Zitieren Sie bitte immer diesen URN: urn:nbn:de:kobv:b43-603955
Machine learning for efficient grazing-exit x-ray absorption near edge structure spectroscopy analysis: Bayesian optimization approach
- In materials science, traditional techniques for analyzing layered structures are essential for obtaining information about local structure, electronic properties and chemical states. While valuable, these methods often require high vacuum environments and have limited depth profiling capabilities. The grazing exit x-ray absorption near-edge structure (GE-XANES) technique addresses these limitations by providing depth-resolved insight at ambient conditions, facilitating in situ material analysis without special sample preparation. However, GE-XANES is limited by long data acquisition times, which hinders its practicality for various applications. To overcome this, we have incorporated Bayesian optimization (BO) into the GE-XANES data acquisition process. This innovative approach potentially reduces measurement time by a factor of 50. We have used a standard GE-XANES experiment, which serve as reference, to validate the effectiveness and accuracy of the BO-informed experimental setup.In materials science, traditional techniques for analyzing layered structures are essential for obtaining information about local structure, electronic properties and chemical states. While valuable, these methods often require high vacuum environments and have limited depth profiling capabilities. The grazing exit x-ray absorption near-edge structure (GE-XANES) technique addresses these limitations by providing depth-resolved insight at ambient conditions, facilitating in situ material analysis without special sample preparation. However, GE-XANES is limited by long data acquisition times, which hinders its practicality for various applications. To overcome this, we have incorporated Bayesian optimization (BO) into the GE-XANES data acquisition process. This innovative approach potentially reduces measurement time by a factor of 50. We have used a standard GE-XANES experiment, which serve as reference, to validate the effectiveness and accuracy of the BO-informed experimental setup. Our results show that this optimized approach maintains data quality while significantly improving efficiency, making GE-XANES more accessible to a wider range of materials science applications.…
Autor*innen: | Cafer Tufan CakirORCiD, Can Bogoclu, Franziska Emmerling, Christina Streli, Ana Guilherme BuzanichORCiD, Martin RadtkeORCiD |
---|---|
Dokumenttyp: | Zeitschriftenartikel |
Veröffentlichungsform: | Verlagsliteratur |
Sprache: | Englisch |
Titel des übergeordneten Werkes (Englisch): | Machine Learning: Science and Technology |
Jahr der Erstveröffentlichung: | 2024 |
Organisationseinheit der BAM: | 6 Materialchemie |
6 Materialchemie / 6.3 Strukturanalytik | |
Veröffentlichende Institution: | Bundesanstalt für Materialforschung und -prüfung (BAM) |
Verlag: | IOP Publishing |
Jahrgang/Band: | 5 |
Ausgabe/Heft: | 2 |
Aufsatznummer: | 025037 |
Erste Seite: | 1 |
Letzte Seite: | 12 |
DDC-Klassifikation: | Technik, Medizin, angewandte Wissenschaften / Ingenieurwissenschaften / Ingenieurwissenschaften und zugeordnete Tätigkeiten |
Freie Schlagwörter: | Bayesian Optimization; GE-XANES; Machine Learning |
Themenfelder/Aktivitätsfelder der BAM: | Material |
Material / Materialdesign | |
DOI: | 10.1088/2632-2153/ad4253 |
URN: | urn:nbn:de:kobv:b43-603955 |
Verfügbarkeit des Dokuments: | Datei für die Öffentlichkeit verfügbar ("Open Access") |
Lizenz (Deutsch): | Creative Commons - CC BY - Namensnennung 4.0 International |
Datum der Freischaltung: | 26.06.2024 |
Referierte Publikation: | Ja |
Datum der Eintragung als referierte Publikation: | 26.06.2024 |
Schriftenreihen ohne Nummerierung: | Wissenschaftliche Artikel der BAM |