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[2602.18213] Machine-learning force-field models for dynamical simulations of metallic magnets
Summary
This article reviews advancements in machine learning force-field models for simulating spin dynamics in metallic magnets, emphasizing scalability and accuracy.
Why It Matters
The integration of machine learning in simulating complex magnetic systems represents a significant advancement in computational physics, enabling researchers to explore novel phenomena in spintronics and improve the understanding of nonequilibrium dynamics in materials.
Key Takeaways
- Machine learning models enhance the accuracy of spin dynamics simulations.
- The study focuses on the scalability and transferability of these models.
- Novel phenomena in spin order and phase separation dynamics are revealed through ML simulations.
- Symmetry-aware descriptors improve the predictive capabilities of the models.
- The findings contribute to the development of versatile tools for studying itinerant magnets.
Condensed Matter > Strongly Correlated Electrons arXiv:2602.18213 (cond-mat) [Submitted on 20 Feb 2026] Title:Machine-learning force-field models for dynamical simulations of metallic magnets Authors:Gia-Wei Chern, Yunhao Fan, Sheng Zhang, Puhan Zhang View a PDF of the paper titled Machine-learning force-field models for dynamical simulations of metallic magnets, by Gia-Wei Chern and 3 other authors View PDF HTML (experimental) 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. Comments: Subje...