mlsanityData to 7 Oct 2026

Machine learning nonequilibrium electron forces for adiabatic spin dynamics

arXiv 2112.12124 ↗First posted 22 Dec 2021cond-mat.mes-hall cond-mat.str-el cs.LGcited by 17

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Abstract (arXiv)

We present a generalized potential theory of nonequilibrium torques for the Landau-Lifshitz equation. The general formulation of exchange forces in terms of two potential energies allows for the implementation of accurate machine learning models for adiabatic spin dynamics of out-of-equilibrium itinerant magnetic systems. To demonstrate our approach, we develop a deep-learning neural network that successfully learns the forces in a driven s-d model computed from the nonequilibrium Green's function method. We show that the Landau-Lifshitz dynamics simulations with forces predicted from the neural-net model accurately reproduce the voltage-driven domain-wall propagation. Our work opens a new avenue for multi-scale modeling of nonequilibrium dynamical phenomena in itinerant magnets and spintronics based on machine-learning models.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 12 of its 106 references that are in the map. First posted on arXiv 22 Dec 2021.

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12 of 106 references.

References · 12

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2017Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanicscited by 213
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  4. 2019Unifying machine learning and quantum chemistry -- a deep neural network for molecular wavefunctionscited by 73
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  6. 2020Machine Learning for Condensed Matter Physicscited by 22
  7. 2016Accelerate Monte Carlo Simulations with Restricted Boltzmann Machinescited by 21
  8. 2020Machine learning dynamics of phase separation in correlated electron magnetscited by 19
  9. 2021Anomalous phase separation dynamics in a correlated electron system: machine-learning enabled large-scale kinetic Monte Carlo simulationscited by 18
  10. 2018Machine learning electron correlation in a disordered mediumcited by 17
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  12. 2016Modeling Tangential Vector Fields on a Spherecited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 6; 2025: 1; 2026: 7.

17 papers.

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  1. 2023Machine learning for phase ordering dynamics of charge density wavescited by 15
  2. 2024Coarsening of chiral domains in itinerant electron magnets: A machine learning force field approachcited by 10
  3. 2024Kinetics of orbital ordering in cooperative Jahn-Teller models: Machine-learning enabled large-scale simulationscited by 9
  4. 2024Machine learning force-field model for kinetic Monte Carlo simulations of itinerant Ising magnetscited by 6
  5. 2026Machine-learning force-field models for dynamical simulations of metallic magnetscited by 3
  6. 2023Equivariant Neural Networks for Spin Dynamics Simulations of Itinerant Magnetscited by 1
  7. 2023Machine learning force-field models for metallic spin glasscited by 1
  8. 2024Echo State network for coarsening dynamics of charge density wavescited by 1
  9. 2026Machine learning nonequilibrium phase transitions in charge-density wave insulatorscited by 1
  10. 2026Graph neural network force fields for adiabatic dynamics of lattice Hamiltonianscited by 1
  11. 2026Machine-learning modeling of magnetization dynamics in quasi-equilibrium and driven metallic spin systemscited by 1
  12. 2026Graph Neural Networks in the Wilson Loop Representation of Abelian Lattice Gauge Theoriescited by 1
5 more
  1. 2024Machine learning approach for vibronically renormalized electronic band structurescited by 0
  2. 2024Enhanced coarsening of charge density waves induced by electron correlation: Machine-learning enabled large-scale dynamical simulationscited by 0
  3. 2025Learning Degenerate Manifolds of Frustrated Magnets with Boltzmann Machinescited by 0
  4. 2026Magnetic HIP-NN for spin dynamics in disordered itinerant magnetscited by 0
  5. 2026Graph Neural Network Force Fields for Spin Dynamics in Metallic Magnetscited by 0