Regensburg 2025 – scientific programme
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O: Fachverband Oberflächenphysik
O 88: 2D Materials: Stacking and Heterostructures (joint session O/HL)
O 88.8: Talk
Thursday, March 20, 2025, 16:45–17:00, H6
Impact of point defects and grain boundaries on sulfur diffusion and memristive properties of MoS2 single sheets — •Aaron Flötotto1, Jules Oumard1, Benjamin Spetzler2, Martin Ziegler2, Erich Runge1, and Christian Dressler1 — 1Technische Universität Ilmenau, Germany — 2Christian-Albrechts-Universität zu Kiel, Germany
The memristive properties of transition metal dichalcogenides, such as MoS2, are currently the subject of intense research and have recently been traced back to the dynamics of sulfur vacancies [1, 2]. In this theoretical work, we employ molecular dynamics to determine the sulfur vacancy diffusion coefficients in the vicinity of various point defect structures and grain boundaries in single sheet MoS2. To address the necessity of large cell sizes and long time scales, we utilize machine learning force fields, applying both Gaussian approximation potential and equivariant graph neural networks. We then compare the accuracy of these force fields and discuss the results in regard to the memristive properties of MoS2. Our findings indicate a reduction in energy barriers for sulfur vacancy diffusion as the size of vacancy clusters increases and highlight the importance of certain interstitial sites in these vacancy clusters.
[1] Li, D., et al. (2018). ACS Nano, 12(9), 9240-9252. doi.org/10.1021/acsnano.8b03977
[2] Spetzler, B., et al. (2024). Adv. Electron. Mater., 10, 2300635. doi.org/10.1002/aelm.202300635
Keywords: transition metal dichalcogenides; machine learning force fields; crystal defects