用机器学习建模电子自旋动力学，高效模拟金属磁体新现象。
Machine-learning force-field models for dynamical simulations of metallic magnets
- 基于深度神经网络和对称性描述符，构建可迁移的力场模型。
- 在三角晶格上发现四面体自旋序异常粗化，在方格子中观察到相分离冻结。
- 适合研究强关联自旋电子系统中的非平衡态行为。
我们综述了机器学习（ML）力场方法在巡游电子磁体中用于朗道-利夫希茨-吉尔伯特（LLG）模拟的最新进展，重点关注可扩展性与泛化能力。基于局域性原理，开发了一种深度神经网络模型，可高效准确地预测驱动自旋动态的电子媒介力。通过群论方法构建的对称性感知描述符，严格包含晶格对称性和自旋旋转对称性。该框架以广泛应用于自旋电子学的典型s-d交换模型为例进行验证。基于机器学习的大规模模拟揭示了新型非平衡现象，包括在三角晶格上的四面体自旋序异常粗化，以及轻掺杂空穴、强耦合方格子系统中相分离动力学的冻结。这些结果确立了机器学习力场框架在巡游磁体非平衡自旋动力学建模中的可扩展性、高精度与多功能性。
原文摘要 · Abstract (English)
We review recent advances in machine learning (ML) force-field methods for Landau-Lifshitz-Gilbert (LLG) simulations of itinerant electron magnets, focusing on scalability and transferability. Built on the principle of locality, a deep neural network model is developed to efficiently and accurately predict the electron-mediated forces governing spin dynamics. Symmetry-aware descriptors constructed through a group-theoretical approach ensure rigorous incorporation of both lattice and spin-rotation symmetries. The framework is demonstrated using the prototypical s-d exchange model widely employed in spintronics. ML-enabled large-scale simulations reveal novel nonequilibrium phenomena, including anomalous coarsening of tetrahedral spin order on the triangular lattice and the freezing of phase separation dynamics in lightly hole-doped, strong-coupling square-lattice systems. These results establish ML force-field frameworks as scalable, accurate, and versatile tools for modeling nonequilibrium spin dynamics in itinerant magnets.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。