A Graph Neural Network-Based Approach to XANES Data Analysis
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2022
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| _version_ | 1866910037101772800 |
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| author | Zhan, Fei Zheng, Lirong Yao, Haodong Geng, Zhi Yu, Can Han, Xue Song, Xueqi Chen, Shuguang Zhao, Haifeng |
| author_facet | Zhan, Fei Zheng, Lirong Yao, Haodong Geng, Zhi Yu, Can Han, Xue Song, Xueqi Chen, Shuguang Zhao, Haifeng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2205_04463 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | A Graph Neural Network-Based Approach to XANES Data Analysis Zhan, Fei Zheng, Lirong Yao, Haodong Geng, Zhi Yu, Can Han, Xue Song, Xueqi Chen, Shuguang Zhao, Haifeng Chemical Physics 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. |
| title | A Graph Neural Network-Based Approach to XANES Data Analysis |
| topic | Chemical Physics |
| url | https://arxiv.org/abs/2205.04463 |