Powder x-ray diffraction analysis with machine learning for organic-semiconductor crystal-structure determination

Author:

Niitsu Naoyuki1ORCID,Mitani Masato12ORCID,Ishii Hiroyuki3ORCID,Kobayashi Nobuhiko3ORCID,Hirose Kenji3,Watanabe Shun1ORCID,Okamoto Toshihiro12ORCID,Takeya Jun14ORCID

Affiliation:

1. Material Innovation Research Center (MIRC), Department of Advanced Materials Science, Graduate School of Frontier Sciences, The University of Tokyo 1 , 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 2 , 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 3 , 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan

4. International Center for Materials Nanoarchitectonics (WPI-MANA), National Institute for Materials Science (NIMS) 4 , 1-1 Namiki, Tsukuba, Ibaraki 305-0044, Japan

Abstract

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.

Funder

Japan Society for the Promotion of Science

Core Research for Evolutional Science and Technology

Publisher

AIP Publishing

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