Transferable Machine Learning 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 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 is able to find an intra-unit cell distortion in a sample of PrScO₃ that is missed by ADF using data that cannot be used for ptychography.