  Characterizing crystal structures and interfaces down to the atomic level is
an important step for designing advanced materials. Modern electron microscopy
routinely achieves atomic resolution and is capable to resolve complex
arrangements of atoms with picometer precision. Here, we present AI-STEM, an
automatic, artificial-intelligence based method, for accurately identifying key
characteristics from atomic-resolution scanning transmission electron
microscopy (STEM) images of polycrystalline materials. The method is based on a
Bayesian convolutional neural network (BNN) that is trained only on simulated
images. AI-STEM automatically and accurately identifies crystal structure,
lattice orientation, and location of interface regions in synthetic and
experimental images. The model is trained on cubic and hexagonal crystal
structures, yielding classifications and uncertainty estimates, while no
explicit information on structural patterns at the interfaces is included
during training. This work combines principles from probabilistic modeling,
deep learning, and information theory, enabling automatic analysis of
experimental, atomic-resolution images.
