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Machine Learning Prediction of Valence and Coordination from EELS Spectra of Iron Containing Compounds

Published online by Cambridge University Press:  22 July 2022

Samuel P. Gleason ,
Deyu Lu  and
Jim Ciston
Samuel P. Gleason
Affiliation:
National Center for Electron Microscopy Facility, Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA, United States Department of Chemistry, University of California, Berkeley, CA, United States
Deyu Lu
Affiliation:
Center for Functional Nanomaterials, Brookhaven National Laboratory, Upton, NY, United States
Jim Ciston
Affiliation:
National Center for Electron Microscopy Facility, Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA, United States

Abstract

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Type
Artificial Intelligence, Instrument Automation, And High-dimensional Data Analytics for Microscopy and Microanalysis
Information
Microscopy and Microanalysis , Volume 28 , Supplement S1 , August 2022 , pp. 3086 - 3087
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Copyright
Copyright © Microscopy Society of America 2022

References

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This work was primarily funded by the US Department of Energy, Office of Science, Office of Basic Energy Sciences in the program “4D Camera Distillery: From Massive Electron Microscopy Scattering Data to Useful Information with AI/ML." Electron Microscopy use at the Molecular Foundry was supported by the Office of Science, Office of Basic Energy Sciences, of the US Department of Energy under Contract No. DE-AC02-05CH11231. This research used resources of the Center for Functional Nanomaterials (CFN), which is a U.S. Department of Energy Office of Science User Facility, at Brookhaven National Laboratory under Contract No. DE-SC0012704.Google Scholar