Manifold Learning of Four-dimensional Scanning Transmission Electron
Microscopy
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by
Xin Li, Ondrej E. Dyck, Mark P. Oxley, Andrew R. Lupini, Leland
McInnes, John Healy, Stephen Jesse, Sergei V. Kalinin
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.
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