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Manifold Learning of Four-dimensional Scanning Transmission Electron Microscopy
- Source :
- npj Computational Materials volume 5, Article number: 5 (2019)
- Publication Year :
- 2018
-
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 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.
Details
- Database :
- arXiv
- Journal :
- npj Computational Materials volume 5, Article number: 5 (2019)
- Publication Type :
- Report
- Accession number :
- edsarx.1811.00080
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.1038/s41524-018-0139-y