TY - JOUR
T1 - Finite-temperature interplay of structural stability, chemical complexity, and elastic properties of bcc multicomponent alloys from ab initio trained machine-learning potentials
AU - Gubaev, Konstantin
AU - Ikeda, Yuji
AU - Tasnádi, Ferenc
AU - Neugebauer, Jörg
AU - Shapeev, Alexander V.
AU - Grabowski, Blazej
AU - Körmann, Fritz
PY - 2021
Y1 - 2021
N2 - 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).
AB - 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).
UR - http://www.scopus.com/inward/record.url?scp=85109958190&partnerID=8YFLogxK
U2 - 10.1103/PhysRevMaterials.5.073801
DO - 10.1103/PhysRevMaterials.5.073801
M3 - Article
AN - SCOPUS:85109958190
SN - 2475-9953
VL - 5
JO - Physical Review Materials
JF - Physical Review Materials
IS - 7
M1 - 073801
ER -