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Leveraging First Principles Modeling and Machine Learning for Microscopy Data Inversion

Published online by Cambridge University Press:  04 August 2017

Eric Schwenker
Affiliation:
Center for Nanoscale Materials, Argonne National Laboratory, Lemont IL, USA.
Fatih Sen
Affiliation:
Center for Nanoscale Materials, Argonne National Laboratory, Lemont IL, USA.
Spencer Hills
Affiliation:
Center for Nanoscale Materials, Argonne National Laboratory, Lemont IL, USA.
Tadas Pualauskas
Affiliation:
Department of Physics, University of Illinois at Chicago, Chicago IL, USA.
Ce Sun
Affiliation:
Department of Materials Science and Engineering, University of Texas at Dallas, Dallas TX, USA.
Liang Li
Affiliation:
Center for Nanoscale Materials, Argonne National Laboratory, Lemont IL, USA.
Alper Kinaci
Affiliation:
Center for Nanoscale Materials, Argonne National Laboratory, Lemont IL, USA.
Kendra Letchworth-Weaver
Affiliation:
Center for Nanoscale Materials, Argonne National Laboratory, Lemont IL, USA.
Moon Kim
Affiliation:
Department of Materials Science and Engineering, University of Texas at Dallas, Dallas TX, USA.
Robert Klie
Affiliation:
Department of Physics, University of Illinois at Chicago, Chicago IL, USA.
Jianguo Wen
Affiliation:
Center for Nanoscale Materials, Argonne National Laboratory, Lemont IL, USA.
Maria K. Y. Chan
Affiliation:
Center for Nanoscale Materials, Argonne National Laboratory, Lemont IL, USA.

Abstract

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Type
Abstract
Copyright
© Microscopy Society of America 2017 

References

[1] Lowe, D. Proceedings of the International Conference on Computer Vision 2 1999). p. 1150.Google Scholar
[2] Dalal, N., Navneet, & Triggs, B. IEEE Computer Society Conference on Computer Vision and Pattern Recognition 1 2005). p. 886.Google Scholar
[3] Paulauskas, T., et al 43rd IEEE Photovoltaic Specialists Conference (PVSC) (2016).Google Scholar
[4] We acknowledge helpful discussions with Dane Morgan, Paul Voyles, Rebecca Willet, Simon Billinge, Amanda Petford-Long, and John Mitchell. Use of the Center of 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. We gratefully acknowledge the computing resources provided on Blues, a high performance computing cluster operated by the Laboratory Computing Resource Center at Argonne National Laboratory.Google Scholar