Predicting shear transformation events in glasses via energy landscape sampling*

Shear transformation (ST) events, as the elementary process for plastic deformation of glasses, are of vital importance to understand the mechanical behavior of glasses. Here, by characterizing first-order saddle points in the potential energy landscape, we develop a framework to characterize and to predict the triggering (i.e. locations, triggering strains, and local structural transformations under different shear protocols) of ST events. Verification undertaken with a model Cu-Zr glass reveals that the predictions agree well with athermal quasistatic shear simulations. The proposed framework is believed to provide an important tool for developing a quantitative understanding of the deformation processes that control mechanical behavior of metallic glasses.