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Meeting 2027 TMS Annual Meeting & Exhibition
Symposium AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling
Presentation Title Design of Experiments for Efficient XRF Under Compositional Uncertainty
Author(s) Abigail C. Hefferan, Aaron Stebner
On-Site Speaker (Planned) Abigail C. Hefferan
Abstract Scope Traditional X-ray Fluorescence (XRF) requires a preliminary scan to select optimal parameters and then a full rerun compared to calibration standards to accurately identify concentrations of different elements in a sample. The expense of preliminary scans and calibrating the XRF means that the traditional XRF analysis framework is inefficient for bulk characterization of samples. Previous experimental planning tools for XRF require prior knowledge of the composition to plan around. We propose a design of experiments (DOE) model that can output the optimal experimental parameters for samples with high compositional uncertainty. Modeling many plausible compositions lets the model select sweep angles, step size, and excitation energy robust to that uncertainty. By optimizing the first scan itself, the DOE model could eliminate the need to rerun XRF experiments for bulk characterization of samples within an autonomous lab.
Proceedings Inclusion? Planned:
Keywords Characterization, Computational Materials Science & Engineering,

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