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

arXiv: https://arxiv.org/abs/2504.17124 · HTML: https://mlsanity.com/paper/2504.17124/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 46 references that are in the map.
- First posted on arXiv 23 Apr 2025.

## Builds on (most cited first, 3 of 3)

- [Deep Kernel Learning](https://mlsanity.com/paper/1511.02222/) (2015)
- [A Framework for Dynamic Image Sampling Based on Supervised Learning (SLADS)](https://mlsanity.com/paper/1703.04653/) (2017)
- [SLADS-Net: Supervised Learning Approach for Dynamic Sampling using Deep Neural Networks](https://mlsanity.com/paper/1803.02972/) (2018)

## Built on by (most cited first, 1 of 1)

- [EAA: Automating materials characterization with vision language model agents](https://mlsanity.com/paper/2602.15294/) (2026)

## Abstract (arXiv)

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 XANES spectra structure. 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 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 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 in near the absorption edge and enabling dynamic experiments that require high temporal resolution or limited measurement time.