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

过渡金属二卤化物(TMDs)是一类很有前途的2D材料，它有可能给纳米电子学带来革命性的变化，实现2D材料的长期前景。二维材料研究中的一个反复出现的主题是缺陷对观察到的材料性质的贡献的意义。在TMD的情况下，已经发现了一系列缺陷，就流行率而言，替代氧占主导地位。这些缺陷及其系综在这些材料的氧化过程中也起着至关重要的作用，如WS2所证明的那样。氧化可以是一把双刃剑，导致无意的腐蚀，也可以作为工具，根据材料的不同，用于清洁的蚀刻(例如WS2)或在器件中创建干净的界面(例如HfS2)。研究缺陷和表面反应以理解介观、系综测量，传统上依赖于高时空分辨率的技术，如扫描隧道显微镜和从头计算。然而，这些方法通常局限于研究孤立的、静态的点缺陷，并且通常不能捕捉到实际操作条件下缺陷的动态行为。此外，这些技术往往忽略了与操作条件相关的复杂性，导致结果可能缺乏普遍性和统计相关性。该项目旨在通过开发一个高通量的计算-实验框架来弥合这一差距，该框架结合了一种扩展的、多尺度的建模方法，以获得对缺陷动力学和氧化的原子学理解，该方法由机器学习原子间相互作用势驱动，与非侵入性光学椭偏测量法相结合。最近开发的消息传递神经网络，MACE，经过密度泛函理论计算，将被用于研究缺陷动力学和氧化热力学和动力学势垒。同时，光学椭偏测量仪将提供TMD厚度的非侵入性测量并监测氧化动力学。通过比较实验和计算的动力学速率，我们的目标是提供一幅完整的、微观的TMD中缺陷动态行为的图像。

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.