A transferable machine-learning framework linking interstice distribution and plastic heterogeneity in metallic glasses

Abstract

Understanding plastic deformation in metallic glasses is challenging due to their heterogeneous atomic environments. Here the authors propose a machine learning approach generalizable across compositions to predict the structural features from which plastic deformation is initiated in a metallic glass.