Atomistic modeling of mechanical properties of base-centered cubic multi-component alloys

Lobzenko, I.   ; Tsuru, Tomohito   

Refractory multicomponent alloys (MCAs) have a body-centered cubic (BCC) structure and are one of the most important materials with high potential for use under severe conditions. In this study, we developed a machine learning method to calculate dislocation motion with high computational accuracy in classical molecular dynamics simulations. As a result, it was found that the dislocation exhibits a peculiar slip surface, which is different from the slip surfaces that occur in metals with the general BCC structure. In the presentation, we also report the effect of the supervised machine learning dataset on the reliability of the machine learning potential.