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

Gubaev, KonstantinIkeda, YujiTasnádi, FerencNeugebauer, JörgShapeev, Alexander V.Grabowski, BlazejKörmann, Fritz2022-08-242022-08-2420212475-99531815189967http://nbn-resolving.de/urn:nbn:de:bsz:93-opus-ds-123171http://elib.uni-stuttgart.de/handle/11682/12317http://dx.doi.org/10.18419/opus-12300An 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).eninfo:eu-repo/semantics/openAccess530Finite-temperature interplay of structural stability, chemical complexity, and elastic properties of bcc multicomponent alloys from ab initio trained machine-learning potentialsarticle