Machine-learning interatomic potential for W-Mo alloys

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http://hdl.handle.net/10138/333993

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Nikoulis , G , Byggmästar , J , Kioseoglou , J , Nordlund , K & Djurabekova , F 2021 , ' Machine-learning interatomic potential for W-Mo alloys ' , Journal of Physics. Condensed Matter , vol. 33 , no. 31 , 315403 . https://doi.org/10.1088/1361-648X/ac03d1

Title: Machine-learning interatomic potential for W-Mo alloys
Author: Nikoulis, Georgios; Byggmästar, Jesper; Kioseoglou, Joseph; Nordlund, Kai; Djurabekova, Flyura
Contributor: University of Helsinki, Department of Physics
University of Helsinki, Department of Physics
University of Helsinki, Department of Physics
University of Helsinki, Department of Physics
Date: 2021-08-04
Language: eng
Number of pages: 11
Belongs to series: Journal of Physics. Condensed Matter
ISSN: 0953-8984
URI: http://hdl.handle.net/10138/333993
Abstract: In this work, we develop a machine-learning interatomic potential for WxMo1−x random alloys. The potential is trained using the Gaussian approximation potential framework and density functional theory data produced by the Vienna ab initio simulation package. The potential focuses on properties such as elastic properties, melting, and point defects for the whole range of WxMo1−x compositions. Moreover, we use all-electron density functional theory data to fit an adjusted Ziegler–Biersack–Littmarck potential for the short-range repulsive interaction. We use the potential to investigate the effect of alloying on the threshold displacement energies and find a significant dependence on the local chemical environment and element of the primary recoiling atom.
Subject: 114 Physical sciences
interatomic potential
machine learning
tungsten
molybdenum
alloys
THRESHOLD DISPLACEMENT ENERGIES
MOLECULAR-DYNAMICS
METALS
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