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:
-
- 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
- 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.
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
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.
@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}
}
Web of Science
Figures / Tables:

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