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Machine learning for direct quantification of XRF measurements

  • In this talk I’ll describe the use of artificial neural networks (ANN) for quantifying X-ray fluorescence (XRF) measurements. The main idea of this talk is to give an overview of the process needed to generate a model that can then be applied to a specific problem. In XRF, a sample is excited with X-rays and the resulting characteristic radiation is detected to determine elements quantitatively and qualitatively. This is traditionally done in several time-consuming steps. I’ll show the possibilities and problems of using a neural network to realise a "one-click" quantification. This includes generating training data using Monte Carlo simulation and augmenting the existing data set with an ANN to generate more data. The search for the optimal hyperparameters, manually and automatically, is also described. For the case presented, we were able to train a network with a mean absolute error of 0.1% by weight for the synthetic data and 0.7% by weight for a set of experimental dataIn this talk I’ll describe the use of artificial neural networks (ANN) for quantifying X-ray fluorescence (XRF) measurements. The main idea of this talk is to give an overview of the process needed to generate a model that can then be applied to a specific problem. In XRF, a sample is excited with X-rays and the resulting characteristic radiation is detected to determine elements quantitatively and qualitatively. This is traditionally done in several time-consuming steps. I’ll show the possibilities and problems of using a neural network to realise a "one-click" quantification. This includes generating training data using Monte Carlo simulation and augmenting the existing data set with an ANN to generate more data. The search for the optimal hyperparameters, manually and automatically, is also described. For the case presented, we were able to train a network with a mean absolute error of 0.1% by weight for the synthetic data and 0.7% by weight for a set of experimental data obtained with certified reference materials.zeige mehrzeige weniger

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  • ML_seminar2021 Grenoble.pdf
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Metadaten
Autor*innen:Martin RadtkeORCiD
Dokumenttyp:Vortrag
Veröffentlichungsform:Präsentation
Sprache:Englisch
Jahr der Erstveröffentlichung:2021
Organisationseinheit der BAM:6 Materialchemie
6 Materialchemie / 6.3 Strukturanalytik
DDC-Klassifikation:Naturwissenschaften und Mathematik / Chemie / Analytische Chemie
Freie Schlagwörter:Artificial intelligence; Machine learning; Synchrotron; XRF
Themenfelder/Aktivitätsfelder der BAM:Chemie und Prozesstechnik
Chemie und Prozesstechnik / Chemische Charakterisierung und Spurenanalytik
Veranstaltung:Seminar series: Artificial Intelligence applied to X-ray / synchrotron techniques
Veranstaltungsort:Online meeting
Beginndatum der Veranstaltung:24.06.2021
Verfügbarkeit des Dokuments:Datei im Netzwerk der BAM verfügbar ("Closed Access")
Datum der Freischaltung:21.12.2021
Referierte Publikation:Nein
Eingeladener Vortrag:Nein
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