Structure and dynamics of sulfur vacancies in monolayer MoS2 studied by DFT-based machine learning potentials

Adam Hložný, Ján Brndiar, Michele Casula, Ivan Štich

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DOI: 10.1063/5.0281071
Published: 2025-12-05
Container: The Journal of Chemical Physics
Publisher: AIP Publishing
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1063/5.0281071,
  title = {Structure and dynamics of sulfur vacancies in monolayer MoS2 studied by DFT-based machine learning potentials},
  author = {Adam Hložný and Ján Brndiar and Michele Casula and Ivan Štich},
  year = {2025},
  journal = {The Journal of Chemical Physics},
  doi = {10.1063/5.0281071},
  url = {https://doi.org/10.1063/5.0281071}
}

RIS

TY  - JOUR
TI  - Structure and dynamics of sulfur vacancies in monolayer MoS2 studied by DFT-based machine learning potentials
AU  - Adam Hložný
AU  - Ján Brndiar
AU  - Michele Casula
AU  - Ivan Štich
PY  - 2025
JO  - The Journal of Chemical Physics
DO  - 10.1063/5.0281071
UR  - https://doi.org/10.1063/5.0281071
ER  -

APA

Hložný, A., Brndiar, J., Casula, M., & Štich, I. (2025). Structure and dynamics of sulfur vacancies in monolayer MoS2 studied by DFT-based machine learning potentials. The Journal of Chemical Physics. https://doi.org/10.1063/5.0281071

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crossref · retrieved 2026-09-26T04:59:40.373Z