Benchmarking machine learning approaches for polarization mapping in ferroelectrics using 4D-STEM (opens in new tab)

Four-dimensional scanning transmission electron microscopy (4D-STEM) provides rich, atomic-scale insights into materials structures. However, extracting specific physical properties—such as polarization directions essential for understanding functional properties of ferroelectrics—remains a significant challenge. In this study, we systematically benchmark multiple machine learning models, namely ResNet, VGG, a custom convolutional neural network, and PCA-informed k-Nearest Neighbors, to autom...