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

Konstantin Gubaev, Yuji Ikeda, Ferenc Tasnádi, Jörg Neugebauer, Alexander V. Shapeev, Blazej Grabowski, Fritz Körmann

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)
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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).

Original languageEnglish
Article number073801
Number of pages10
JournalPhysical Review Materials
Volume5
Issue number7
DOIs
Publication statusPublished - 2021

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