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Journal Article

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

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Neugebauer,  Jörg
Computational Materials Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;

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Körmann,  Fritz
Department of Materials Science and Engineering, Delft University of Technology, Mekelweg 2, 2628 CD Delft, The Netherlands;
Computational Phase Studies, Computational Materials Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;

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Citation

Gubaev, K., Ikeda, Y., Tasnádi, F., Neugebauer, J., Shapeev, A., Grabowski, B., et al. (2021). Finite-temperature interplay of structural stability, chemical complexity, and elastic properties of bcc multicomponent alloys from ab initio trained machine-learning potentials. Physical Review Materials, 5(7): 073801. doi:10.1103/PhysRevMaterials.5.073801.


Cite as: https://hdl.handle.net/21.11116/0000-0009-028B-8
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). © 2021 American Physical Society.