Machine learning nonequilibrium electron forces for spin dynamics of itinerant magnets
Abstract 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...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-03-01
|
| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-023-00990-0 |
| _version_ | 1797864110841397248 |
|---|---|
| author | Puhan Zhang Gia-Wei Chern |
| author_facet | Puhan Zhang Gia-Wei Chern |
| author_sort | Puhan Zhang |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-04-09T22:46:16Z |
| format | Article |
| id | doaj.art-c5e3fbfbd1074f8f89dda9937dbcc5fa |
| institution | Directory Open Access Journal |
| issn | 2057-3960 |
| language | English |
| last_indexed | 2024-04-09T22:46:16Z |
| publishDate | 2023-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Computational Materials |
| spelling | doaj.art-c5e3fbfbd1074f8f89dda9937dbcc5fa2023-03-22T11:50:28ZengNature Portfolionpj Computational Materials2057-39602023-03-019111010.1038/s41524-023-00990-0Machine learning nonequilibrium electron forces for spin dynamics of itinerant magnetsPuhan Zhang0Gia-Wei Chern1Department of Physics, University of VirginiaDepartment of Physics, University of VirginiaAbstract 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.https://doi.org/10.1038/s41524-023-00990-0 |
| spellingShingle | Puhan Zhang Gia-Wei Chern Machine learning nonequilibrium electron forces for spin dynamics of itinerant magnets npj Computational Materials |
| title | Machine learning nonequilibrium electron forces for spin dynamics of itinerant magnets |
| title_full | Machine learning nonequilibrium electron forces for spin dynamics of itinerant magnets |
| title_fullStr | Machine learning nonequilibrium electron forces for spin dynamics of itinerant magnets |
| title_full_unstemmed | Machine learning nonequilibrium electron forces for spin dynamics of itinerant magnets |
| title_short | Machine learning nonequilibrium electron forces for spin dynamics of itinerant magnets |
| title_sort | machine learning nonequilibrium electron forces for spin dynamics of itinerant magnets |
| url | https://doi.org/10.1038/s41524-023-00990-0 |
| work_keys_str_mv | AT puhanzhang machinelearningnonequilibriumelectronforcesforspindynamicsofitinerantmagnets AT giaweichern machinelearningnonequilibriumelectronforcesforspindynamicsofitinerantmagnets |