Hostname: page-component-586b7cd67f-t7czq Total loading time: 0 Render date: 2024-11-26T16:31:27.946Z Has data issue: false hasContentIssue false
  • English
  • Français

Finding Features from Microscopes to Simulations Via Ensemble Learning and Atomic Manipulation

Published online by Cambridge University Press:  22 July 2022

Ayana Ghosh ,
Kevin M. Roccapriore ,
Bobby Sumpter ,
Ondrej Dyck ,
Maxim Ziatdinov  and
Sergei V. Kalinin
Ayana Ghosh
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
Kevin M. Roccapriore
Affiliation:
Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, USA
Bobby Sumpter
Affiliation:
Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, USA
Ondrej Dyck
Affiliation:
Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, 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
Sergei V. Kalinin
Affiliation:
Department of Materials Science and Engineering, University of Tennessee, Knoxville, TN, USA

Abstract

An abstract is not available for this content so a preview has been provided. As you have access to this content, a full PDF is available via the ‘Save PDF’ action button.
Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
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. 3078 - 3080
Check for updates
Copyright
Copyright © Microscopy Society of America 2022

References

Pennycook, S. J., Ultramicroscopy, 2017 180, 22-33.CrossRefGoogle Scholar
Pennycook, S. J., Ultramicroscopy, 2012 123, 28-37.CrossRefGoogle Scholar
Gerber, C. and Lang, H.P., Nat. Nanotechnol. 2006 1, 3-5.CrossRefGoogle Scholar
Barth, C., et al. , Adv. Mater. 2011 23, 477-501.CrossRefGoogle Scholar
Bonnell, D. A., and Garra, J., Rep. Prog. Phys. 2008 71 4, 044501.CrossRefGoogle Scholar
Ghosh, A., Sumpter, B.G.., Dyck, O., Kalinin, S. V., Ziatdinov, M., npj Comput. Mater., 2021 7, 1.Google Scholar
Ghosh, A., Ziatdinov, M., Dyck, O., Sumpter, B. G., Kalinin, S. V., arXiv preprint, 2021. arXiv:2109.04541.Google Scholar
Ziatdinov, M., Ghosh, A., Wong, T., Kalinin, S.V., arXiv preprint, 2021. arXiv:2105.07485.Google Scholar
This effort (machine learning) is based upon work supported by the U.S. Department of Energy (DOE), Office of Science, Office of Basic Energy Sciences Data, Artificial Intelligence Nanoscale Science Research (NSRC AI) Centers program (A.G., BGS, S.V.K.). The STEM experiments were supported by the DOE, Office of Science, Basic Energy Sciences (BES), Materials Sciences and Engineering Division (O.D.) and by the DOE, Office of Science, Basic Energy Sciences (BES), Materials Sciences and Engineering Division (K.M.R., S.V.K.). Work was performed and partially supported (M.Z) at Oak Ridge National Laboratory's Center for Nanophase Materials Sciences (CNMS), a DOE Office of Science User Facility. This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE).Google Scholar