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.
@article{arxiv.1811.00080,
title = {Manifold Learning of Four-dimensional Scanning Transmission Electron Microscopy},
author = {Xin Li and Ondrej E. Dyck and Mark P. Oxley and Andrew R. Lupini and Leland McInnes and John Healy and Stephen Jesse and Sergei V. Kalinin},
journal= {arXiv preprint arXiv:1811.00080},
year = {2019}
}