  We report the development of deep learning coherent electron diffractive
imaging at sub-angstrom resolution using convolutional neural networks (CNNs)
trained with only simulated data. We experimentally demonstrate this method by
applying the trained CNNs to directly recover the phase images from electron
diffraction patterns of twisted hexagonal boron nitride, monolayer graphene and
a Au nanoparticle with comparable quality to those reconstructed by a
conventional ptychographic method. Fourier ring correlation between the CNN and
ptychographic images indicates the achievement of a spatial resolution in the
range of 0.70 and 0.55 angstrom (depending on different resolution criteria).
The ability to replace iterative algorithms with CNNs and perform real-time
imaging from coherent diffraction patterns is expected to find broad
applications in the physical and biological sciences.
