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

  • Matthew L. Gong (a1) (a2), Brandon D. Miller (a1), Ray R. Unocic (a3), Khallid Hattar (a4), Bryan Reed (a5), Dan Masiel (a5), Tolga Tasdizen (a2) and Jeffery A. Aguiar (a1)...
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      Merging Deep Learning, Chemistry, and Diffraction for High-Throughput Material Structure Prediction
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[1]Vasudevan, R.K. et al. , ACS Nano. 8 (2014). doi:10.1021/nn504730n.
[2]Vasudevan, R.K. et al. , Appl Phys Lett. 106 (2015). doi:10.1063/1.4914016.
[3]Dongarra, J et al. , Int J High Perform Comput Appl. 25 (2011). doi:10.1177/1094342010391989.
[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.

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

  • Matthew L. Gong (a1) (a2), Brandon D. Miller (a1), Ray R. Unocic (a3), Khallid Hattar (a4), Bryan Reed (a5), Dan Masiel (a5), Tolga Tasdizen (a2) and Jeffery A. Aguiar (a1)...

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