Self-organized defect clustering and concentration-dependent vacancy diffusion in MoS
arXiv:2607.14951
The paper uses kinetic Monte‑Carlo simulations, informed by machine‑learning interatomic potentials, to study how sulfur vacancies in MoS₂ cluster and diffuse, revealing concentration‑dependent transport regimes that explain memristive behavior.
Abstract
Sulfur vacancy migration has a crucial impact on electronic transport and the functional behavior of MoS-based devices such as memristors and memtransistors. According to recent atomistic simulations, vacancy migration proceeds via cooperative, vacancy-assisted sulfur jumps, implying strongly correlated defect dynamics. Here, we investigate the collective behavior of sulfur-vacancy clusters in MoS using kinetic Monte-Carlo simulations with transition rates derived from machine learning interatomic potential molecular dynamics simulations. We identify three transport regimes: At low concentrations, vacancies are immobile or confined within small clusters, whereas at high concentrations, classical diffusive transport with a constant diffusion coefficient is observed, and vacancies aggregate into anisotropically extended clusters. A well defined intermediate regime is characterized by clusters merging into a connected, fluctuating network with a concentration-dependent diffusion coefficient. This regime is characterized by a broad distribution of cluster sizes. The strong dependence of the vacancy diffusion coefficient on the average defect concentration provides new insights into the origin of memristive behavior observed in MoS.