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Updated: Jan 16, 2026
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Published on: January 28, 2021
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Data-driven ELNES/XANES analysis: predicting spectra, unveiling structures and quantifying properties
1Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro, Tokyo 113-8505, Japan.
Microscopy (Oxford, England)
|October 5, 2025
Summary
Data-driven methods revolutionize core-loss spectroscopy (electron energy loss near-edge structures/ELNES and X-ray absorption near-edge structures/XANES). These advanced techniques accelerate simulations, extract material properties, and enable faster materials discovery.
Area of Science:
- Materials Science
- Spectroscopy
- Computational Materials Science
Background:
- Core-loss spectroscopies like ELNES and XANES are vital for materials characterization.
- Traditional analysis relies on qualitative interpretation or reference spectra.
- Limitations exist in quantitative analysis and predictive capabilities.
Purpose of the Study:
- To review novel data-driven methodologies for ELNES/XANES analysis.
- To highlight advancements beyond conventional spectral interpretation.
- To showcase the potential for accelerated materials discovery.
Main Methods:
- Application of machine learning (ML) and data-driven approaches to spectral data.
- Development of methods for accelerating ELNES/XANES simulations.
- Utilizing sensitivity analysis to interpret ML model predictions.
Main Results:
- Data-driven methods enable quantitative extraction of radial distribution functions.
- Multiple material properties can be quantified directly from spectral data.
- Accelerated simulations and enhanced interpretability of ML models.
Conclusions:
- Novel data-driven approaches significantly enhance ELNES/XANES analysis.
- These methods facilitate deeper understanding and faster materials discovery.
- The future points to automated, interpretable, and scalable spectroscopy for materials science.
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