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Predicting local plasmon resonances and geometries using autoencoder networks in complex nanoparticle assemblies

Published online by Cambridge University Press:  30 July 2021

Kevin Roccapriore
Affiliation:
Oak Ridge National Laboratory, United States
Maxim Ziatdinov
Affiliation:
Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States
Shin Hum Cho
Affiliation:
Samsung Electronics, United States
Delia Milliron
Affiliation:
University of Texas at Austin, United States
Jordan Hachtel
Affiliation:
Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, 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
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

Kalinin, S. V. et al. , arXiv 2020 arXiv:2006.01374Google Scholar
Cho, S. H. et al. , J., Chem. Mater. 2019 31, 2661.CrossRefGoogle Scholar
Roccapriore, K. M. et al. , arXiv 2020 arXiv:2009.09005Google 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 (J.A.H., 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. S.H.C acknowledges (NSF, CHE-19052631609656, CBET-1704634, NASCENT, an NSF ERC EEC-1160494, and CDCM, an NSF MRSEC DMR-1720595), the Welch Foundation (F-1848), and the Fulbright Program (IIE-15151071).Google Scholar