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

Gubaev, K. and Ikeda, Y. and Tasnádi, F. and Neugebauer, J. and Shapeev, A.V. and Grabowski, B. and Körmann, F.

Volume: 5 Pages:
DOI: 10.1103/PhysRevMaterials.5.073801
Published: 2021

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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