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

Chern, Gia-Wei [Univ. of Virginia, Charlottesville, VA (United States)] (ORCID:0000000257737850); Fan, Yunhao [Univ. of Virginia, Charlottesville, VA (United States)] (ORCID:0009000760641271); Zhang, Sheng [Univ. of Virginia, Charlottesville, VA (United States)] (ORCID:0000000258088709); Zhang, Puhan [Univ. of Virginia, Charlottesville, VA (United States)]

doi:10.1063/9.0001056

DOE OSTI · 3232004

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

Chern, Gia-Wei [Univ. of Virginia, Charlottesville, VA (United States)] (ORCID:0000000257737850)·Fan, Yunhao [Univ. of Virginia, Charlottesville, VA (United States)] (ORCID:0009000760641271)·Zhang, Sheng [Univ. of Virginia, Charlottesville, VA (United States)] (ORCID:0000000258088709)·Zhang, Puhan [Univ. of Virginia, Charlottesville, VA (United States)]

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Abstract

We review recent advances in machine-learning (ML) force-field methods for Landau–Lifshitz–Gilbert simulations of itinerant electron magnets, focusing on their scalability and transferability. Built on the principle of locality, a deep neural-network model is developed to efficiently and accurately predict 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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Artificial neural networks Chirality Computational physics Computer simulation Exchange interactions Geometric frustration Landau-Lifshitz-Gilbert equation Machine learning Magnetic ordering Spintronics

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Chern, Gia-Wei [Univ. of Virginia, Charlottesville, VA (United States)] (ORCID:0000000257737850), Fan, Yunhao [Univ. of Virginia, Charlottesville, VA (United States)] (ORCID:0009000760641271), Zhang, Sheng [Univ. of Virginia, Charlottesville, VA (United States)] (ORCID:0000000258088709), Zhang, Puhan [Univ. of Virginia, Charlottesville, VA (United States)]. 2026-02-12. Machine-learning force-field models for dynamical simulations of metallic magnets. https://doi.org/10.1063/9.0001056

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