ABSTRACT The formation of extended sulfur vacancies in MoS 2 monolayers is closely associated with catalytic activity and may also be the basis for its memristive behavior. Nanosecond‐scale molecular dynamics simulations using machine learning interatomic potentials (MLIPs) reveal key mechanisms of cooperative vacancy transport, including incorporation of vacancies into clusters of arbitrary size. The simulations provide a coherent atomistic explanation for irradiation‐induced vacancy patterns observed experimentally, especially the formation of line defects spanning tens of nanometers. Results and performance are compared of two MLIP frameworks: (i) on‐the‐fly learning with Gaussian approximation potential, and (ii) fine‐tuning of an equivariant foundation model.
Flötotto et al. (Sun,) studied this question.