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Journal Articles npj Computational Materials Year : 2022

Quantum-accurate magneto-elastic predictions with classical spin-lattice dynamics

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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.
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Dates and versions

cea-03651978 , version 1 (26-04-2022)

Licence

Attribution - CC BY 4.0

Identifiers

Cite

Svetoslav Nikolov, Mitchell A. Wood, Atilla Canggi, Jean-Bernanrd Maillet, Mihai Cosmin Marinica, et al.. Quantum-accurate magneto-elastic predictions with classical spin-lattice dynamics. npj Computational Materials, 2022, 7, pp.153. ⟨10.1038/s41524-021-00617-2⟩. ⟨cea-03651978⟩
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