Publications Repository - Helmholtz-Zentrum Dresden-Rossendorf
1 PublicationData-driven magneto-elastic predictions with scalable classical spin-lattice dynamics
Nikolov, S.; Wood, M. A.; Cangi, A.; Maillet, J.-B.; Marinica, M.-C.; Thompson, A. P.; Desjarlais, M. P.; Tranchida, J.
Abstract
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 α-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.
Keywords: magnetism; molecular dynamics; spin dynamics; lattice dynamics; density functional theory; machine learning; interatomic potentials
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npj Computational Materials 7(2021), 153
DOI: 10.1038/s41524-021-00617-2
Cited 21 times in Scopus
Permalink: https://www.hzdr.de/publications/Publ-32248