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arXiv · 2508.13790

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

Aaron Flötotto·Benjamin Spetzler·Rose von Stackelberg·Martin Ziegler·Erich Runge·Christian Dreßler
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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.

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Aaron Flötotto, Benjamin Spetzler, Rose von Stackelberg, Martin Ziegler, Erich Runge, Christian Dreßler. 2025-08-19. Large-scale cooperative sulfur vacancy dynamics in two-dimensional MoS2 from machine learning interatomic potentials. https://doi.org/10.1002/smll.202510679

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