Machine learning nonequilibrium electron forces for spin dynamics of itinerant magnets

Journal Article · 03 March 2023 · npj Computational Materials

DOI: https://doi.org/10.1038/s41524-023-00990-0 · OSTI ID:1959879

Zhang, Puhan ^[1]; Chern, Gia-Wei ^[1]

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1. Univ. of Virginia, Charlottesville, VA (United States)

Here we present a generalized potential theory for conservative as well as nonconservative forces for the Landau-Lifshitz magnetization dynamics. Importantly, this formulation makes possible an elegant generalization of the Behler-Parrinello machine learning (ML) approach, which is a cornerstone of ML-based quantum molecular dynamics methods, to the modeling of force fields in adiabatic spin dynamics of out-of-equilibrium itinerant magnetic systems. We demonstrate our approach by developing a deep-learning neural network that successfully learns the electron-mediated exchange fields in a driven s-d model computed from the nonequilibrium Green’s function method. We show that dynamical simulations with forces predicted from the neural network accurately reproduce the voltage-driven domain-wall propagation. Our work also lays the foundation for ML modeling of spin transfer torques and opens a avenue for ML-based multi-scale modeling of nonequilibrium dynamical phenomena in itinerant magnets and spintronics.

• Research Organization: Univ. of Virginia, Charlottesville, VA (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Basic Energy Sciences (BES); USDOE
• Grant/Contract Number: SC0020330
• OSTI ID: 1959879
• Alternate ID(s): OSTI ID: 2421545
• Journal Information: npj Computational Materials, Vol. 9, Issue 1; ISSN 2057-3960
• Publisher: Nature Publishing GroupCopyright Statement
• Country of Publication: United States
• Language: English