Deep Learning Enables Rapid Identification of a New Quasicrystal from Multiphase Powder Diffraction Patterns
Abstract Since the discovery of the quasicrystal, approximately 100 stable quasicrystals are identified. To date, the existence of quasicrystals is verified using transmission electron microscopy; however, this technique requires significantly more elaboration than rapid and automatic powder X‐ray d...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | Advanced Science |
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| Online Access: | https://doi.org/10.1002/advs.202304546 |
| _version_ | 1797365332063551488 |
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| author | Hirotaka Uryu Tsunetomo Yamada Koichi Kitahara Alok Singh Yutaka Iwasaki Kaoru Kimura Kanta Hiroki Naoya Miyao Asuka Ishikawa Ryuji Tamura Satoshi Ohhashi Chang Liu Ryo Yoshida |
| author_facet | Hirotaka Uryu Tsunetomo Yamada Koichi Kitahara Alok Singh Yutaka Iwasaki Kaoru Kimura Kanta Hiroki Naoya Miyao Asuka Ishikawa Ryuji Tamura Satoshi Ohhashi Chang Liu Ryo Yoshida |
| author_sort | Hirotaka Uryu |
| collection | DOAJ |
| description | Abstract Since the discovery of the quasicrystal, approximately 100 stable quasicrystals are identified. To date, the existence of quasicrystals is verified using transmission electron microscopy; however, this technique requires significantly more elaboration than rapid and automatic powder X‐ray diffraction. Therefore, to facilitate the search for novel quasicrystals, developing a rapid technique for phase‐identification from powder diffraction patterns is desirable. This paper reports the identification of a new Al–Si–Ru quasicrystal using deep learning technologies from multiphase powder patterns, from which it is difficult to discriminate the presence of quasicrystalline phases even for well‐trained human experts. Deep neural networks trained with artificially generated multiphase powder patterns determine the presence of quasicrystals with an accuracy >92% from actual powder patterns. Specifically, 440 powder patterns are screened using the trained classifier, from which the Al–Si–Ru quasicrystal is identified. This study demonstrates an excellent potential of deep learning to identify an unknown phase of a targeted structure from powder patterns even when existing in a multiphase sample. |
| first_indexed | 2024-03-08T16:48:36Z |
| format | Article |
| id | doaj.art-fb5ad7241ef54a9d8f6fe4fef0ac9c3e |
| institution | Directory Open Access Journal |
| issn | 2198-3844 |
| language | English |
| last_indexed | 2024-03-08T16:48:36Z |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Science |
| spelling | doaj.art-fb5ad7241ef54a9d8f6fe4fef0ac9c3e2024-01-05T08:26:58ZengWileyAdvanced Science2198-38442024-01-01111n/an/a10.1002/advs.202304546Deep Learning Enables Rapid Identification of a New Quasicrystal from Multiphase Powder Diffraction PatternsHirotaka Uryu0Tsunetomo Yamada1Koichi Kitahara2Alok Singh3Yutaka Iwasaki4Kaoru Kimura5Kanta Hiroki6Naoya Miyao7Asuka Ishikawa8Ryuji Tamura9Satoshi Ohhashi10Chang Liu11Ryo Yoshida12Department of Applied Physics Tokyo University of Science 6‐3‐1 Niijuku, Katsushika‐ku Tokyo 125‐8585 JapanDepartment of Applied Physics Tokyo University of Science 6‐3‐1 Niijuku, Katsushika‐ku Tokyo 125‐8585 JapanDepartment of Materials Science and Engineering National Defense Academy 1‐10‐20 Hashirimizu, Yokosuka Kanagawa 239‐8686 JapanElectron Microscopy Unit, Research Network and Facility Services Division National Institute for Materials Science 1‐2‐1 Sengen Tsukuba Ibaraki 305‐0047 JapanDepartment of Advanced Materials Science The University of Tokyo 5‐1‐5 Kashiwanoha, Kashiwa Chiba 277‐8561 JapanDepartment of Advanced Materials