期刊
NPJ COMPUTATIONAL MATERIALS
卷 7, 期 1, 页码 -出版社
NATURE PORTFOLIO
DOI: 10.1038/s41524-021-00617-2
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资金
- U.S. Department of Energy's National Nuclear Security Administration [DE-NA0003525]
- Center for Advanced Systems Understanding (CASUS) - German Federal Ministry of Education and Research (BMBF)
- Saxon State Ministry for Science, Art, and Tourism (SMWK)
A data-driven framework is presented for building magneto-elastic machine-learning interatomic potentials (ML-IAPs) for large-scale spin-lattice dynamics simulations. The combined potential energy surface yields excellent agreement with first-principles magneto-elastic calculations and quantitative predictions of diverse materials properties across the ferromagnetic-paramagnetic phase transition. The efficacy of this data-driven framework is demonstrated across magneto-structural phase transitions by generating a magneto-elastic ML-IAP for alpha-iron.
A data-driven framework is presented for building magneto-elastic machine-learning interatomic potentials (ML-IAPs) for large-scale spin-lattice dynamics simulations. The magneto-elastic ML-IAPs are constructed by coupling a collective atomic spin model with an ML-IAP. Together they represent a potential energy surface from which the mechanical forces on the atoms and the precession dynamics of the atomic spins are computed. Both the atomic spin model and the ML-IAP are parametrized on data from first-principles calculations. We demonstrate the efficacy of our data-driven framework across magneto-structural phase transitions by generating a magneto-elastic ML-IAP for alpha-iron. The combined potential energy surface yields excellent agreement with first-principles magneto-elastic calculations and quantitative predictions of diverse materials properties including bulk modulus, magnetization, and specific heat across the ferromagnetic-paramagnetic phase transition.
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