Digital Twin for Multimodal Synchrotron Experiments
- 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.…

