Machine-learning force-field models for dynamical simulations of metallic magnets

• URL: http://arxiv.org/abs/2602.18213v1
• Date: Fri, 20 Feb 2026 13:51:29 GMT
• Title: Machine-learning force-field models for dynamical simulations of metallic magnets
• Authors: Gia-Wei Chern, Yunhao Fan, Sheng Zhang, Puhan Zhang,
• Abstract summary: We review recent advances in machine learning (ML) force-field methods for Landau-Lifshitz-Gilbert (LLG) simulations of itinerant electron magnets.<n>A deep neural network model is developed to efficiently and accurately predict the electron-mediated forces governing spin dynamics.<n>Results establish ML force-field frameworks as scalable, accurate, and versatile tools for modeling nonequilibrium spin dynamics in itinerant magnets.
• Score: 3.1567633240529616
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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