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  Finite-temperature interplay of structural stability, chemical complexity, and elastic properties of bcc multicomponent alloys from ab initio trained machine-learning potentials

Gubaev, K., Ikeda, Y., Tasnádi, F., Neugebauer, J., Shapeev, A., Grabowski, B., et al. (2021). Finite-temperature interplay of structural stability, chemical complexity, and elastic properties of bcc multicomponent alloys from ab initio trained machine-learning potentials. Physical Review Materials, 5(7): 073801. doi:10.1103/PhysRevMaterials.5.073801.

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 Creators:
Gubaev, Konstantin1, 2, Author              
Ikeda, Yuji3, Author              
Tasnádi, Ferenc4, Author              
Neugebauer, Jörg5, Author              
Shapeev, Alexander6, Author              
Grabowski, Blazej7, Author              
Körmann, Fritz8, 9, Author              
Affiliations:
1Materials Science and Engineering, Delft University of Technology, 2628CD Delft, The Netherlands, ou_persistent22              
2Institute for Materials Science, University of Stuttgart, Pfaffenwaldring 55, Stuttgart, 70569, Germany, ou_persistent22              
3Institute of Materials Science, University of Stuttgart, Pfaffenwaldring 55, D-70569 Stuttgart, Germany, ou_persistent22              
4Department of Physics, Chemistry and Biology, Linköping University Linköping, Sweden, ou_persistent22              
5Computational Materials Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society, ou_1863337              
6Skolkovo Institute of Science and Technology, Skolkovo Innovation Center, Nobel St. 3, Moscow, 143026, Russian Federation, ou_persistent22              
7Institute of Materials Science, University of Stuttgart, Pfaffenwaldring 55, Stuttgart, 70569, Germany, ou_persistent22              
8Department of Materials Science and Engineering, Delft University of Technology, Mekelweg 2, 2628 CD Delft, The Netherlands, ou_persistent22              
9Computational Phase Studies, Computational Materials Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society, ou_1863341              

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Free keywords: Elasticity; Hafnium alloys; Machine learning; Molecular dynamics; Temperature distribution; Titanium alloys; Zircaloy, Body-centered cubic; Chemical complexity; Finite temperatures; Interatomic potential; Molecular dynamics simulations; Multi-component alloy; Structural stabilities; Temperature dependence, Chemical stability
 Abstract: An active learning approach to train machine-learning interatomic potentials (moment tensor potentials) for multicomponent alloys to ab initio data is presented. Employing this approach, the disordered body-centered cubic (bcc) TiZrHfTax system with varying Ta concentration is investigated via molecular dynamics simulations. Our results show a strong interplay between elastic properties and the structural ω phase stability, strongly affecting the mechanical properties. Based on these insights we systematically screen composition space for regimes where elastic constants show little or no temperature dependence (elinvar effect). © 2021 American Physical Society.

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Language(s): eng - English
 Dates: 2021-07-08
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1103/PhysRevMaterials.5.073801
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Project name : Collaborative DFG-RFBR Grant (Grants No. DFG KO 5080/3-1, No. DFG GR 3716/6-1, and No. RFBR 20-53-12012) European Research Council (ERC) under the EU’s Horizon 2020 Research and Innovation Programme (Grant No. 865855) DFG priority program (SPP 2006), DFG Grant No. 405621160 NWO/STW (VIDI Grant No.15707)
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Title: Physical Review Materials
  Abbreviation : Phys. Rev. Mater.
Source Genre: Journal
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Publ. Info: College Park, MD : American Physical Society
Pages: 10 Volume / Issue: 5 (7) Sequence Number: 073801 Start / End Page: - Identifier: ISSN: 2475-9953
CoNE: https://pure.mpg.de/cone/journals/resource/2475-9953