Influence of group IV element on basic mechanical properties of BCC medium-entropy alloys using machine-learning potentials

Lobzenko, I.; 都留 智仁; 他2名*

Computational Materials Science, 219, p.112010_1 - 112010_9, 2023/02

https://doi.org/10.1016/j.commatsci.2023.112010

To elucidate the origin of excellent mechanical properties of high-entropy alloys (HEA), it is essential to develop the atomic-level depiction of defect structures considering the effects of the constituent elements. While classical molecular dynamics have been one of the most effective tools for understanding the defect structures from the atomic level, there is still a problem with the accuracy of the inter-atomic potential of complicated alloy systems such as HEA. A new technique for building such potentials based on machine learning was recently developed. We employed the technique and constructed highly accurate potentials with good robustness for two BCC medium-entropy alloys: MoNbTa and ZrNbTa. Atomic simulation based on the new potentials indicate significant differences in the fundamental mechanical properties of two alloys, depending on the constituent elements, that dominate deformation behavior.