Speaker
Description
Unlike their crystalline counterparts, glasses have a complex structure that lacks any long-range order, resulting in the system possessing a large number of metastable states. The system transitions between these metastable states, giving rise to a complex mechanical response when subjected to mechanical deformation. These transitions manifest as localized atomic rearrangements also known as plastic events. It has been postulated that certain regions that are prone to rearrangements can be identified in the stress-free configuration. Efforts to predict these regions have employed a wide range of methods, from computationally expensive local mechanical simulations to data-intensive machine learning techniques that require large training datasets. In contrast, we propose the use of the fabric tensor as a simple, geometry-based predictor for soft spots. The fabric tensor relies solely on atomic positions to characterize bond directionality within the system. We demonstrate a strong correlation between certain features of the fabric tensor and soft spots in two-dimensional silica samples generated using the Monte Carlo bond-switching algorithm and subjected to pure shear under athermal quasistatic conditions. These results show that a purely geometrical measure can effectively predict soft spots in disordered solids independent of the underlying potential energy landscape.
