Fig. 5: Clustering results on real space images in 4D-STEM datasets.
a Visualizing 4D data in a momentum-major order, where each pixel in diffraction pattern can be considered as a real-space image. Scale bar, 500 nm. b Schematic of the divisive hierarchical clustering architecture on real space images. c Map of hierarchical clustering results in diffraction space on a WS2–WSe2 superlattice. d–g Mean real-space images of the superlattice in each cluster. d Displays the image from dark blue area in (c), which represents a virtual BF image; e, f are the images from light blue and yellow portions in (c), corresponding to DF images for different flakes. g Sums all other areas in (c), indicating a thickness variation in the sample. h Map of hierarchical clustering results in diffraction space on a cross-sectional InGaP/GaAs crystal, with an ADF image displayed in the inset. Scale bar, 200 nm. i–n Mean real-space images of the sample in each cluster. i corresponds to the center beam, the green area in (h), and shows a virtual BF image; j, k are from the dark blue and cyan area in respect, which are from the amorphous carbon and thickness effect in the sample; l–n are virtual DF images for the cross-section, showing strain effects and twin grains.
