Molecular dynamics simulation and machine learning to predict mechanical behavior of Cu/Zr multilayer nanofilms under tension-compression

This study utilizes molecular dynamics simulations to examine the mechanical response of Cu/Zr multilayer nanofilms under tension and compression deformation with the assistance of machine learning. The results demonstrate slip behavior during the tensile process, occurring exclusively in the Cu film, and phase transformation during the compression process, occurring solely in the Zr film. Additionally, this study investigates the effects of temperature, layer thickness, and strain rate on dislocation evolution within nanofilms. This study reveals that lattice disorder in Cu/Zr nanofilms mitigates the impact of external conditions by inhibiting the reverse movement of dislocations. Temperature and strain rate significantly affect the mechanical behavior, while the number of layers is negligible. Therefore, temperature and strain rate primarily influence plastic deformation in Cu/Zr nanofilms. Additionally, the research elucidates how temperature, strain rates, and layer configuration contribute to lattice disorder. These findings offer novel insights into the mechanical characteristics and deformation mechanisms of Cu/Zr at the atomic scale.