Innovative Sample Structure Prediction using Bayesian Estimation and XPS Simulator

• 資料種別: 記事
• タイトル: Innovative Sample Structure Prediction using Bayesian Estimation and XPS Simulator
• 著者・編者: Hiroshi Shinotsuka; Kenji Nagata; Hideki Yoshikawa; Hayaru Shouno; Masato Okada
• 著者標目: Hiroshi Shinotsuka; Kenji Nagata; Hideki Yoshikawa; Hayaru Shouno; Masato Okada
• タイトル（掲載誌）: Journal of surface analysis / 表面分析研究会 編
• 巻号年月日等（掲載誌）: 31(3):2025.3
• 掲載巻: 31
• 掲載号: 3
• 掲載ページ: 224-233
• 掲載年月日（W3CDTF）: 2025-03
• ISSN（掲載誌）: 1341-1756
• ISSN-L（掲載誌）: 1341-1756
• 出版事項（掲載誌）: 東京 : 表面分析研究会
• 出版地（国名コード）: JP
• 本文の言語コード: eng
• NDLC: ZM35
• 対象利用者: 一般
• 所蔵機関: 国立国会図書館
• 請求記号: Z15-B66
• 連携機関・データベース: 国立国会図書館 : 国立国会図書館雑誌記事索引; https://ndlsearch.ndl.go.jp
• 書誌ID（NDLBibID）: 034060463; http://id.ndl.go.jp/bib/034060463
• 整理区分コード: 632

• 要約等: We have developed a framework that integrates the XPS simulator SESSA into Bayesian estimation to solve the inverse problem of XPS. This approach automates the typically labor-intensive task of manually adjusting sample structure parameters for XPS simulation. By incorporating Bayesian methods, we can estimate the distribution of plausible sample structures based on experimental XPS data, providing not only an optimal solution but also a detailed visualization of the solution's distribution. In a case study, we performed angle-resolved XPS on a four-layered sample and successfully estimated the sample structure using this framework. This method streamlines the analysis of XPS data and offers a comprehensive view of sample structures, marking a significant step forward in the application of data science techniques to experimental data.
• DOI: 10.1384/jsa.31.224
• オンライン閲覧公開範囲: インターネット公開
• 連携機関・データベース: 科学技術振興機構 : J-STAGE; http://www.jstage.jst.go.jp

• 要約等: We have developed a framework that integrates the XPS simulator SESSA into Bayesian estimation to solve the inverse problem of XPS. This approach automates the typically labor-intensive task of manually adjusting sample structure parameters for XPS simulation. By incorporating Bayesian methods, we can estimate the distribution of plausible sample structures based on experimental XPS data, providing not only an optimal solution but also a detailed visualization of the solution's distribution. In a case study, we performed angle-resolved XPS on a four-layered sample and successfully estimated the sample structure using this framework. This method streamlines the analysis of XPS data and offers a comprehensive view of sample structures, marking a significant step forward in the application of data science techniques to experimental data.
• DOI: 10.1384/jsa.31.224; https://doi.org/10.1384/jsa.31.224
• 関連情報（URI）: http://id.ndl.go.jp/bib/034060463; https://ndlsearch.ndl.go.jp/books/R000000004-I034060463; https://www.jstage.jst.go.jp/article/jsa/31/3/31_224/_pdf
• 参照: Sample structure prediction from measured XPS data using Bayesian estimation and SESSA simulator; https://cir.nii.ac.jp/crid/1050016206128606080; Development of multiple core-level XPS spectra decomposition method based on the Bayesian information criterion; https://cir.nii.ac.jp/crid/1050579156082006656; Bayesian inference method utilizing SESSA in quantitative layer structure estimation from XPS data; https://cir.nii.ac.jp/crid/1050869378915317760; Development of spectral decomposition based on Bayesian information criterion with estimation of confidence interval; https://cir.nii.ac.jp/crid/1360009142889193344; Bayesian estimation for XPS spectral analysis at multiple core levels; https://cir.nii.ac.jp/crid/1360294643791223552; Bayesian spectral deconvolution with the exchange Monte Carlo method; https://cir.nii.ac.jp/crid/1360565166044783616; Simulation of electron spectra for surface analysis (SESSA) version 2.2.0 user s guide; https://cir.nii.ac.jp/crid/1360579817461516032; Bayesian inference for peak feature extraction and prediction of material property in X-ray diffraction data; https://cir.nii.ac.jp/crid/1360869454372775168; Exchange Monte Carlo method and application to spin glass simulation; https://cir.nii.ac.jp/crid/1390001204180017792
• 連携機関・データベース: 国立情報学研究所 : CiNii Research; https://cir.nii.ac.jp/
• 提供元機関・データベース: Japan Link Center; https://japanlinkcenter.org/top; 雑誌記事索引データベース; https://ndlsearch.ndl.go.jp; Crossref; https://www.crossref.org
• 書誌ID（NDLBibID）: 034060463; http://id.ndl.go.jp/bib/034060463