ausblenden:
Schlagwörter:
Elasticity; Hafnium alloys; Machine learning; Molecular dynamics; Temperature distribution; Titanium alloys; Zircaloy, Body-centered cubic; Chemical complexity; Finite temperatures; Interatomic potential; Molecular dynamics simulations; Multi-component alloy; Structural stabilities; Temperature dependence, Chemical stability
Zusammenfassung:
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.
