Strain-rate and temperature dependence of yield stress of amorphous solids via self-learning metabasin escape algorithm

A general self-learning metabasin escape (SLME) algorithm~\citep{caoPRE2012} is coupled in this work with continuous shear deformations to probe the yield stress as a function of strain rate and temperature for a binary Lennard-Jones (LJ) amorphous solid. The approach is shown to match the results of classical molecular dynamics (MD) at high strain rates where the MD results are valid, but, importantly, is able to access experimental strain rates that are about ten orders of magnitude slower than MD. In doing so, we find in agreement with previous experimental studies that a substantial decrease in yield stress is observed with decreasing strain rate. At room temperature and laboratory strain rates, the activation volume associated with yield is found to contain about 10 LJ particles, while the yield stress is as sensitive to a $1.5\%T_{\rm g}$ increase in temperature as it is to a one order of magnitude decrease in strain rate. Moreover, our SLME results suggest the SLME and extrapolated results from MD simulations follow distinctly different energetic pathways during the applied shear deformation at low temperatures and experimental strain rates, which implies that extrapolation of the governing deformation mechanisms from MD strain rates to experimental may not be valid.