Investigating the atomic structures and electronic properties of WS2 thin films with sulfur vacancies via a neural network potential-aided first-principles study

R Otsuka and K Shimizu and H Wakabayashi and S Watanabe, APPLIED PHYSICS EXPRESS, 17, 115501 (2024).

DOI: 10.35848/1882-0786/ad8b0c

Transition metal dichalcogenides are promising materials for high- performance electronics, whereas the impact of defects on their electronic properties remains elusive. Here, we employ neural network potentials (NNPs) constructed from density functional theory (DFT) data to investigate defect-laden WS2 thin films. Molecular dynamics simulations reveal that at low defect concentrations (S/W ratio of 1.9), single sulfur vacancies are predominant. Conversely, at high defect concentrations (S/W ratio of 1.7), complex defects with short lifetimes appear. Additionally, DFT results indicate that the band gap persists at S/W = 1.9 but disappears at 1.7, aligning with observed device degradation at high defect concentrations.

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