Manifold Learning of Four-dimensional Scanning Transmission Electron Microscopy
This incremental method addresses data processing bottlenecks for researchers in materials science using 4D-STEM, potentially accelerating discoveries in fields like ferroelectric and topological materials.
The paper tackled the challenge of processing and interpreting large 4D-STEM datasets, particularly for weak signals in materials like graphene, by using manifold learning to visualize and analyze atomic diffraction patterns, effectively discriminating single dopant anomalies.
Four-dimensional scanning transmission electron microscopy (4D-STEM) of local atomic diffraction patterns is emerging as a powerful technique for probing intricate details of atomic structure and atomic electric fields. However, efficient processing and interpretation of large volumes of data remain challenging, especially for two-dimensional or light materials because the diffraction signal recorded on the pixelated arrays is weak. Here we employ data-driven manifold leaning approaches for straightforward visualization and exploration analysis of the 4D-STEM datasets, distilling real-space neighboring effects on atomically resolved deflection patterns from single-layer graphene, with single dopant atoms, as recorded on a pixelated detector. These extracted patterns relate to both individual atom sites and sublattice structures, effectively discriminating single dopant anomalies via multi-mode views. We believe manifold learning analysis will accelerate physics discoveries coupled between data-rich imaging mechanisms and materials such as ferroelectric, topological spin and van der Waals heterostructures.