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Machine Learning-based Crystal Structure Prediction for X-Ray Microdiffraction

Published online by Cambridge University Press:  10 August 2018

Yuta Suzuki ,
Hideitsu Hino ,
Yasuo Takeichi ,
Takafumi Hawai ,
Masato Kotsugi  and
Kanta Ono
Yuta Suzuki
Affiliation:
Tokyo University of Science, Department of Materials Science and Technology, Tokyo, Japan High Energy Accelerator Research Organization, Institute of Materials Structure Science, Ibaraki, Japan
Hideitsu Hino
Affiliation:
The Institute of Statistical Mathematics, Tokyo, Japan
Yasuo Takeichi
Affiliation:
High Energy Accelerator Research Organization, Institute of Materials Structure Science, Ibaraki, Japan
Takafumi Hawai
Affiliation:
High Energy Accelerator Research Organization, Institute of Materials Structure Science, Ibaraki, Japan
Masato Kotsugi
Affiliation:
Tokyo University of Science, Department of Materials Science and Technology, Tokyo, Japan
Kanta Ono*
Affiliation:
High Energy Accelerator Research Organization, Institute of Materials Structure Science, Ibaraki, Japan
*
*Corresponding author, kanta.ono@kek.jp

Abstract

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Abstract
Information
Microscopy and Microanalysis , Volume 24 , Supplement S2: Proceedings of the 14th International Conference on X-ray Microscopy (XRM2018) , August 2018 , pp. 142 - 143
Copyright
© Microscopy Society of America 2018 

References

References:

[1] Qin, H B, et al, Environ. Sci. Technol. 51 2017) p. 6027–6035.CrossRefGoogle Scholar
[2] https://www.fiz-karlsruhe.de/en/leistungen/kristallographie/icsd.html.Google Scholar
[3] Ong, S P, et al, Comput. Mater. Sci. 68 2013) p. 314–319.CrossRefGoogle Scholar
[4] Breiman, L Mach. Learn. 45 2001) p. 5–32.Google Scholar
[5] Park, W B, et al, IUCrJ 4 2017) p. 486–494.Google Scholar
[6] Ueno, T, et al, npj Comput. Mater 4 2018) p. 4.CrossRefGoogle Scholar
[7] This work was partly supported by JST CREST JPMJCR1761.Google Scholar