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Autonomous Electron Microscopy Enabling Physics Discovery: Applications in Plasmonics of 2D Systems

Published online by Cambridge University Press:  22 July 2022

Kevin Roccapriore ,
Sergei V. Kalinin  and
Maxim Ziatdinov
Kevin Roccapriore
Affiliation:
Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, USA
Sergei V. Kalinin
Affiliation:
Department of Materials Science and Engineering, University of Tennessee, Knoxville TN, USA
Maxim Ziatdinov
Affiliation:
Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, USA Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA

Abstract

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Type
On Demand - Artificial Intelligence, Instrument Automation, and High-Dimensional Data Analytics for Microscopy and Microanalysis
Information
Microscopy and Microanalysis , Volume 28 , Supplement S1 , August 2022 , pp. 3142 - 3144
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Copyright
Copyright © Microscopy Society of America 2022

References

Wilson, A. G. et al. , Artificial Intelligence and Statistics 2016, 370–378.Google Scholar
Roccapriore, K. M. et al. , arXiv 2021 2108.03290 10.5465/AMBPP.2021.10280abstractCrossRefGoogle Scholar
Roccapriore, K. M. et al. , arXiv 2021 2110.01568 10.5465/AMBPP.2021.10280abstractCrossRefGoogle Scholar
This effort (ML and STEM) is based upon work supported by the U.S. Department of Energy (DOE), Office of Science, Basic Energy Sciences (BES), Materials Sciences and Engineering Division (K.M.R., S.V.K.) and was performed and partially supported (M.Z.) at the Oak Ridge National Laboratory's Center for Nanophase Materials Sciences (CNMS), a U.S. Department of Energy, Office of Science User Facility.Google Scholar