Hostname: page-component-586b7cd67f-t7czq Total loading time: 0 Render date: 2024-11-28T03:31:56.561Z Has data issue: false hasContentIssue false

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

Published online by Cambridge University Press:  10 August 2018

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

Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Abstract
Copyright
© Microscopy Society of America 2018 

References

References:

[1] Qin, H B, et al, Environ. Sci. Technol. 51 2017) p. 60276035.CrossRefGoogle Scholar
[3] Ong, S P, et al, Comput. Mater. Sci. 68 2013) p. 314319.CrossRefGoogle Scholar
[4] Breiman, L Mach. Learn. 45 2001) p. 532.Google Scholar
[5] Park, W B, et al, IUCrJ 4 2017) p. 486494.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