npj Computational Materials (Sep 2021)

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

  • Svetoslav Nikolov,
  • Mitchell A. Wood,
  • Attila Cangi,
  • Jean-Bernard Maillet,
  • Mihai-Cosmin Marinica,
  • Aidan P. Thompson,
  • Michael P. Desjarlais,
  • Julien Tranchida

DOI
https://doi.org/10.1038/s41524-021-00617-2
Journal volume & issue
Vol. 7, no. 1
pp. 1 – 12

Abstract

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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.