  Electron ptychography provides new opportunities to resolve atomic structures
with deep sub-angstrom spatial resolution and studying electron-beam sensitive
materials with high dose efficiency. In practice, obtaining accurate
ptychography images requires simultaneously optimizing multiple parameters that
are often selected based on trial-and-error, resulting in low-throughput
experiments and preventing wider adoption. Here, we develop an automatic
parameter selection framework to circumvent this problem using Bayesian
optimization with Gaussian processes. With minimal prior knowledge, the
workflow efficiently produces ptychographic reconstructions that are superior
than the ones processed by experienced experts. The method also facilitates
better experimental designs by exploring optimized experimental parameters from
simulated data.
