AI-Driven XANES Prediction: Universal and Experiment-Calibrated
Published:Dec 29, 2025 13:12
•1 min read
•ArXiv
Analysis
This paper addresses the limitations of current XANES simulation methods by developing an AI model for faster and more accurate prediction. The key innovation is the use of a crystal graph neural network pre-trained on simulated data and then calibrated with experimental data. This approach allows for universal prediction across multiple elements and significantly improves the accuracy of the predictions, especially when compared to experimental data. The work is significant because it provides a more efficient and reliable method for analyzing XANES spectra, which is crucial for materials characterization, particularly in areas like battery research.
Key Takeaways
- •Developed an AI model for XANES prediction using a crystal graph neural network.
- •The model is pre-trained on simulated data and calibrated with experimental data.
- •Achieves universal XANES prediction across 48 elements.
- •Significantly reduces edge energy misalignment error after calibration.
- •Provides a faster and more accurate method for XANES analysis.
Reference
“The method demonstrated in this work opens up a new way to achieve fast, universal, and experiment-calibrated XANES prediction.”