Work

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

Year: 2025

Type: article

Source: The Journal of Chemical Physics

DOI: 10.1063/5.0281071

Authors: Adam Hložný, Ján Brndiar, Michele Casula, Ivan Štich

Institutions: Slovak University of Technology in Bratislava, Slovak Academy of Sciences, Institute of Informatics of the Slovak Academy of Sciences, Centre National de la Recherche Scientifique, Sorbonne Université, Institut de minéralogie, de physique des matériaux et de cosmochimie, Institute of Physics of the Slovak Academy of Sciences

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: closed

Funders: European High Performance Computing Joint Undertaking, U.S. Department of Energy, Grand Équipement National De Calcul Intensif, NextGenerationEU

Awards: G2144061185, G4112177047, G4224005562, G42916606, G5057629512

OpenAlex ID: W4417037043

FWCI:

Cites: 37

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