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A Deep Learning Approach to Retrieving 3D Structure Information from High Resolution Time-Resolved TEM Images

Published online by Cambridge University Press:  30 July 2021

Ramon Manzorro
Affiliation:
Arizona State University, United States
Matan Leibovich
Affiliation:
New York University, United States
Joshua Vincent
Affiliation:
Arizona State University, United States
Sreyas Mohan
Affiliation:
New York University, United States
David Matteson
Affiliation:
Cornell University, United States
Carlos Fernandez-Granda
Affiliation:
New York University, United States
Peter Crozier
Affiliation:
Arizona State University, Tempe, Arizona, United States

Abstract

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Type
Full System and Workflow Automation for Enabling Big Data and Machine Learning in Electron Microscopy
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of the Microscopy Society of America

References

Van Aert, S., et al. Nature, 2011. 470, p. 374-377.CrossRefGoogle Scholar
Li, Z. Y., et al. Nature, 2008. 451, p. 46-48.CrossRefGoogle Scholar
Jia, C. L., et al. Nature Materials, 2014. 13, p.1044-1049.CrossRefGoogle Scholar
Madsen, J., et al. Adv. Theory Sim, 2018. 1, 1800037CrossRefGoogle Scholar
Ragone, M., et al. Comput. Mat. Sci., 2020. 180, 109722.CrossRefGoogle Scholar
Vincent, J. L., et al. , pre-print at: https://arxiv.org/abs/2101.07770, 2021.Google Scholar
We gratefully acknowledge support of NSF grant CBET-1604971, NRT-1922658, CCF-1934985, OAC-1940097, OAC-1940124 and OAC-1940276, and the facilities at ASU's John M. Cowley Center for High Resolution Electron MicroscopyGoogle Scholar
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A Deep Learning Approach to Retrieving 3D Structure Information from High Resolution Time-Resolved TEM Images
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