X-ray absorption spectroscopy (XAS) is an indispensable tool to characterize the atomic-scale three-dimensional local structure of the system, in which XANES is the most important energy region to reflect the three-dimensional structure. However quantitative analysis of three-dimensional structure from XANES requires users to have a deep understanding and accurate judgment of structural information and summarize several structural parameters, which is often difficult to achieve. In this work, We construct \textbf{physics-informed Graph neural network} and \textbf{Transformer} models for calculating XANES from the input three-dimensional structure; we improve the efficiency of the model based on the physical meaning of XAS; then we combine the model and optimization algorithm to fit the three-dimensional structure of given system. This method does not require users to summarize the structural parameters, has wide application range. It can be applied to the three-dimensional structure analysis of solid materials and has positive significance for the study of structure-function relationship in the fields of energy and catalysis. In addition, this method is expected to be developed into an online three-dimensional structure analysis method for XAS related beamlines.
@article{arxiv.2205.04463,
title = {A Graph Neural Network-Based Approach to XANES Data Analysis},
author = {Fei Zhan and Lirong Zheng and Haodong Yao and Zhi Geng and Can Yu and Xue Han and Xueqi Song and Shuguang Chen and Haifeng Zhao},
journal= {arXiv preprint arXiv:2205.04463},
year = {2026}
}