$Symmetry prediction and knowledge discovery from X-ray diffraction patterns using an interpretable machine learning approach$
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Symmetry prediction and knowledge discovery from X-ray diffraction patterns using an interpretable machine learning approach

Abstract Determination of crystal system and space group in the initial stages of crystal structure analysis forms a bottleneck in material science workflow that often requires manual tuning. Herein we propose a machine-learning (ML)-based approach for crystal system and space group classification b...

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Autores principales:	Yuta Suzuki, Hideitsu Hino, Takafumi Hawai, Kotaro Saito, Masato Kotsugi, Kanta Ono
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f7939e475f4d47e6923216709369547d
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spelling	oai:doaj.org-article:f7939e475f4d47e6923216709369547d2021-12-02T16:18:05ZSymmetry prediction and knowledge discovery from X-ray diffraction patterns using an interpretable machine learning approach10.1038/s41598-020-77474-42045-2322https://doaj.org/article/f7939e475f4d47e6923216709369547d2020-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-77474-4https://doaj.org/toc/2045-2322Abstract Determination of crystal system and space group in the initial stages of crystal structure analysis forms a bottleneck in material science workflow that often requires manual tuning. Herein we propose a machine-learning (ML)-based approach for crystal system and space group classification based on powder X-ray diffraction (XRD) patterns as a proof of concept using simulated patterns. Our tree-ensemble-based ML model works with nearly or over 90% accuracy for crystal system classification, except for triclinic cases, and with 88% accuracy for space group classification with five candidates. We also succeeded in quantifying empirical knowledge vaguely shared among experts, showing the possibility for data-driven discovery of unrecognised characteristics embedded in experimental data by using an interpretable ML approach.Yuta SuzukiHideitsu HinoTakafumi HawaiKotaro SaitoMasato KotsugiKanta OnoNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-11 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yuta Suzuki Hideitsu Hino Takafumi Hawai Kotaro Saito Masato Kotsugi Kanta Ono Symmetry prediction and knowledge discovery from X-ray diffraction patterns using an interpretable machine learning approach
description	Abstract Determination of crystal system and space group in the initial stages of crystal structure analysis forms a bottleneck in material science workflow that often requires manual tuning. Herein we propose a machine-learning (ML)-based approach for crystal system and space group classification based on powder X-ray diffraction (XRD) patterns as a proof of concept using simulated patterns. Our tree-ensemble-based ML model works with nearly or over 90% accuracy for crystal system classification, except for triclinic cases, and with 88% accuracy for space group classification with five candidates. We also succeeded in quantifying empirical knowledge vaguely shared among experts, showing the possibility for data-driven discovery of unrecognised characteristics embedded in experimental data by using an interpretable ML approach.
format	article
author	Yuta Suzuki Hideitsu Hino Takafumi Hawai Kotaro Saito Masato Kotsugi Kanta Ono
author_facet	Yuta Suzuki Hideitsu Hino Takafumi Hawai Kotaro Saito Masato Kotsugi Kanta Ono
author_sort	Yuta Suzuki
title	Symmetry prediction and knowledge discovery from X-ray diffraction patterns using an interpretable machine learning approach
title_short	Symmetry prediction and knowledge discovery from X-ray diffraction patterns using an interpretable machine learning approach
title_full	Symmetry prediction and knowledge discovery from X-ray diffraction patterns using an interpretable machine learning approach
title_fullStr	Symmetry prediction and knowledge discovery from X-ray diffraction patterns using an interpretable machine learning approach
title_full_unstemmed	Symmetry prediction and knowledge discovery from X-ray diffraction patterns using an interpretable machine learning approach
title_sort	symmetry prediction and knowledge discovery from x-ray diffraction patterns using an interpretable machine learning approach
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/f7939e475f4d47e6923216709369547d
work_keys_str_mv	AT yutasuzuki symmetrypredictionandknowledgediscoveryfromxraydiffractionpatternsusinganinterpretablemachinelearningapproach AT hideitsuhino symmetrypredictionandknowledgediscoveryfromxraydiffractionpatternsusinganinterpretablemachinelearningapproach AT takafumihawai symmetrypredictionandknowledgediscoveryfromxraydiffractionpatternsusinganinterpretablemachinelearningapproach AT kotarosaito symmetrypredictionandknowledgediscoveryfromxraydiffractionpatternsusinganinterpretablemachinelearningapproach AT masatokotsugi symmetrypredictionandknowledgediscoveryfromxraydiffractionpatternsusinganinterpretablemachinelearningapproach AT kantaono symmetrypredictionandknowledgediscoveryfromxraydiffractionpatternsusinganinterpretablemachinelearningapproach
_version_	1718384209531240448

Symmetry prediction and knowledge discovery from X-ray diffraction patterns using an interpretable machine learning approach

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