Finite-temperature interplay of structural stability, chemical complexity, and elastic properties of bcc multicomponent alloys from ab initio trained machine-learning potentials
Konstantin Gubaev (University of Stuttgart)
Yuji Ikeda (University of Stuttgart)
Ferenc Tasnádi (Linköping University)
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, TU Delft - Team Marcel Sluiter)
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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).