論文 Rapid, comprehensive search of crystalline phases from X-ray diffraction in seconds via GPU-accelerated Bayesian variational inference

Ryo Murakami SAMURAI ORCID (National Institute for Materials Science) ; Kenji Nagata SAMURAI ORCID (National Institute for Materials Science) ; Yoshitaka Matsushita SAMURAI ORCID (National Institute for Materials Science) ; Masahiko Demura SAMURAI ORCID (National Institute for Materials Science)

Rapid comprehensive search of crystalline phases from X-ray diffraction in seconds via GPU-accelerated Bayesian variational inference.pdf
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引用
Ryo Murakami, Kenji Nagata, Yoshitaka Matsushita, Masahiko Demura. Rapid, comprehensive search of crystalline phases from X-ray diffraction in seconds via GPU-accelerated Bayesian variational inference. SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS-METHODS. 2025, 5 (1), 2485016. https://doi.org/10.1080/27660400.2025.2485016

説明:

(abstract)

In analysis of X-ray diffraction data, identifying the crystalline phase is important for interpreting the material. The typical method is identifying the crystalline phase from the coincidence of the main diffraction peaks. This method identifies crystalline phases by matching them as individual crystalline phases rather than as combinations of crystalline phases, in the same way as the greedy method. If multiple candidates are obtained, the researcher must subjectively select the crystalline phases. Thus, the identification results depend on the researcher's experience and knowledge of materials science. To solve this problem, we have developed a Bayesian estimation method to identify the combination of crystalline phases, taking the entire profile into account. This method estimates the Bayesian posterior probability of crystalline phase combinations by performing an approximate exhaustive search of all possible combinations. It is a method for identifying crystalline phases that takes into account all peak shapes and phase combinations. However, it takes a few hours to obtain the analysis results. The aim of this study is to develop a Bayesian method for crystalline phase identification that can provide results in seconds, which is a practical calculation time. We introduce variational sparse estimation and GPU computing. Our method is able to provide results within 10 seconds even when analysing 250 candidate crystalline phase combinations. Furthermore, the crystalline phases identified by our method are consistent with the results of previous studies that used a high-precision algorithm.

権利情報:

キーワード: X-ray diffraction, GPU computing, sparse modeling, bayesian inference, model selection

刊行年月日: 2025-12-31

出版者: Informa UK Limited

掲載誌:

  • SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS-METHODS (ISSN: 27660400) vol. 5 issue. 1 2485016

研究助成金:

  • JST JPMJGX23S6 (GteX Program Japan)

原稿種別: 出版者版 (Version of record)

MDR DOI:

公開URL: https://doi.org/10.1080/27660400.2025.2485016

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更新時刻: 2025-07-26 08:30:18 +0900

MDRでの公開時刻: 2025-07-26 08:16:42 +0900

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