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Merging Deep Learning, Chemistry, and Diffraction for High-Throughput Material Structure Prediction

Published online by Cambridge University Press:  05 August 2019

Matthew L. Gong
Affiliation:
Idaho National Laboratory, Nuclear Materials Department, Idaho Falls, IdahoUSA. University of Utah, Scientific Computing Imaging Institute, Department of Electrical and Computer Engineering, Salt Lake City, UtahUSA.
Brandon D. Miller
Affiliation:
Idaho National Laboratory, Nuclear Materials Department, Idaho Falls, IdahoUSA.
Ray R. Unocic
Affiliation:
Oak Ridge National Laboratory, Center for Nanophase Materials Science, Oak Ridge, TennesseeUSA.
Khallid Hattar
Affiliation:
Sandia National Laboratories, Center for Integrated Nanotechnologies Albuquerque, New MexicoUSA.
Bryan Reed
Affiliation:
Integrated Dynamic Electron Solutions, Pleasanton, CaliforniaUSA.
Dan Masiel
Affiliation:
Integrated Dynamic Electron Solutions, Pleasanton, CaliforniaUSA.
Tolga Tasdizen
Affiliation:
University of Utah, Scientific Computing Imaging Institute, Department of Electrical and Computer Engineering, Salt Lake City, UtahUSA.
Jeffery A. Aguiar
Affiliation:
Idaho National Laboratory, Nuclear Materials Department, Idaho Falls, IdahoUSA.
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Abstract

Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Data Acquisition Schemes, Machine Learning Algorithms, and Open Source Software Development for Electron Microscopy
Copyright
Copyright © Microscopy Society of America 2019 

References

[1]Vasudevan, R.K. et al. , ACS Nano. 8 (2014). doi:10.1021/nn504730n.Google Scholar
[2]Vasudevan, R.K. et al. , Appl Phys Lett. 106 (2015). doi:10.1063/1.4914016.Google Scholar
[3]Dongarra, J et al. , Int J High Perform Comput Appl. 25 (2011). doi:10.1177/1094342010391989.Google Scholar
[4]Work supported through the INL Laboratory Directed Research& Development (LDRD) Program under DOE Idaho Operations Office Contract DE-AC07-05ID14517. This work was performed, in part, at the Center for Integrated Nanotechnologies, an Office of Science User Facility operated for the U.S. Department of Energy (DOE) Office of Science. Sandia National Laboratories is a multi-mission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC., a wholly owned subsidiary of Honeywell International, Inc., for the U.S. DOE's National Nuclear Security Administration under contract DE-NA-0003525. The views expressed in the article do not necessarily represent the views of the U.S. DOE or the United States Government. In part, this research was conducted at the Center for Nanophase Materials Sciences, which is a DOE Office of Science User Facility.Google Scholar
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Merging Deep Learning, Chemistry, and Diffraction for High-Throughput Material Structure Prediction
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