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  1. Home
  3. Universal Rapid Machine Learning Models For Predicting Unconvoluted And Convoluted X-ray Absorption Spectra.
  1. Home
  3. Universal Rapid Machine Learning Models For Predicting Unconvoluted And Convoluted X-ray Absorption Spectra.

Related Experiment Video

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements
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Published on: June 28, 2016

Universal Rapid Machine Learning Models for Predicting Unconvoluted and Convoluted X-ray Absorption Spectra.

Fei Zhan1, Zhi Geng1

  • 1Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, CN 100049, China.

The Journal of Physical Chemistry. A
|June 9, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a new X-ray absorption near-edge structure (XANES) prediction model. The model accurately predicts XANES spectra from 3D atomic structures, enabling real-time validation in experiments.

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Area of Science:

  • Materials Science
  • Spectroscopy
  • Computational Chemistry

Background:

  • X-ray absorption near-edge structure (XANES) is crucial for analyzing local 3D atomic structures in materials and molecules.
  • Accurate and rapid computation of XANES spectra from structural data is essential for quantitative analysis.
  • Current methods often require element-specific models and extensive data.

Purpose of the Study:

  • To develop a universal XANES prediction model that accepts 3D atomic structures as input.
  • To demonstrate the model's generalizability across different X-ray absorption spectroscopy (XAS) types and elements.
  • To enable rapid, real-time structural validation during XAS experiments.

Main Methods:

  • Development of a novel computational model for XANES prediction.
  • Input: 3D atomic structures of materials or molecules.
  • Output: Predicted XANES or unconvoluted XANES spectra.

    • Main Results:

    • The model demonstrates excellent generalizability across diverse broadening conditions.
    • Validated predictive accuracy for hard X-ray XAS (3d/4d transition metals K-edge) and soft X-ray XAS (sulfur K-edge).
    • Successfully predicts XANES spectra even with sparse 3D structural data.
    • A single unified model can predict XANES for multiple elements, eliminating the need for element-specific models.

    Conclusions:

    • The developed XANES prediction model offers a powerful, unified approach for analyzing local atomic structures.
    • It significantly enhances the efficiency and accessibility of quantitative XANES analysis.
    • Empowers researchers with real-time structural validation capabilities during XAS experiments.