Research Highlights

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.

Deep learning method enables defect‘sniffer’ for 2D materials

April 1, 2020

Developed a deep-learning approach to automatically create libraries of structural and electronic properties of atomic defects in 2D materials.