Science The University of Tokyo 5‐1‐5 Kashiwanoha, Kashiwa Chiba 277‐8561 JapanDepartment of Applied Physics Tokyo University of Science 6‐3‐1 Niijuku, Katsushika‐ku Tokyo 125‐8585 JapanDepartment of Materials Science and Technology Tokyo University of Science 6‐3‐1 Niijuku, Katsushika‐ku Tokyo 125‐8585 JapanResearch Institute of Science and Technology Tokyo University of Science 6‐3‐1 Niijuku, Katsushika‐ku Tokyo 125‐8585 JapanDepartment of Materials Science and Technology Tokyo University of Science 6‐3‐1 Niijuku, Katsushika‐ku Tokyo 125‐8585 JapanInstitute of Multidisciplinary Research for Advanced Materials Tohoku University 2‐1‐1 Katahira, Aoba‐ku, Sendai Miyagi 980‐8577 JapanThe Institute of Statistical Mathematics Research Organization of Information and Systems 10‐3 Midori‐cho, Tachikawa Tokyo 190‐8562 JapanThe Institute of Statistical Mathematics Research Organization of Information and Systems 10‐3 Midori‐cho, Tachikawa Tokyo 190‐8562 JapanAbstract Since the discovery of the quasicrystal, approximately 100 stable quasicrystals are identified. To date, the existence of quasicrystals is verified using transmission electron microscopy; however, this technique requires significantly more elaboration than rapid and automatic powder X‐ray diffraction. Therefore, to facilitate the search for novel quasicrystals, developing a rapid technique for phase‐identification from powder diffraction patterns is desirable. This paper reports the identification of a new Al–Si–Ru quasicrystal using deep learning technologies from multiphase powder patterns, from which it is difficult to discriminate the presence of quasicrystalline phases even for well‐trained human experts. Deep neural networks trained with artificially generated multiphase powder patterns determine the presence of quasicrystals with an accuracy >92% from actual powder patterns. Specifically, 440 powder patterns are screened using the trained classifier, from which the Al–Si–Ru quasicrystal is identified. This study demonstrates an excellent potential of deep learning to identify an unknown phase of a targeted structure from powder patterns even when existing in a multiphase sample.https://doi.org/10.1002/advs.202304546deep neural networksicosahedral quasicrystalsmachine learningphase‐identificationpowder X‐ray diffraction |
| spellingShingle | Hirotaka Uryu Tsunetomo Yamada Koichi Kitahara Alok Singh Yutaka Iwasaki Kaoru Kimura Kanta Hiroki Naoya Miyao Asuka Ishikawa Ryuji Tamura Satoshi Ohhashi Chang Liu Ryo Yoshida Deep Learning Enables Rapid Identification of a New Quasicrystal from Multiphase Powder Diffraction Patterns Advanced Science deep neural networks icosahedral quasicrystals machine learning phase‐identification powder X‐ray diffraction |
| title | Deep Learning Enables Rapid Identification of a New Quasicrystal from Multiphase Powder Diffraction Patterns |
| title_full | Deep Learning Enables Rapid Identification of a New Quasicrystal from Multiphase Powder Diffraction Patterns |
| title_fullStr | Deep Learning Enables Rapid Identification of a New Quasicrystal from Multiphase Powder Diffraction Patterns |
| title_full_unstemmed | Deep Learning Enables Rapid Identification of a New Quasicrystal from Multiphase Powder Diffraction Patterns |
| title_short | Deep Learning Enables Rapid Identification of a New Quasicrystal from Multiphase Powder Diffraction Patterns |
| title_sort | deep learning enables rapid identification of a new quasicrystal from multiphase powder diffraction patterns |
| topic | deep neural networks icosahedral quasicrystals machine learning phase‐identification powder X‐ray diffraction |
| url | https://doi.org/10.1002/advs.202304546 |
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