用图神经网络构建磁力场模型，加速金属磁体自旋动力学模拟。
Graph Neural Network Force Fields for Spin Dynamics in Metallic Magnets
- 用GNN学习电子计算数据，直接建模自旋能量函数。
- 模拟结果与精确电子计算高度一致，误差小且效率提升显著。
- 适合需要大规模非平衡磁性仿真的研究人员使用。
金属磁体中的复杂自旋动力学由电子诱导的相互作用主导。传统预测模拟需在时间演化中反复求解电子问题，计算成本高昂。本文提出一种基于图神经网络（GNN）的磁力场框架，直接从电子计算中学习支配巡游自旋动力学的有效磁能泛函。该方法概念上类似于机器学习的原子间势能，可在保持非线性与空间扩展相互作用的同时，高效计算自旋扭矩。我们在具有共线、非共线及非共面磁序的代表性金属磁体系上进行验证，所学力场准确再现了电子生成的自旋扭矩，并在非平衡自旋动力学上与直接电子模拟结果高度吻合。结果表明，图神经网络是构建机器学习磁力场的强大工具，为跨多尺度的非平衡磁性预测模拟提供了可行路径。
原文摘要 · Abstract (English)
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.
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