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dc.contributor.authorTantardini, Christian
dc.contributor.authorKotykhov, Alexey S.
dc.contributor.authorGubaev, Konstantin
dc.contributor.authorHodapp, Max
dc.contributor.authorShapeev, Alexander V.
dc.contributor.authorNovikov, van S.
dc.date.accessioned2024-01-04T10:52:55Z
dc.date.available2024-01-04T10:52:55Z
dc.date.issued2023-11-13
dc.description.abstractWe propose a machine-learning interatomic potential for multi-component magnetic materials. In this potential we consider magnetic moments as degrees of freedom (features) along with atomic positions, atomic types, and lattice vectors. We create a training set with constrained DFT (cDFT) that allows us to calculate energies of confgurations with non-equilibrium (excited) magnetic moments and, thus, it is possible to construct the training set in a wide confguration space with great variety of non-equilibrium atomic positions, magnetic moments, and lattice vectors. Such a training set makes possible to ft reliable potentials that will allow us to predict properties of confgurations in the excited states (including the ones with non-equilibrium magnetic moments). We verify the trained potentials on the system of bcc Fe–Al with diferent concentrations of Al and Fe and diferent ways Al and Fe atoms occupy the supercell sites. Here, we show that the formation energies, the equilibrium lattice parameters, and the total magnetic moments of the unit cell for diferent Fe–Al structures calculated with machine-learning potentials are in good correspondence with the ones obtained with DFT. We also demonstrate that the theoretical calculations conducted in this study qualitatively reproduce the experimentally-observed anomalous volume-composition dependence in the Fe–Al system.en_US
dc.identifier.citationTantardini. Constrained DFT-based magnetic machine-learning potentials for magnetic alloys: a case study of Fe–Al. Scientific Reports. 2023;13(1)en_US
dc.identifier.cristinIDFRIDAID 2207552
dc.identifier.doi10.1038/s41598-023-46951-x
dc.identifier.issn2045-2322
dc.identifier.urihttps://hdl.handle.net/10037/32309
dc.language.isoengen_US
dc.publisherSpringer Natureen_US
dc.relation.journalScientific Reports
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2023 The Author(s)en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0en_US
dc.rightsAttribution 4.0 International (CC BY 4.0)en_US
dc.titleConstrained DFT-based magnetic machine-learning potentials for magnetic alloys: a case study of Fe–Alen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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Attribution 4.0 International (CC BY 4.0)
Except where otherwise noted, this item's license is described as Attribution 4.0 International (CC BY 4.0)