  Recording atomic-resolution transmission electron microscopy (TEM) images is
becoming increasingly routine. A new bottleneck is then analyzing this
information, which often involves time-consuming manual structural
identification. We have developed a deep learning-based algorithm for
recognition of the local structure in TEM images, which is stable to microscope
parameters and noise. The neural network is trained entirely from simulation
but is capable of making reliable predictions on experimental images. We apply
the method to single sheets of defected graphene, and to metallic nanoparticles
on an oxide support.
