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  Thermodynamics up to the melting point in a TaVCrW high entropy alloy: Systematic ab initio study aided by machine learning potentials

Zhou, Y., Srinivasan, P., Körmann, F., Grabowski, B., Smith, R., Goddard, P., et al. (2022). Thermodynamics up to the melting point in a TaVCrW high entropy alloy: Systematic ab initio study aided by machine learning potentials. Physical Review B, 105(21): 214302. doi:10.1103/PhysRevB.105.214302.

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American Physical Society




Zhou, Ying1, Author
Srinivasan, Prashanth2, Author           
Körmann, Fritz3, 4, Author           
Grabowski, Blazej5, Author           
Smith, Roger1, Author
Goddard, Pooja1, Author
Duff, Andrew Ian6, Author           
1School of Science, Loughborough University, LE11 3TU, United Kingdom, ou_persistent22              
2Institute of Materials Science, University of Stuttgart, Pfaffenwaldring 55, D-70569 Stuttgart, Germany, ou_persistent22              
3Department of Materials Science and Engineering, Delft University of Technology, Mekelweg 2, 2628 CD Delft, The Netherlands, ou_persistent22              
4Computational Materials Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society, ou_1863337              
5Institute of Materials Science, University of Stuttgart, Pfaffenwaldring 55, Stuttgart, 70569, Germany, ou_persistent22              
6Scientific Computing Department, STFC Daresbury Laboratory, Hartree Centre, Warrington, UK, ou_persistent22              


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 Abstract: Multi-principal-component alloys have attracted great interest as a novel paradigm in alloy design, with often unique properties and a vast compositional space auspicious for materials discovery. High entropy alloys (HEAs) belong to this class and are being investigated for prospective nuclear applications with reported superior mechanical properties including high-temperature strength and stability compared to conventional alloys. Computational materials design has the potential to play a key role in screening such alloys, yet for high-temperature properties, challenges remain in finding an appropriate balance between accuracy and computational cost. Here we develop an approach based on density-functional theory (DFT) and thermodynamic integration aided by machine learning based interatomic potential models to address this challenge. We systematically evaluate and compare the efficiency of computing the full free energy surface and thermodynamic properties up to the melting point at different stages of the thermodynamic integration scheme. Our new approach provides a ??4 speed-up with respect to comparable free energy approaches at the level of DFT, with errors on high-temperature free energy predictions less than 1 meV/atom. Calculations are performed on an equiatomic HEA, TaVCrW???a low-activation composition and therefore of potential interest for next generation fission and fusion reactors.


Language(s): eng - English
 Dates: 2022-06-10
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1103/PhysRevB.105.214302
 Degree: -



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Project name : This work was supported by United Kingdom EPSRC Grant No. EP/S032819/1 and EP/S032835/1, Loughborough's High Performance Computing unit and ARCHER UK National Supercomputing Service. Via our membership of the UK's HEC Materials Chemistry Consortium, which is funded by EPSRC (EP/L000202), this work used the UK Materials and Molecular Modelling Hub for computational resources, MMM Hub, which is partially funded by EPSRC (EP/P020194 and EP/T022213). P.S. would like to thank the Alexander von Humboldt Foundation for their support through the Alexander von Humboldt Postdoctoral Fellowship Program. F.K. acknowledges support by the Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO) (VIDI Grant No. 15707). B.G. acknowledges funding from the European Research Council (ERC) under the European Unions Horizon 2020 research and innovation programme (Grant Agreement No. No 865855) and support by the Stuttgart Centre for Simulation Science (SimTech). F.K. and B.G. acknowledge support from the collaborative DFG-RFBR Grant (Grants No. DFG KO 5080/3-1, No. DFG GR 3716/6-1).
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Source 1

Title: Physical Review B
  Abbreviation : Phys. Rev. B
Source Genre: Journal
Publ. Info: Woodbury, NY : American Physical Society
Pages: 14 Volume / Issue: 105 (21) Sequence Number: 214302 Start / End Page: - Identifier: ISSN: 1098-0121
CoNE: https://pure.mpg.de/cone/journals/resource/954925225008