2026-03-02 アルゴンヌ国立研究所（ANL）
Artistic rendering shows new AI-guided approach capturing absorption edge from atomic structure of material analyzed by XANES at a light source. (Image by Argonne National Laboratory.)
＜関連情報＞
- https://www.anl.gov/article/artificial-intelligence-makes-xray-spectroscopy-five-times-faster-smarter-and-less-prone-to-human
- https://www.nature.com/articles/s41524-025-01771-7
動的X線分光法のためのAI駆動型ワークフローのデモンストレーション Demonstration of an AI-driven workflow for dynamic x-ray spectroscopy
Ming Du,Mark Wolfman,Chengjun Sun,Shelly D. Kelly & Mathew J. Cherukara
npj Computational Materials 27 October 2025
DOI:https://doi.org/10.1038/s41524-025-01771-7
Abstract
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.
