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説明:

Since the discovery of the quasicrystal, approximately 100 stable quasicrystalsare identified. To date, the existence of quasicrystals is verified usingtransmission electron microscopy; however, this technique requiressignificantly more elaboration than rapid and automatic powder X-raydiffraction. Therefore, to facilitate the search for novel quasicrystals,developing a rapid technique for phase-identification from powder diffractionpatterns is desirable. This paper reports the identification of a new Al–Si–Ruquasicrystal using deep learning technologies from multiphase powderpatterns, from which it is difficult to discriminate the presence ofquasicrystalline phases even for well-trained human experts. Deep neuralnetworks trained with artificially generated multiphase powder patternsdetermine the presence of quasicrystals with an accuracy >92% from actualpowder patterns. Specifically, 440 powder patterns are screened using thetrained classifier, from which the Al–Si–Ru quasicrystal is identified. Thisstudy demonstrates an excellent potential of deep learning to identify anunknown phase of a targeted structure from powder patterns even whenexisting in a multiphase sample.

権利情報:

• Creative Commons BY Attribution 4.0 International
  

キーワード: deep learning, x-ray powder diffraction, quasicrystal, phase identification, machine learning

刊行年月日: 2023-11-14

出版者: Wiley

掲載誌:

• Advanced Science (ISSN: 21983844) vol. 11 issue. 1 2304546

研究助成金:

• Japan Society for the Promotion of Science 19H05820
• Japan Society for the Promotion of Science 19H05818
• Core Research for Evolutional Science and Technology JPMJCR19I3
• Core Research for Evolutional Science and Technology JPMJCR22O3

原稿種別: 出版者版 (Version of record)

MDR DOI:

公開URL: https://doi.org/10.1002/advs.202304546

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更新時刻: 2024-12-13 12:30:39 +0900

MDRでの公開時刻: 2024-12-13 12:30:39 +0900