دورية أكاديمية
Convolutional neural network framework for the automated analysis of transition metal X-ray photoelectron spectra.
| العنوان: | Convolutional neural network framework for the automated analysis of transition metal X-ray photoelectron spectra. |
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| المؤلفون: | Pielsticker L; Max Planck Institute for Chemical Energy Conversion, Stiftstr. 34-36, 45470, Muelheim an der Ruhr, Germany. Electronic address: lukas.pielsticker@cec.mpg.de., Nicholls RL; Max Planck Institute for Chemical Energy Conversion, Stiftstr. 34-36, 45470, Muelheim an der Ruhr, Germany., DeBeer S; Max Planck Institute for Chemical Energy Conversion, Stiftstr. 34-36, 45470, Muelheim an der Ruhr, Germany., Greiner M; Max Planck Institute for Chemical Energy Conversion, Stiftstr. 34-36, 45470, Muelheim an der Ruhr, Germany. |
| المصدر: | Analytica chimica acta [Anal Chim Acta] 2023 Aug 29; Vol. 1271, pp. 341433. Date of Electronic Publication: 2023 May 27. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Elsevier Country of Publication: Netherlands NLM ID: 0370534 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-4324 (Electronic) Linking ISSN: 00032670 NLM ISO Abbreviation: Anal Chim Acta Subsets: PubMed not MEDLINE; MEDLINE |
| أسماء مطبوعة: | Publication: Amsterdam : Elsevier Original Publication: Amsterdam. |
| مواضيع طبية MeSH: | Neural Networks, Computer*, X-Rays ; Reproducibility of Results |
| مستخلص: | X-ray photoelectron spectroscopy is an indispensable technique for the quantitative determination of sample composition and electronic structure in diverse research fields. Quantitative analysis of the phases present in XP spectra is usually conducted manually by means of empirical peak fitting performed by trained spectroscopists. However, with recent advancements in the usability and reliability of XPS instruments, ever more (inexperienced) users are creating increasingly large data sets that are harder to analyze by hand. In order to aid users with the analysis of large XPS data sets, more automated, easy-to-use analysis techniques are needed. Here, we propose a supervised machine learning framework based on artificial convolutional neural networks. By training such networks on large numbers of artificially created XP spectra with known quantifications (i.e., for each spectrum, the concentration of each chemical species is known), we created universally applicable models for auto-quantification of transition-metal XPS data that are able to predict the sample composition from spectra within seconds. Upon evaluation against more traditional peak fitting methods, we showed that these neural networks achieve competitive quantification accuracy. The proposed framework is shown to be flexible enough to accommodate spectra containing multiple chemical elements and measured with different experimental parameters. The use of dropout variational inference for the determination of quantification uncertainty is illustrated. Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (Copyright © 2023 Elsevier B.V. All rights reserved.) |
| فهرسة مساهمة: | Keywords: Automated analysis; Convolutional neural networks; Electron spectroscopy; Supervised machine learning; Transition metals |
| تواريخ الأحداث: | Date Created: 20230616 Date Completed: 20230619 Latest Revision: 20230619 |
| رمز التحديث: | 20240628 |
| DOI: | 10.1016/j.aca.2023.341433 |
| PMID: | 37328241 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 1873-4324 |
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| DOI: | 10.1016/j.aca.2023.341433 |