Machine learning nonequilibrium electron forces for spin dynamics of itinerant magnets  

作　　者：Puhan Zhang Gia-Wei Chern 

机构地区：[1]Department of Physics,University of Virginia,Charlottesville,VA 22904,USA

出　　处：《npj Computational Materials》2023年第1期2026-2035,共10页计算材料学（英文）

基　　金：This work was supported by the US Department of Energy Basic Energy Sciences under Award No.DE-SC0020330.The authors also acknowledge the support of Research Computing at the University of Virginia.

摘　　要：We present a generalized potential theory for conservative as well as nonconservative forces for the Landau-Lifshitz magnetizationdynamics. 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 spindynamics of out-of-equilibrium itinerant magnetic systems. We demonstrate our approach by developing a deep-learning neuralnetwork that successfully learns the electron-mediated exchange fields in a driven s-d model computed from the nonequilibriumGreen’s function method. We show that dynamical simulations with forces predicted from the neural network accurately reproducethe voltage-driven domain-wall propagation. Our work also lays the foundation for ML modeling of spin transfer torques and opens aavenue for ML-based multi-scale modeling of nonequilibrium dynamical phenomena in itinerant magnets and spintronics.

关 键 词：dynamics NONEQUILIBRIUM FORCES 

分 类 号：O441[理学—电磁学] TP3[理学—物理]