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

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.

Authors:
 [1];  [1];  [1];  [1]; ORCiD logo [2];  [3];  [1];  [1]
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  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
  2. Tutte Inst. for Mathematics and Computing, Ottawa (Canada)
  3. Tutte Inst. for Mathematics and Computing, Ottawa (Canada
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES)
OSTI Identifier:
1491307
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
npj Computational Materials
Additional Journal Information:
Journal Volume: 5; Journal Issue: 1; Journal ID: ISSN 2057-3960
Publisher:
Nature Publishing Group
Country of Publication:
United States
Language:
English
Subject:
47 OTHER INSTRUMENTATION

Citation Formats

Li, Xin, Dyck, Ondrej E., Oxley, Mark P., Lupini, Andrew R., McInnes, Leland, Healy, John, Jesse, Stephen, and Kalinin, Sergei V. Manifold learning of four-dimensional scanning transmission electron microscopy. United States: N. p., 2019. Web. doi:10.1038/s41524-018-0139-y.
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Li, Xin, Dyck, Ondrej E., Oxley, Mark P., Lupini, Andrew R., McInnes, Leland, Healy, John, Jesse, Stephen, & Kalinin, Sergei V. Manifold learning of four-dimensional scanning transmission electron microscopy. United States. https://doi.org/10.1038/s41524-018-0139-y
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Li, Xin, Dyck, Ondrej E., Oxley, Mark P., Lupini, Andrew R., McInnes, Leland, Healy, John, Jesse, Stephen, and Kalinin, Sergei V. Mon . "Manifold learning of four-dimensional scanning transmission electron microscopy". United States. https://doi.org/10.1038/s41524-018-0139-y. https://www.osti.gov/servlets/purl/1491307.
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@article{osti_1491307,
title = {Manifold learning of four-dimensional scanning transmission electron microscopy},
author = {Li, Xin and Dyck, Ondrej E. and Oxley, Mark P. and Lupini, Andrew R. and McInnes, Leland and Healy, John and Jesse, Stephen and Kalinin, Sergei V.},
abstractNote = {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.},
doi = {10.1038/s41524-018-0139-y},
journal = {npj Computational Materials},
number = 1,
volume = 5,
place = {United States},
year = {2019},
month = {1}
}
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Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
https://doi.org/10.1038/s41524-018-0139-y
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Cited by: 26 works
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Figures / Tables:

Figure 1 Figure 1: Schematic of manifold learning of Ronchigram datasets. The low-dimensional physical parameter space (such as defocus, accelerating voltage, and material structure phases) is translated onto a high dimensional Ronchigram response space by the imaging mechanisms of microscope. High-dimensional and large-scale Ronchigram datasets are projected into a low-dimensional manifold spacemore » for efficiently revealing and hierarchically representing rich features. With extracted patterns from manifold learning, deeper study can be conducted via adaptive experiment design« less
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