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

2025-04-23

AI Summary

XANES spectroscopy suffers from low data acquisition efficiency due to the requirement of dense energy-point sampling in conventional scanning protocols. To address this, we propose a knowledge-injected Bayesian optimization framework that— for the first time—incorporates domain-specific XANES priors (e.g., absorption edge position and pre-edge feature characteristics) directly into the Bayesian optimization process, enabling semantic-aware, adaptive, dynamic sampling. This approach overcomes the fundamental limitation of conventional adaptive strategies, which lack physical interpretability grounded in spectroscopic principles. Our method achieves high-fidelity full-spectrum reconstruction using only 15–20% of conventional measurement points: absorption edge localization error < 0.1 eV, characteristic peak energy error < 0.03 eV, and RMSE < 0.005. The framework has been rigorously validated on static and operando experiments involving battery materials and heterogeneous catalysts, delivering substantial improvements in both temporal resolution and data acquisition efficiency.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Bayesian Learning
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Web Mining and Content Analysis: Large pretrained models with web data

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 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.

Problem

• Efficient XANES data collection with fewer measurement points
• Accurate spectral reconstruction with minimal errors in key features
• Enabling high temporal resolution for dynamic chemical tracking

Innovation

• AI-driven Bayesian optimization for XANES
• Incorporates spectral feature knowledge
• Reduces measurement points by 80%