Large-scale cooperative sulfur vacancy dynamics in two-dimensional MoS2 from machine learning interatomic potentials

TL;DR

This study uses machine learning interatomic potentials to simulate sulfur vacancy dynamics in MoS2 monolayers, revealing mechanisms of cooperative vacancy transport and explaining experimental vacancy patterns.

Contribution

It introduces a detailed atomistic simulation approach for vacancy dynamics in MoS2 using two advanced MLIP frameworks, advancing understanding of defect behavior.

Findings

Vacancy clusters incorporate into larger structures

Line defects span tens of nanometers as observed experimentally

MLIP frameworks accurately model vacancy transport

Abstract

The formation of extended sulfur vacancies in MoS2 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.