The crystal structure of organic semiconductors is an important factor that dominates various electronic properties, including charge transport properties. However, compared with the crystal structures of inorganic semiconductors, those of organic semiconductors are difficult to determine by powder x-ray diffraction (PXRD) analysis. Our proposed machine-learning (neural-network) technique can determine the diffraction peaks buried in noise and make deconvolution of the overlapped peaks of organic semiconductors, resulting in crystal-structure determination by the Rietveld analysis. As a demonstration, we apply the method to a few high-mobility organic semiconductors and confirm that the method is potentially useful for analyzing the crystal structure of organic semiconductors. The present method is also expected to be applicable to the determination of complex crystal structures in addition to organic semiconductors.
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1 July 2024
Research Article|
July 03 2024
Powder x-ray diffraction analysis with machine learning for organic-semiconductor crystal-structure determination Available to Purchase
Naoyuki Niitsu
;
Naoyuki Niitsu
(Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Writing – original draft)
1
Material Innovation Research Center (MIRC), Department of Advanced Materials Science, Graduate School of Frontier Sciences, The University of Tokyo
, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8561, Japan
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Masato Mitani
;
Masato Mitani
(Data curation, Formal analysis)
1
Material Innovation Research Center (MIRC), Department of Advanced Materials Science, Graduate School of Frontier Sciences, The University of Tokyo
, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8561, Japan
2
Department of Chemical Science and Engineering, School of Materials and Chemical Technology, Tokyo Institute of Technology
, 4259-G1-7 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan
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Hiroyuki Ishii
;
Hiroyuki Ishii
a)
(Funding acquisition, Investigation, Validation, Visualization, Writing – original draft, Writing – review & editing)
3
Department of Applied Physics, Faculty of Pure and Applied Sciences, and Consortium of Organic-Inorganic Quantum Spin Science and Technology (OIQST), University of Tsukuba
, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan
a)Authors to whom correspondence should be addressed: [email protected]; [email protected]; and [email protected]
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Nobuhiko Kobayashi
;
Nobuhiko Kobayashi
a)
(Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Visualization, Writing – original draft)
3
Department of Applied Physics, Faculty of Pure and Applied Sciences, and Consortium of Organic-Inorganic Quantum Spin Science and Technology (OIQST), University of Tsukuba
, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan
a)Authors to whom correspondence should be addressed: [email protected]; [email protected]; and [email protected]
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Kenji Hirose;
Kenji Hirose
(Conceptualization, Investigation, Project administration, Supervision, Validation)
3
Department of Applied Physics, Faculty of Pure and Applied Sciences, and Consortium of Organic-Inorganic Quantum Spin Science and Technology (OIQST), University of Tsukuba
, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan
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Shun Watanabe
;
Shun Watanabe
(Data curation, Investigation)
1
Material Innovation Research Center (MIRC), Department of Advanced Materials Science, Graduate School of Frontier Sciences, The University of Tokyo
, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8561, Japan
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Toshihiro Okamoto
;
Toshihiro Okamoto
(Conceptualization, Data curation, Investigation, Resources, Supervision)
1
Material Innovation Research Center (MIRC), Department of Advanced Materials Science, Graduate School of Frontier Sciences, The University of Tokyo
, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8561, Japan
2
Department of Chemical Science and Engineering, School of Materials and Chemical Technology, Tokyo Institute of Technology
, 4259-G1-7 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan
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Jun Takeya
Jun Takeya
a)
(Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Supervision)
1
Material Innovation Research Center (MIRC), Department of Advanced Materials Science, Graduate School of Frontier Sciences, The University of Tokyo
, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8561, Japan
4
International Center for Materials Nanoarchitectonics (WPI-MANA), National Institute for Materials Science (NIMS)
, 1-1 Namiki, Tsukuba, Ibaraki 305-0044, Japan
a)Authors to whom correspondence should be addressed: [email protected]; [email protected]; and [email protected]
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Naoyuki Niitsu
1
Masato Mitani
1,2
Hiroyuki Ishii
3,a)
Nobuhiko Kobayashi
3,a)
Kenji Hirose
3
Shun Watanabe
1
Toshihiro Okamoto
1,2
Jun Takeya
1,4,a)
1
Material Innovation Research Center (MIRC), Department of Advanced Materials Science, Graduate School of Frontier Sciences, The University of Tokyo
, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8561, Japan
2
Department of Chemical Science and Engineering, School of Materials and Chemical Technology, Tokyo Institute of Technology
, 4259-G1-7 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan
3
Department of Applied Physics, Faculty of Pure and Applied Sciences, and Consortium of Organic-Inorganic Quantum Spin Science and Technology (OIQST), University of Tsukuba
, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan
4
International Center for Materials Nanoarchitectonics (WPI-MANA), National Institute for Materials Science (NIMS)
, 1-1 Namiki, Tsukuba, Ibaraki 305-0044, Japan
a)Authors to whom correspondence should be addressed: [email protected]; [email protected]; and [email protected]
Appl. Phys. Lett. 125, 013301 (2024)
Article history
Received:
March 18 2024
Accepted:
June 14 2024
Citation
Naoyuki Niitsu, Masato Mitani, Hiroyuki Ishii, Nobuhiko Kobayashi, Kenji Hirose, Shun Watanabe, Toshihiro Okamoto, Jun Takeya; Powder x-ray diffraction analysis with machine learning for organic-semiconductor crystal-structure determination. Appl. Phys. Lett. 1 July 2024; 125 (1): 013301. https://doi.org/10.1063/5.0208919
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