AI RESEARCH
Machine-learning modeling of magnetization dynamics in quasi-equilibrium and driven metallic spin systems
arXiv CS.LG
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ArXi:2604.11513v1 Announce Type: cross We review recent advances in machine-learning (ML) force-field methods for large-scale Landau-Lifshitz-Gilbert (LLG) simulations of metallic spin systems. We generalize the Behler-Parrinello (BP) ML architecture -- originally developed for quantum molecular dynamics -- to construct scalable and transferable ML models capable of capturing the intricate dependence of electron-mediated exchange fields on the local magnetic environment characteristic of itinerant magnets.