Revealing structural signatures associated with stress overshoot in two-dimensional Lennard-Jones systems based on interpretable deep learning

Determining whether stress overshoot can be predicted from pre-yield structural information remains a central challenge in understanding amorphous yielding. However, conventional analyses usually require the complete stress-strain response to determine whether stress overshoot occurs. To overcome this limitation, a precise predictive model with hybrid convolutional-temporal encoding is developed, and the temporal-spatial class activation mapping (TS-CAM) method is introduced to interpret stress-overshoot behavior from pre-yield amorphous configuration sequences. Combined with the TS-CAM analysis, the spatiotemporal evolution of model-salient regions associated with stress-overshoot classification is quantitatively characterized. Furthermore, a model-derived scalar descriptor is constructed to characterize predictive signatures correlated with stress-overshoot yielding within the present dataset. This work demonstrates the capability of interpretable deep learning in exploring the plastic deformation mechanism of amorphous solids and provides a data-driven insight for the yielding regulation and structural optimization of amorphous materials.