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.