Manifold learning of four-dimensional scanning transmission electron microscopy

Li, Xin; Dyck, Ondrej E.; Oxley, Mark P.; Lupini, Andrew R.; McInnes, Leland; Healy, John; Jesse, Stephen; Kalinin, Sergei V.

doi:10.1038/s41524-018-0139-y

Title: Manifold learning of four-dimensional scanning transmission electron microscopy

Journal Article · 07 January 2019 · npj Computational Materials

DOI: https://doi.org/10.1038/s41524-018-0139-y · OSTI ID:1491307

Li, Xin ^[1]; Dyck, Ondrej E. ^[1]; Oxley, Mark P. ^[1]; Lupini, Andrew R. ^[1];

^[2]; Healy, John ^[3]; Jesse, Stephen ^[1]; Kalinin, Sergei V. ^[1]

Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Center for Nanophase Materials Science (CNMS); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Inst. for Functional Imaging of Materials
Tutte Inst. for Mathematics and Computing, Ottawa (Canada)
Tutte Inst. for Mathematics and Computing, Ottawa (Canada

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.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1491307

Journal Information:: npj Computational Materials, Vol. 5, Issue 1; ISSN 2057-3960

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 29 works

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