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  Magnetic Moment Tensor Potentials for collinear spin-polarized materials reproduce different magnetic states of bcc Fe

Novikov, I., Grabowski, B., Körmann, F., & Shapeev, A. (2022). Magnetic Moment Tensor Potentials for collinear spin-polarized materials reproduce different magnetic states of bcc Fe. npj Computational Materials, 8(1): 13. doi:10.1038/s41524-022-00696-9.

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Item Permalink: https://hdl.handle.net/21.11116/0000-000B-4EF8-7 Version Permalink: https://hdl.handle.net/21.11116/0000-000B-4EFB-4
Genre: Journal Article

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s41524-022-00696-9.pdf (Publisher version), 2MB
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2022
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 Creators:
Novikov, Ivan1, 2, Author
Grabowski, Blazej1, Author           
Körmann, Fritz3, 4, Author           
Shapeev, Alexander2, Author           
Affiliations:
1Institute of Materials Science, University of Stuttgart, Pfaffenwaldring 55, Stuttgart, 70569, Germany, ou_persistent22              
2Skolkovo Institute of Science and Technology, Skolkovo Innovation Center, Nobel St. 3, Moscow, 143026, Russian Federation, ou_persistent22              
3Computational Materials Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society, ou_1863337              
4Department of Materials Science and Engineering, Delft University of Technology, Mekelweg 2, 2628 CD Delft, The Netherlands, ou_persistent22              

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 Abstract: We present the magnetic Moment Tensor Potentials (mMTPs), a class of machine-learning interatomic potentials, accurately reproducing both vibrational and magnetic degrees of freedom as provided, e.g., from first-principles calculations. The accuracy is achieved by a two-step minimization scheme that coarse-grains the atomic and the spin space. The performance of the mMTPs is demonstrated for the prototype magnetic system bcc iron, with applications to phonon calculations for different magnetic states, and molecular-dynamics simulations with fluctuating magnetic moments.

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 Dates: 2022
 Publication Status: Published online
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 Rev. Type: Peer
 Identifiers: DOI: 10.1038/s41524-022-00696-9
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Project name : We acknowledge support from the collaborative DFG-RFBR Grant (Grants no. DFG KO 5080/3-1, DFG GR 3716/6-1, and RFBR 20-53-12012). B.G. acknowledges the support by the Stuttgart Center for Simulation Science (SimTech) and funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 865855).
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Title: npj Computational Materials
  Abbreviation : npj Comput. Mater.
Source Genre: Journal
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Publ. Info: London : Springer Nature
Pages: - Volume / Issue: 8 (1) Sequence Number: 13 Start / End Page: - Identifier: ISSN: 2057-3960
CoNE: https://pure.mpg.de/cone/journals/resource/2057-3960