemical space to 439 candidates for first-principles calculations. Density functional theory calculations revealed that 159 compounds exhibit positive axial magnetic anisotropy energy, favoring [001] over the tested in-plane directions. The resulting dataset reveals chemical trends in magnetic anisotropy across the CaCu$_5$ compositional space and provides a set of candidates for further investigation. More broadly, this work demonstrates a practical strategy for combining existing machine learning models with first-principles calculations to systematically reduce large chemical spaces to tractable sets for computationally intensive materials screening.
