Machine-learning interatomic potential for W-Mo alloys

Giorgos Nikoulis; Jesper Byggmästar; Joseph Kioseoglou; Kai Nordlund; Flyura Djurabekova

doi:10.1088/1361-648X/ac03d1

Machine-learning interatomic potential for W-Mo alloys

J Phys Condens Matter. 2021 Jun 18;33(31). doi: 10.1088/1361-648X/ac03d1.

Authors

Giorgos Nikoulis^{1

2}, Jesper Byggmästar², Joseph Kioseoglou¹, Kai Nordlund², Flyura Djurabekova^{2

3}

Affiliations

¹ Department of Physics, Aristotle University of Thessaloniki, GR-54124 Thessaloniki, Greece.
² Department of Physics, University of Helsinki, PO Box 43, FI-00014, Finland.
³ Helsinki Institute of Physics, Helsinki, Finland.

PMID: 34020426
DOI: 10.1088/1361-648X/ac03d1

Abstract

In this work, we develop a machine-learning interatomic potential for W_xMo_1-xrandom alloys. The potential is trained using the Gaussian approximation potential framework and density functional theory data produced by the Viennaab initiosimulation package. The potential focuses on properties such as elastic properties, melting, and point defects for the whole range of W_xMo_1-xcompositions. 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.

Keywords: alloys; interatomic potential; machine learning; molybdenum; tungsten.