A Digital Twin Workflow for Optimizing X-ray Emission Spectroscopy (XES) Parameters in Material Science

  • In this contribution, I present a digital twin-based workflow designed to optimize experimental parameters in X-ray emission spectroscopy (XES), with a focus on reproducibility, data integration, and alignment with FAIR (Findable, Accessible, Interoperable, Reusable) principles in materials science and engineering (MSE). The developed pipeline begins with automated retrieval of crystallographic information from the Materials Project database based on a given sample composition. This structural data is then used to simulate the corresponding XES spectra using FDMNES, allowing for accurate prediction of element-specific emission lines. The simulated emission lines are fed into an X-ray tracing (XRT) module, which builds a virtual replica of the experimental setup. This digital twin environment enables predictive modeling of spectrometer performance based on geometric configurations. The optimization focuses on two key parameters: the choice of the analyzing crystal and the distanceIn this contribution, I present a digital twin-based workflow designed to optimize experimental parameters in X-ray emission spectroscopy (XES), with a focus on reproducibility, data integration, and alignment with FAIR (Findable, Accessible, Interoperable, Reusable) principles in materials science and engineering (MSE). The developed pipeline begins with automated retrieval of crystallographic information from the Materials Project database based on a given sample composition. This structural data is then used to simulate the corresponding XES spectra using FDMNES, allowing for accurate prediction of element-specific emission lines. The simulated emission lines are fed into an X-ray tracing (XRT) module, which builds a virtual replica of the experimental setup. This digital twin environment enables predictive modeling of spectrometer performance based on geometric configurations. The optimization focuses on two key parameters: the choice of the analyzing crystal and the distance between the sample and crystal (which also defines the crystal–detector distance due to Bragg condition constraints). An active learning algorithm is employed to iteratively adjust these parameters in order to achieve a desired energy-per-pixel (E/pixel) resolution with minimal intensity loss, enabling efficient, data-driven experimental planning. By integrating data-driven simulations with real-time optimization strategies, this workflow supports efficient experiment planning while minimizing resource consumption and human error. Furthermore, all stages of the process—from data collection and simulation to optimization and visualization—are structured to ensure traceability and interoperability, facilitating future reuse and collaborative research. I hope this contribution aligns well with the topic “Workflows for FAIR MSE Data” and offers a concrete example of how digital twins can be harnessed to improve the design, execution, and documentation of spectroscopy experiments in the MSE domain.zeige mehrzeige weniger

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Autor*innen:Cafer Tufan Cakir, Ana de Oliveira Guilherme BuzanichORCiD, Martin RadtkeORCiD
Dokumenttyp:Vortrag
Veröffentlichungsform:Präsentation
Sprache:Englisch
Jahr der Erstveröffentlichung:2025
Organisationseinheit der BAM:6 Materialchemie
6 Materialchemie / 6.3 Strukturanalytik
DDC-Klassifikation:Naturwissenschaften und Mathematik / Chemie / Analytische Chemie
Freie Schlagwörter:Digital-Twin; Machine Learning; Optimization; XES
Themenfelder/Aktivitätsfelder der BAM:Chemie und Prozesstechnik
Chemie und Prozesstechnik / Chemische Charakterisierung und Spurenanalytik
Veranstaltung:Opportunities and Challenges of FAIR Data at Photon and Neutron Facilities
Veranstaltungsort:Bad Honnef, Germany
Beginndatum der Veranstaltung:12.10.2025
Enddatum der Veranstaltung:15.10.2025
Verfügbarkeit des Dokuments:Datei im Netzwerk der BAM verfügbar ("Closed Access")
Datum der Freischaltung:12.11.2025
Referierte Publikation:Nein
Eingeladener Vortrag (wissenschaftliche Konferenzen):Nein
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