Machine-learning force-field models for dynamical simulations of metallic magnets
This provides scalable and accurate tools for modeling nonequilibrium spin dynamics in itinerant magnets, which is important for spintronics research, though it builds on existing methods with incremental improvements.
The authors tackled the problem of simulating nonequilibrium spin dynamics in itinerant electron magnets by developing a machine learning force-field model, which enabled large-scale simulations that revealed novel phenomena such as anomalous coarsening of tetrahedral spin order and freezing of phase separation dynamics.
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.