Transferable and interpretable machine learning model for four-dimensional scanning transmission electron microscopy data

The challenge brought to scientific discovery by the data revolution may be overcome by data scientific approaches. Here we focus on 4D scanning transmission electron microscopy (STEM) data. With advances in detector technology, STEM records the full scattering distribution at each scan position in real space, producing a 4D phase-space distribution. An efficient approach is needed to turn these data into a real space image with subatomic resolution. Existing approaches are limited: annular dark field (ADF) imaging by low dose efficiency and resolution, and ptychography to a few atomic layers and by high computational cost. Here, we develop an efficient, interpretable machine learning model to map the entire STEM dataset to real space images. Our model has higher contrast than ADF, still distinguishes atomic species, and transfers well between samples of different lattice symmetry. We benchmark against conventional approaches using quantitative metrics for resolution and contrast.