Data-driven magneto-elastic predictions with scalable classical spin-lattice dynamics

Data-driven magneto-elastic predictions with scalable classical spin-lattice dynamics

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 represe...

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Bibliographic Details
Main Authors: Svetoslav Nikolov, Mitchell A. Wood, Attila Cangi, Jean-Bernard Maillet, Mihai-Cosmin Marinica, Aidan P. Thompson, Michael P. Desjarlais, Julien Tranchida
Format: Article
Language:English
Published: Nature Publishing Group 2021-09-01
Series:npj Computational Materials
Online Access:https://doi.org/10.1038/s41524-021-00617-2

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https://doi.org/10.1038/s41524-021-00617-2

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