Large‐Scale Cooperative Sulfur Vacancy Dynamics in Two‐Dimensional MoS 2 From Machine Learning Interatomic Potentials

2025/08/19 by Aaron Flötotto, Flötotto, Aaron, Benjamin Spetzler +9 · 4 citations
Energy · Engineering · Materials Science · #2D Materials and Applications #Electrocatalysts for Energy Conversion #Fuel Cells and Related Materials #Gaussian #Interatomic potential #Line (geometry) #Machine Learning in Materials Science #Molecular dynamics #Sulfur #Vacancy defect

paper · pdf · open access · doi:10.1002/smll.202510679

published in Small 22(20), e10679 (Wiley)

openalex publication_date 2026/02/15 · openalex created_date 2026/02/17 · openalex updated_date 2026/06/15

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.

Citations

• Quickstep: Fast and accurate density functional calculations using a mixed Gaussian and plane waves approach
• Glassy dynamics of kinetically constrained models

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