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...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Svetoslav Nikolov, Mitchell A. Wood, Attila Cangi, Jean-Bernard Maillet, Mihai-Cosmin Marinica, Aidan P. Thompson, Michael P. Desjarlais, Julien Tranchida
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/d348af2bd3da41768c9cb3ef7706b759
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:d348af2bd3da41768c9cb3ef7706b759
record_format	dspace
spelling	oai:doaj.org-article:d348af2bd3da41768c9cb3ef7706b7592021-12-02T19:17:04ZData-driven magneto-elastic predictions with scalable classical spin-lattice dynamics10.1038/s41524-021-00617-22057-3960https://doaj.org/article/d348af2bd3da41768c9cb3ef7706b7592021-09-01T00:00:00Zhttps://doi.org/10.1038/s41524-021-00617-2https://doaj.org/toc/2057-3960Abstract 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.Svetoslav NikolovMitchell A. WoodAttila CangiJean-Bernard MailletMihai-Cosmin MarinicaAidan P. ThompsonMichael P. DesjarlaisJulien TranchidaNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492Computer softwareQA76.75-76.765ENnpj Computational Materials, Vol 7, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
spellingShingle	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765 Svetoslav Nikolov Mitchell A. Wood Attila Cangi Jean-Bernard Maillet Mihai-Cosmin Marinica Aidan P. Thompson Michael P. Desjarlais Julien Tranchida Data-driven magneto-elastic predictions with scalable classical spin-lattice dynamics
description	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.
format	article
author	Svetoslav Nikolov Mitchell A. Wood Attila Cangi Jean-Bernard Maillet Mihai-Cosmin Marinica Aidan P. Thompson Michael P. Desjarlais Julien Tranchida
author_facet	Svetoslav Nikolov Mitchell A. Wood Attila Cangi Jean-Bernard Maillet Mihai-Cosmin Marinica Aidan P. Thompson Michael P. Desjarlais Julien Tranchida
author_sort	Svetoslav Nikolov
title	Data-driven magneto-elastic predictions with scalable classical spin-lattice dynamics
title_short	Data-driven magneto-elastic predictions with scalable classical spin-lattice dynamics
title_full	Data-driven magneto-elastic predictions with scalable classical spin-lattice dynamics
title_fullStr	Data-driven magneto-elastic predictions with scalable classical spin-lattice dynamics
title_full_unstemmed	Data-driven magneto-elastic predictions with scalable classical spin-lattice dynamics
title_sort	data-driven magneto-elastic predictions with scalable classical spin-lattice dynamics
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/d348af2bd3da41768c9cb3ef7706b759
work_keys_str_mv	AT svetoslavnikolov datadrivenmagnetoelasticpredictionswithscalableclassicalspinlatticedynamics AT mitchellawood datadrivenmagnetoelasticpredictionswithscalableclassicalspinlatticedynamics AT attilacangi datadrivenmagnetoelasticpredictionswithscalableclassicalspinlatticedynamics AT jeanbernardmaillet datadrivenmagnetoelasticpredictionswithscalableclassicalspinlatticedynamics AT mihaicosminmarinica datadrivenmagnetoelasticpredictionswithscalableclassicalspinlatticedynamics AT aidanpthompson datadrivenmagnetoelasticpredictionswithscalableclassicalspinlatticedynamics AT michaelpdesjarlais datadrivenmagnetoelasticpredictionswithscalableclassicalspinlatticedynamics AT julientranchida datadrivenmagnetoelasticpredictionswithscalableclassicalspinlatticedynamics
_version_	1718376939704549376

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

Ejemplares similares