Electronic Transport and Machine-Learned Modeling of Defect Dynamics in MoS₂ Monolayers for Resistive Switching Devices

Memristors based on 2D materials hold great promise for neuromorphic computing systems; however, their switching behavior remains poorly understood. We investigate resistive switching in monolayer MoS₂ planar channels where sulfur-vacancy concentration and spatial distribution control conductance. Using density-functional theory combined with nonequilibrium Green’s function (DFT+NEGF), we quantify how vacancy density and configuration modulate electronic transmission and set ON/OFF ratios. Furthermore, we train an equivariant machine-learning interatomic potential with DFT accuracy and employ molecular dynamics under an applied electric field to simulate vacancy migration pathways and kinetics, field-driven defect reconfiguration, and the resulting evolution of channel conductance and device hysteresis. Our framework links atomistic defect dynamics to electronic-transport response, clarifying the microscopic origins of switching in MoS₂ memristors and providing insight into design rules for stable 2D memristive devices.