  Recent advances in scanning transmission electron and scanning tunneling
microscopies allow researchers to measure materials structural and electronic
properties, such as atomic displacements and charge density modulations, at an
Angstrom scale in real space. At the same time, the ability to quickly acquire
large, high-resolution datasets has created a challenge for rapid physics-based
analysis of images that typically contain several hundreds to several thousand
atomic units. Here we demonstrate a universal deep-learning based framework for
locating and characterizing atomic species in the lattice, which can be applied
to different types of atomically resolved measurements on different materials.
Specifically, by inspecting and categorizing features in the output layer of a
convolutional neural network, we are able to detect structural and electronic
'anomalies' associated with the presence of point defects in a tungsten
disulfide monolayer, non-uniformity of the charge density distribution around
specific lattice sites on the surface of strongly correlated oxides, and
transition between different structural states of buckybowl molecules. We
further extended our method towards tracking, from one image frame to another,
minute distortions in the geometric shape of individual Si dumbbells in a
3-dimensional Si sample, which are associated with a motion of lattice defects
and impurities. Due the applicability of our framework to both scanning
tunneling microscopy and scanning transmission electron microscopy
measurements, it can provide a fast and straightforward way towards creating a
unified database of defect-property relationships from experimental data for
each material.
