发表机构
University of Virginia（弗吉尼亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出图神经网络磁力场框架，从电子计算学习磁能泛函，高效评估自旋轨道矩，经基准测试可准确再现自旋轨道矩，为非平衡磁学大规模模拟提供新途径。
AI 中文摘要
金属磁体表现出由电子产生的相互作用主导的复杂自旋动力学。对这类动力学的预测性模拟通常需要在整个时间演化过程中反复求解潜在的电子问题，这造成了重大的计算瓶颈。在此，我们提出一种图神经网络（GNN）磁力场框架，该框架直接从电子计算中学习控制巡游电子自旋动力学的有效磁能泛函。其概念与机器学习的原子间势类似，所提框架可在保留巡游电子产生的非线性及空间扩展相互作用的同时，高效评估自旋轨道矩。我们在具有共线、非共线和非共面磁序的代表性金属磁体系统上对该方法进行基准测试。所学的力场准确再现了电子产生的自旋轨道矩，且得到的非平衡自旋动力学与直接电子模拟结果高度吻合。我们的结果确立了图神经网络作为机器学习磁力场的强大框架，为跨多个长度和时间尺度的非平衡磁学的预测性大规模模拟提供了途径。
英文摘要
Metallic magnets exhibit complex spin dynamics governed by electronically generated interactions. Predictive simulations of such dynamics typically require repeated solutions of an underlying electronic problem throughout the time evolution, creating a major computational bottleneck. Here we introduce a graph neural network (GNN) magnetic force-field framework that learns the effective magnetic energy functional governing itinerant spin dynamics directly from electronic calculations. Conceptually analogous to machine-learned interatomic potentials, the proposed framework enables efficient evaluation of spin torques while capturing the nonlinear and spatially extended interactions generated by itinerant electrons. We benchmark the method on representative metallic magnetic systems exhibiting collinear, noncollinear, and noncoplanar magnetic order. The learned force fields accurately reproduce electronically generated spin torques and yield nonequilibrium spin dynamics in excellent agreement with direct electronic simulations. Our results establish graph neural networks as a powerful framework for machine-learned magnetic force fields, providing a pathway toward predictive large-scale simulations of nonequilibrium magnetism across multiple length and time scales.
Comments15 pages, 5 figures
