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

Photoplasticity, the light-induced\nalteration of mechanical properties\nin semiconductors, is crucial for the development of advanced optoelectronic\ndevices and the understanding of semiconductor mechanics. Despite\nprogress in understanding this phenomenon, atomic-scale mechanisms,\nparticularly under photoexcitation, remain complex and are partially\nunderstood. Here, we introduce a new computational framework combining\nconstrained Density Functional Theory (CDFT) with machine learning\npotential (MLP) to explore Peierls stress and dislocation dynamics\nin zinc sulfide (ZnS) under both ground and excited states. Our results\nreveal that photoexcitation significantly increases Peierls stress\nby reducing strain concentration at the dislocation core, contributing\nto the transition from ductility to brittleness under light exposure.\nImportantly, this enhancement occurs without substantial changes in\nthe dislocation core structure. These insights provide an understanding\nof the atomic-scale mechanisms behind photoplasticity in ZnS, demonstrating\nthat integrating CDFT with MLP is a highly accurate and efficient\napproach to study complex material behaviors under photoexcitation.