| Abstract Scope |
Precise knowledge of atomic structures is critical for understanding, engineering, and harnessing the promise of two-dimensional (2D) materials. Their high surface area-to-volume ratios, van der Waals bonding between layers, and layer-dependent properties mean that variations in thickness and defect density or distribution can greatly impact 2D materials’ properties. Without understanding these features, it will be difficult to realize transformative progress toward their use in applications such as electronics, optoelectronics, spintronics, and sensing. In this talk, I will discuss our work on the identification of thicknesses and defect locations in few-layer 2D materials. In this work, we are using a combination of simulation, experimental aberration-corrected annular dark-field scanning transmission electron microscopy (ADF-STEM), and machine-learning analysis. Key findings include the identification of structural features, as well as elucidation of the strengths and limitations of different methods to identify them. In particular, we will focus on how machine learning can help identify structural details that are difficult to distinguish by other methods. |