Constrained DFT-based magnetic machine-learning potentials for magnetic alloys: a case study of Fe–Al
Abstract We 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) th...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-11-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-46951-x |
| _version_ | 1797559473569529856 |
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| author | Alexey S. Kotykhov Konstantin Gubaev Max Hodapp Christian Tantardini Alexander V. Shapeev Ivan S. Novikov |
| author_facet | Alexey S. Kotykhov Konstantin Gubaev Max Hodapp Christian Tantardini Alexander V. Shapeev Ivan S. Novikov |
| author_sort | Alexey S. Kotykhov |
| collection | DOAJ |
| description | Abstract We 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 configurations with non-equilibrium (excited) magnetic moments and, thus, it is possible to construct the training set in a wide configuration space with great variety of non-equilibrium atomic positions, magnetic moments, and lattice vectors. Such a training set makes possible to fit reliable potentials that will allow us to predict properties of configurations 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 different concentrations of Al and Fe and different 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 different 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. |
| first_indexed | 2024-03-10T17:46:59Z |
| format | Article |
| id | doaj.art-7a56fdc1b6f84dafbbf837586c72bb70 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-10T17:46:59Z |
| publishDate | 2023-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-7a56fdc1b6f84dafbbf837586c72bb702023-11-20T09:30:47ZengNature PortfolioScientific Reports2045-23222023-11-0113111010.1038/s41598-023-46951-xConstrained DFT-based magnetic machine-learning potentials for magnetic alloys: a case study of Fe–AlAlexey S. Kotykhov0Konstantin Gubaev1Max Hodapp2Christian Tantardini3Alexander V. Shapeev4Ivan S. Novikov5Skolkovo Institute of Science and Technology, Skolkovo Innovation CenterUniversity of StuttgartMaterials Center Leoben Forschung GmbH (MCL)Hylleraas Center, Department of Chemistry, UiT The Arctic University of NorwaySkolkovo Institute of Science and Technology, Skolkovo Innovation CenterSkolkovo Institute of Science and Technology, Skolkovo Innovation CenterAbstract We 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 configurations with non-equilibrium (excited) magnetic moments and, thus, it is possible to construct the training set in a wide configuration space with great variety of non-equilibrium atomic positions, magnetic moments, and lattice vectors. Such a training set makes possible to fit reliable potentials that will allow us to predict properties of configurations 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 different concentrations of Al and Fe and different 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 different 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.https://doi.org/10.1038/s41598-023-46951-x |
| spellingShingle | Alexey S. Kotykhov Konstantin Gubaev Max Hodapp Christian Tantardini Alexander V. Shapeev Ivan S. Novikov Constrained DFT-based magnetic machine-learning potentials for magnetic alloys: a case study of Fe–Al Scientific Reports |
| title | Constrained DFT-based magnetic machine-learning potentials for magnetic alloys: a case study of Fe–Al |
| title_full | Constrained DFT-based magnetic machine-learning potentials for magnetic alloys: a case study of Fe–Al |
| title_fullStr | Constrained DFT-based magnetic machine-learning potentials for magnetic alloys: a case study of Fe–Al |
| title_full_unstemmed | Constrained DFT-based magnetic machine-learning potentials for magnetic alloys: a case study of Fe–Al |
| title_short | Constrained DFT-based magnetic machine-learning potentials for magnetic alloys: a case study of Fe–Al |
| title_sort | constrained dft based magnetic machine learning potentials for magnetic alloys a case study of fe al |
| url | https://doi.org/10.1038/s41598-023-46951-x |
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