Structure-property predictions in metallic glasses: insights from data-driven atomistic simulations

Abstract

The field of metallic glasses has been an active area of research owing to the complex structure–property correlations and intricacies surrounding glass formation and relaxation. This review provides a thorough examination of significant works that elucidate the structure–property correlations of metallic glasses, derived from detailed atomistic simulations coupled with data-driven approaches. The review starts with the theoretical and fundamental framework for understanding important properties of metallic glasses such as transition temperatures, relaxation phenomena, the potential energy landscape, structural features such as soft spots and shear transformation zones, atomic stiffness and structural correlations. The need to understand these concepts for leveraging metallic glasses for a wide range of applications such as performance under tensile loading, viscoelastic properties, relaxation behavior and shock loading is also elucidated. Finally, the use of machine learning algorithms in predicting the properties of metallic glasses along with their applications, limitations and scope for future work is presented.