Assisting 4D-STEM Data Processing by Machine Learning and Bayesian Optimization

Explore advanced techniques for processing 4D scanning transmission electron microscopy (4D-STEM) data using machine learning and Bayesian optimization in this conference talk. Discover how hierarchical unsupervised learning workflows can cluster nanobeam electron diffraction patterns, revealing essential features in materials such as strain and ripples in 2D lateral heterojunctions and ferroelectric domains in SnSe samples. Learn about a streamlined data-processing workflow for electron ptychography that employs Bayesian optimization with Gaussian processes to automatically tune reconstruction parameters, significantly improving efficiency and producing high-quality reconstructions. Gain insights into the challenges and advancements in multi-dimensional microscopy, including applications in cryogenic electron microscopy (cryo-EM) and the importance of sample preparation.