Where 2D meets ML: Defects and Reaction Kinetics at the atomic monolayer limit

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Transition metal dichalcogenides (TMDs) are a class of promising 2D materials with the potential to revolutionize nanoelectronics and fulfill the longstanding promise of 2D materials. A recurring themeacross 2D materials research is the significance of defect contribution to the observed material properties. In the case of TMDs, a constellation of defects have been found, with substitutional oxygen dominating in terms of prevalence. These defects and their ensembles also play a crucial role in the oxidation of these materials, as exemplified by WS2. Oxidation can be a double-edged sword, leading to unintentional corrosion, or serving as a tool, depending on the material, for clean etching (e.g. WS2) or for creating clean interfaces (e.g. HfS2) in devices. The study of defects and surface reactions to understand mesoscopic, ensemble measurements has traditionally relied on high spatiotemporal resolution techniques such as scanning tunneling microscopy and ab initio calculations. However, these methods are typically constrained to studying isolated, static point defects and often fail to capture the dynamic behavior of defects under realistic operating conditions. Furthermore, these techniques often overlook the complexity associated with operating conditions, leading to results that may lack generality and statistical relevance. This project aims to bridge this gap by developing a high-throughput computational-experimental framework that combines an extended, multi-scale modelling method for attaining an atomistic understanding of defect dynamics and oxidation, powered by machine learning interatomic potentials, with the noninvasive optical ellipsometry. The recently developed Message Passing Neural Network, MACE, trained on density functional theory calculations, will be employed to study defect dynamics and oxidation thermodynamic and kinetic barriers. Concurrently, optical ellipsometry will provide non-invasive measurements of TMD thickness and monitor oxidation kinetics. By comparing experimental and computational kinetic rates, we aim to provide an integrated, microscopic picture of the dynamic behavior of defects in TMDs.