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Electron ptychography provides new opportunities to resolve atomic structureswith deep sub-angstrom spatial resolution and studying electron-beam sensitivematerials with high dose efficiency. In practice, obtaining accurateptychography images requires simultaneously optimizing multiple parameters thatare often selected based on trial-and-error, resulting in low-throughputexperiments and preventing wider adoption. Here, we develop an automaticparameter selection framework to circumvent this problem using Bayesianoptimization with Gaussian processes. With minimal prior knowledge, theworkflow efficiently produces ptychographic reconstructions that are superiorthan the ones processed by experienced experts. The method also facilitatesbetter experimental designs by exploring optimized experimental parameters fromsimulated data.

Automatic Parameter Selection for Electron Ptychography via Bayesian Optimization