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Demonstration of an AI-driven workflow for dynamic x-ray spectroscopy

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Publicat a:NPJ Computational Materials vol. 11, no. 1 (2025), p. 320-334
Autor principal: Du, Ming
Altres autors: Wolfman, Mark, Sun, Chengjun, Kelly, Shelly D., Cherukara, Mathew J.
Publicat:
Nature Publishing Group
Matèries:
Data collection
Temporal resolution
Bayesian analysis
Oxidation
Neutrons
Spectrum analysis
Fourier transforms
X ray absorption
Sampling methods
Adaptive sampling
Optimization
Spectroscopy
Spectra
Energy
Automation
Error reduction
Algorithms
Sampling
X-ray spectroscopy
Expected values
X-rays
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Resum:X-ray absorption near edge structure (XANES) spectroscopy is a powerful technique for characterizing the chemical state and symmetry of individual elements within materials, but requires collecting data at many energy points which can be time-consuming. While adaptive sampling methods exist for efficiently collecting spectroscopic data, they often lack domain-specific knowledge about the structure of XANES spectra. Here we demonstrate a knowledge-injected Bayesian optimization approach for adaptive XANES data collection that incorporates understanding of spectral features like absorption edges and pre-edge peaks. We show this method accurately reconstructs the absorption edge of XANES spectra using only 15–20% of the measurement points typically needed for conventional sampling, while maintaining the ability to determine the x-ray energy of the sharp peak after the absorption edge with errors less than 0.03 eV, the absorption edge with errors less than 0.1 eV; and overall root-mean-square errors less than 0.005 compared to traditionally sampled spectra. Our experiments on battery materials and catalysts demonstrate the method’s effectiveness for both static and dynamic XANES measurements, improving data collection efficiency and enabling better time resolution for tracking chemical changes. This approach advances the degree of automation in XANES experiments, reducing the common errors of under- or over-sampling points near the absorption edge and enabling dynamic experiments that require high temporal resolution or limited measurement time.
ISSN:2057-3960
DOI:10.1038/s41524-025-01771-7
Font:Health & Medical Collection