Abstract
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 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.
Original language | English |
---|---|
Article number | 5 |
Journal | npj Computational Materials |
Volume | 5 |
Issue number | 1 |
DOIs | |
State | Published - Dec 1 2019 |
Funding
This research was partially supported (X.L., O.E.D., S.J., and S.V.K.) at the Center for Nanophase Materials Sciences, which is a US DOE Office of Science User Facility. Part of this work (M.P.O. and A.R.L.) was supported by the Office of Basic Energy Sciences, Materials Sciences and Engineering Division, US Department of Energy. L.M. and J.H. acknowledge support from Tutte Institute for Mathematics and Computing, Canada. We gratefully acknowledge Myron D. Kapetanakis for providing the structure files used in the simulation.