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

被引:3
作者
Niitsu, Naoyuki [1 ]
Mitani, Masato [1 ,2 ]
Ishii, Hiroyuki [3 ,4 ]
Kobayashi, Nobuhiko [3 ,4 ]
Hirose, Kenji [3 ,4 ]
Watanabe, Shun [1 ]
Okamoto, Toshihiro [1 ,2 ]
Takeya, Jun [1 ,5 ]
机构
[1] Univ Tokyo, Mat Innovat Res Ctr MIRC, Grad Sch Frontier Sci, Dept Adv Mat Sci, 5-1-5 Kashiwanoha, Kashiwa, Chiba 2778561, Japan
[2] Tokyo Inst Technol, Sch Mat & Chem Technol, Dept Chem Sci & Engn, 4259-G1-7 Nagatsuta,Midori Ku, Yokohama 2268502, Japan
[3] Univ Tsukuba, Fac Pure & Appl Sci, Dept Appl Phys, 1-1-1 Tennodai, Tsukuba, Ibaraki 3058573, Japan
[4] Univ Tsukuba, Consortium Organ Inorgan Quantum Spin Sci & Techno, 1-1-1 Tennodai, Tsukuba, Ibaraki 3058573, Japan
[5] Natl Inst Mat Sci NIMS, Int Ctr Mat Nanoarchitecton WPI MANA, 1-1 Namiki, Tsukuba, Ibaraki 3050044, Japan
关键词
STRUCTURE PREDICTION; CHARGE-TRANSPORT; HIGH-MOBILITY; PERFORMANCE;
D O I
10.1063/5.0208919
中图分类号
O59 [应用物理学];
学科分类号
摘要
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.
引用
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页数:5
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