Automatic Parameter Selection for Electron Ptychography via Bayesian Optimization

TL;DR

This paper introduces an automatic parameter selection framework for electron ptychography using Bayesian optimization, significantly improving image quality and experimental efficiency without extensive prior knowledge.

Contribution

It presents a novel Bayesian optimization approach to automatically select parameters in electron ptychography, outperforming expert manual tuning.

Findings

Reconstructed images are superior to those processed by experts.

The method reduces trial-and-error in parameter selection.

It enables optimized experimental designs from simulated data.

Abstract

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.