Hostname: page-component-cd9895bd7-jn8rn Total loading time: 0 Render date: 2024-12-26T08:10:23.288Z Has data issue: false hasContentIssue false
  • English
  • Français

Strain Mapping from Electron Diffraction Patterns using a Fourier-space Complex Neural Network

Published online by Cambridge University Press:  22 July 2022

Joydeep Munshi ,
Alexander Rakowski ,
Benjamin H Savitzky ,
Steven E Zeltmann ,
Jim Ciston ,
Andrew M Minor ,
Maria KY Chan  and
Colin Ophus
Joydeep Munshi*
Affiliation:
Center for Nanoscale Materials, Argonne National Laboratory, Lemont, IL, USA
Alexander Rakowski
Affiliation:
NCEM, Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Benjamin H Savitzky
Affiliation:
NCEM, Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Steven E Zeltmann
Affiliation:
Department of Materials Science and Engineering, University of California, Berkeley, CA, USA
Jim Ciston
Affiliation:
NCEM, Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Andrew M Minor
Affiliation:
NCEM, Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA, USA Department of Materials Science and Engineering, University of California, Berkeley, CA, USA
Maria KY Chan
Affiliation:
Center for Nanoscale Materials, Argonne National Laboratory, Lemont, IL, USA
Colin Ophus
Affiliation:
NCEM, Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
*
*[email protected]

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. 3056 - 3058
Check for updates
Copyright
Copyright © Microscopy Society of America 2022

References

Ophus, C, Microscopy and Microanalysis 25 (2019), p. 563.10.1017/S1431927619000497CrossRefGoogle Scholar
Savitzky, BH et al. , Microscopy and Microanalysis 27 (2021), p. 712.10.1017/S1431927621000477CrossRefGoogle Scholar
Munshi, J et al. , arXiv:2202.00204 (2022).Google Scholar
Rakowski, A et al. , Microscopy and Microanalysis 27(S1) (2021), p. 62.10.1017/S1431927621000829CrossRefGoogle Scholar
Jain, A et al. , APL Materials 1(1) (2013), p. 011002.10.1063/1.4812323CrossRefGoogle Scholar
Hicks, D et al. , Comp. Mat. Sci. 161 ( 2019), p. S1-S1011.10.1016/j.commatsci.2018.10.043CrossRefGoogle Scholar
Rangel-DaCosta, L et al. , Micron 151 (2021), p. 103141.10.1016/j.micron.2021.103141CrossRefGoogle Scholar
Work at the Molecular Foundry was supported by the Office of Science, Office of Basic Energy Sciences, of the U.S. Department of Energy under Contract No. DEAC02-05CH11231. This work is supported by the U.S. Department of Energy (DOE) Office of Science Scientific User Facilities project titled “4DCamera Distillery: From Massive Electron Microscopy Scattering Data to Useful Information with AI/ML”. Use of the Center for Nanoscale Materials, an Office of Science user facility, was supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under Contract No. DE-AC02-06CH11357. This research used resources of the National Energy Research Scientific Computing Center; a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. Work at the Molecular Foundry was supported by the Office of Science, Office of Basic Energy Sciences, of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231.Google Scholar