Data-driven magneto-elastic predictions with scalable classical spin-lattice dynamics
| Autor: | Mitchell Wood, Mihai-Cosmin Marinica, Jean-Bernard Maillet, Michael P. Desjarlais, Aidan P. Thompson, Attila Cangi, Svetoslav Nikolov, Julien Tranchida |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Physics
Coupling Phase transition Bulk modulus Condensed matter physics Spins Dynamics (mechanics) Computer Science Applications Magnetization QA76.75-76.765 Mechanics of Materials Modeling and Simulation Potential energy surface Precession TA401-492 General Materials Science Physics::Atomic Physics Computer software Materials of engineering and construction. Mechanics of materials |
| Zdroj: | npj Computational Materials, Vol 7, Iss 1, Pp 1-12 (2021) |
| ISSN: | 2057-3960 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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