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High Throughput Crystal Structure Classification

Published online by Cambridge University Press:  28 July 2020

Jess Tate
Affiliation:
University of Utah, Salt Lake City, Utah, United States
Jeffery Aguiar
Affiliation:
Idaho National Laboratory, Idaho Falls, Idaho, United States
Matthew Gong
Affiliation:
University of Utah, Salt Lake City, Utah, United States
Tolga Tasdizen
Affiliation:
University of Utah, Salt Lake City, Utah, United States
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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
Advances in Modeling, Simulation, and Artificial Intelligence in Microscopy and Microanalysis for Physical and Biological Systems
Copyright
Copyright © Microscopy Society of America 2020

References

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Vasudevan, R. K., Belianinov, A., Gianfrancesco, A. G., Baddorf, A. P., Tselev, A., Kalinin, S. V., and Jesse, S.. Big data in reciprocal space: Sliding fast fourier transforms for determining periodicity. Applied Physics Letters, 106(9):091601, 2015.10.1063/1.4914016CrossRefGoogle Scholar
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Coelho, A. A.. Indexing of powder diffraction patterns by iterative use of singular value decomposition. Journal of Applied Crystallography, 36(1):8695, Feb 2003.Google Scholar
Coelho, A. A.. An indexing algorithm independent of peak position extraction for X- ray powder diffraction patterns. Journal of Applied Crystallography, 50(5):13231330, Oct 2017.10.1107/S1600576717011359CrossRefGoogle Scholar
Aguiar, J. A., Gong, M. L., Unocic, R. R., Tasdizen, T., and Miller, B. D.. Decoding crys- tallography from high-resolution electron imaging and diffraction datasets with deep learning. Science Advances, 5(10), 2019.10.1126/sciadv.aaw1949CrossRefGoogle Scholar
Aguiar, J. A., Gong, M. L., and Tasdizen, T.. Crystallographic prediction from diffraction and chemistry data for higher throughput classification using machine learning. Computational Materials Science, 173:109409, 2020.Google Scholar
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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