Decoding crystallography from high-resolution electron imaging and diffraction datasets with deep learning-Reference-Cited by-同舟云学术

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Decoding crystallography from high-resolution electron imaging and diffraction datasets with deep learning

Published:2019-10-04 Issue:10 Volume:5 Page:
ISSN:2375-2548
Container-title:Science Advances
language:en
Short-container-title:Sci. Adv.

Author:

Aguiar J. A.¹^ORCID,Gong M. L.¹²^ORCID,Unocic R. R.³^ORCID,Tasdizen T.²^ORCID,Miller B. D.¹^ORCID

Affiliation:

1. Idaho National Laboratory, Nuclear Science and Technology Division, Idaho Falls, ID, USA.

2. Department of Electrical and Computer Engineering, Scientific Computing Imaging Institute, University of Utah, Salt Lake City, UT, USA.

3. Oak Ridge National Laboratory, Center for Nanophase Materials Science, Oak Ridge, TN, USA.

Abstract

Deep learning provides an efficient cross-validation tool for crystallography with no preferred orientation or magnification.

Funder

Idaho National Laboratory

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Multidisciplinary

Reference39 articles.

1. Big–deep–smart data in imaging for guiding materials design

2. Electron ptychographic phase imaging of light elements in crystalline materials using Wigner distribution deconvolution;Yang H.;Ultramicroscopy,2017

3. Big data and deep data in scanning and electron microscopies: Deriving functionality from multidimensional data sets;Belianinov A.;Adv. Struct. Chem. Imaging,2015

4. Electron ptychographic microscopy for three-dimensional imaging

5. Ptychographic electron microscopy using high-angle dark-field scattering for sub-nanometre resolution imaging;Humphry M. J.;Nat. Commun.,2012

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