  Half of wavefunction information is undetected by conventional transmission
electron microscopy (CTEM) as only the intensity, and not the phase, of an
image is recorded. Following successful applications of deep learning to
optical hologram phase recovery, we have developed neural networks to recover
phases from CTEM intensities for new datasets containing 98340 exit
wavefunctions. Wavefunctions were simulated with clTEM multislice propagation
for 12789 materials from the Crystallography Open Database. Our networks can
recover 224x224 wavefunctions in ~25 ms for a large range of physical
hyperparameters and materials, and we demonstrate that performance improves as
the distribution of wavefunctions is restricted. Phase recovery with deep
learning overcomes the limitations of traditional methods: it is live, not
susceptible to distortions, does not require microscope modification or
multiple images, and can be applied to any imaging regime. This paper
introduces multiple approaches to CTEM phase recovery with deep learning, and
is intended to establish starting points to be improved upon by future
research. Source code and links to our new datasets and pre-trained models are
available at https://github.com/Jeffrey-Ede/one-shot
