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

Journal article (2021)

Authors

Konstantin Gubaev University of Stuttgart

Jörg Neugebauer Max-Planck-Institut für Eisenforschung

Alexander Shapeev Skolkovo Institute of Science and Technology

Blazej Grabowski University of Stuttgart

F.H.W. Körmann Max-Planck-Institut für Eisenforschung, Team Marcel Sluiter - Mechanical, Maritime and Materials Engineering

Research Group

Team Marcel Sluiter (Mechanical, Maritime and Materials Engineering) (TU Delft)

DOI: https://doi.org/10.1103/PhysRevMaterials.5.073801

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Published Date

2021

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Materials Science and Engineering

Research Group

Team Marcel Sluiter

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).

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