Unraveling Photoplasticity in ZnS: Enhanced Peierls Stress under Photoexcitation using Machine Learning Potentials

Photoplasticity, the light-induced alteration of mechanical properties in semiconductors, is crucial for the development of advanced optoelectronic devices and the understanding of semiconductor mechanics. Despite progress in understanding this phenomenon, atomic-scale mechanisms, particularly under photoexcitation, remain complex and are partially understood. Here, we introduce a new computational framework combining constrained Density Functional Theory (CDFT) with machine learning potential (MLP) to explore Peierls stress and dislocation dynamics in zinc sulfide (ZnS) under both ground and excited states. Our results reveal that photoexcitation significantly increases Peierls stress by reducing strain concentration at the dislocation core, contributing to the transition from ductility to brittleness under light exposure. Importantly, this enhancement occurs without substantial changes in the dislocation core structure. These insights provide an understanding of the atomic-scale mechanisms behind photoplasticity in ZnS, demonstrating that integrating CDFT with MLP is a highly accurate and efficient approach to study complex material behaviors under photoexcitation.