Deep Learning Enables Rapid Identification of a New Quasicrystal from Multiphase Powder Diffraction Patterns.
deep neural networks
icosahedral quasicrystals
machine learning
phase-identification
powder X-ray diffraction
Journal
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
ISSN: 2198-3844
Titre abrégé: Adv Sci (Weinh)
Pays: Germany
ID NLM: 101664569
Informations de publication
Date de publication:
14 Nov 2023
14 Nov 2023
Historique:
revised:
10
09
2023
received:
05
07
2023
medline:
15
11
2023
pubmed:
15
11
2023
entrez:
15
11
2023
Statut:
aheadofprint
Résumé
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.
Identifiants
pubmed: 37964402
doi: 10.1002/advs.202304546
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e2304546Subventions
Organisme : Japan Society for the Promotion of Science
ID : 19H05820
Organisme : Japan Society for the Promotion of Science
ID : 19H05818
Organisme : Core Research for Evolutional Science and Technology
ID : JPMJCR19I3
Organisme : Core Research for Evolutional Science and Technology
ID : JPMJCR22O3
Informations de copyright
© 2023 The Authors. Advanced Science published by Wiley-VCH GmbH.
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