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Denoising STEM Electron Energy Loss Spectra using Convolutional Autoencoders

Published online by Cambridge University Press:  30 July 2021

Mark Oxley ,
Maxim Ziatdinov  and
Sergei Kalinin
Mark Oxley
Affiliation:
Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States
Maxim Ziatdinov
Affiliation:
Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States
Sergei Kalinin
Affiliation:
Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States

Abstract

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Type
Advanced Imaging and Spectroscopy for Nanoscale Materials Characterization
Information
Microscopy and Microanalysis , Volume 27 , Supplement S1 , August 2021 , pp. 1180 - 1182
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of the Microscopy Society of America

References

Bonnet, N., Journal of Microscopy 190 (1998), p. 2.CrossRefGoogle Scholar
Oxley, M. P. et al. ; Microscopy and Microanalysis 20 (2014), p. 784.Google ScholarPubMed
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 (M.P.O., 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