Large‐Scale Cooperative Sulfur Vacancy Dynamics in Two‐Dimensional MoS 2 From Machine Learning Interatomic Potentials
Abstract
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.
Fields of Study & Subjects
Machine Learning in Materials Science
2D Materials and Applications
Fuel Cells and Related Materials
Publication details
- Journal / source
- Small
- Publisher
- Wiley
- Volume
- 22
- Issue
- 20
- Pages
- e10679–e10679
- Publication date
- 15 February 2026
- Language
- EN