Machine-learning modeling of magnetization dynamics in quasi-equilibrium and driven metallic spin systems
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. A central ingredient of this framework is the implementation of symmetry-aware magnetic descriptors based on group-theoretical bispectrum formalisms. Leveraging these ML force fields, LLG simulations faithfully reproduce hal...
| Authors | Gia-Wei Chern, Yunhao Fan, Sheng Zhang et al. |
|---|---|
| Primary category | cond-mat.str-el |
| Published | 13 April 2026 |
| Last updated at source | 13 April 2026 |
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