AI/ML 磁性材料计算搜索的可靠性：数据库、验证与可合成性

中文摘要 AI 辅助

AI/ML 方法在材料发现领域的快速发展，提高了计算数据库的质量、物理完备性和信息内容的重要性。我们针对磁性材料考察了这些问题，其中竞争磁性态的不完全采样、化学无序和有限温度性质可能导致看似准确但物理上不可靠的预测。我们提出了一种分层发现工作流，将独立的 ML 模型与显式的电子结构、磁性、动力学、热力学、动力学和实验验证相结合。为了将动力学可达性纳入高通量筛选，我们引入了一个基于化学有序-无序温度与 Tammann 温度之间关系的无量纲合成窗口描述符。对 Fe-Co-B 的簇展开分析说明了这一判据的重要性：尽管有序的 Fe3CoB2 在 T=0 时能量上有利，但其较小的有序能导致有序-无序温度远低于室温，使得长程 Fe/Co 有序在动力学上不可实现。这些结果表明，可靠的 AI/ML 磁性材料发现需要包含与目标功能相关的物理信息的数据库和验证程序，而不仅仅是更大数量的计算结构。

英文摘要

The rapid growth of AI/ML methods for materials discovery has increased the importance of the quality, physical completeness, and information content of computational databases. We examine these issues for magnetic materials, where incomplete sampling of competing magnetic states, chemical disorder, and finite-temperature properties can lead to apparently accurate but physically unreliable predictions. We propose a hierarchical discovery workflow combining independent ML models with explicit electronic-structure, magnetic, dynamical, thermodynamic, kinetic, and experimental validation. To incorporate kinetic accessibility into high-throughput screening, we introduce a dimensionless synthesis-window descriptor based on the relation between the chemical order-disorder and Tammann temperatures. A cluster-expansion analysis of Fe-Co-B illustrates the importance of this criterion: although ordered Fe3CoB2 is energetically favored at T=0, its small ordering energy produces an order-disorder temperature far below room temperature, making long-range Fe/Co ordering kinetically unachievable. These results demonstrate that reliable AI/ML discovery of magnetic materials requires databases and validation procedures that contain physical information relevant to the target functionality, rather than simply larger numbers of calculated structures.

发表机构

• Iowa State University（爱荷华州立大学）
• University of Nebraska-Lincoln（内布拉斯加大学林肯分校）
• Ames National Laboratory, U.S. Department of Energy（美国能源 Ames 国家实验室）

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