Figure 3: Creating local imaging libraries.

(a) Scanning transmission electron microscopy imaging of Si impurities in graphene monolayer. (b) Categorization of defects in (a) based on the number/type of chemical species in their first coordination sphere via deep learning (DL) based approach. (c) The extracted 2d atomic coordinates of these defects are then used as an input into density functional theory (DFT) calculations to obtain a fully-relaxed 3d structure and calculate electronic properties (in this case, the local density of electronic states for the bands below (E_V) and above (E_C) the Fermi level). (d) The DFT-calculated data can be then used to search for the specific type of defects in the scanning tunneling microscopy (STM) data from the same sample, which measures the local density of states. The search can be performed manually (if the number of STM images is small) or automatically by training a new machine learning (ML) classifier for categorizing the STM data. Image adapted from Ziatdinov et al.¹⁵⁵