![Constructing libraries of atomic defects (such as those shown above in graphene) relied on an approach that combined experiment and theory to extract and classify defects with STEM, predict electronic structure with DFT, and compare both of these experimental and theoretical results with STM results from the same system.](/sites/default/files/styles/list_page_thumbnail/public/2020-07/Picture8_0.png?h=6e835914&itok=-rVHgfJG)
Developed a deep-learning approach to automatically create libraries of structural and electronic properties of atomic defects in 2D materials.
Developed a deep-learning approach to automatically create libraries of structural and electronic properties of atomic defects in 2D materials.
Achievement: Devised a novel and accurate computational technique for investigating the self-assembly of large macromolecules, and used this method to reveal the initial stages of self-assembly of the carboxysome, the prototype bacterial