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• W2899341660 abstract "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." @default.
• W2899341660 created "2018-11-09" @default.
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• W2899341660 date "2019-01-07" @default.
• W2899341660 modified "2023-10-18" @default.
• W2899341660 title "Manifold learning of four-dimensional scanning transmission electron microscopy" @default.
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• W2899341660 doi "https://doi.org/10.1038/s41524-018-0139-y" @default.
• W2899341660 hasPublicationYear "2019" @default.
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