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Spectra-to-Structure and Structure-to-Spectra Inference Across the Periodic Table
Created by
Haebom
Author
Yufeng Wang, Peiyao Wang, Lu Wei, Lu Ma, Yuewei Lin, Qun Liu, Haibin Ling
Outline
This paper presents XAStruct, a learning-based system for __T2871_____-line absorption spectroscopy (XAS) interpretation. Trained on a large dataset containing over 70 elements, XAStruct generalizes to a wide range of chemistries and bonding environments. It includes the first machine learning approach to directly predict neighboring atomic types from XAS spectra and a generalizable regression model for average nearest neighbor distances that does not require element-specific tuning. By combining deep neural networks for complex structure-property mapping with efficient baseline models for simple tasks, it provides a scalable and extensible data-driven XAS analysis and local structure inference solution.
Takeaways, Limitations
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Takeaways:
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Provides a general XAS analysis model applicable to various elements and chemical environments.
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Direct prediction of neighboring atomic types from XAS spectra.
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Predicting average nearest neighbor distance without element-specific adjustments.
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Providing a scalable and extensible solution for data-driven XAS analysis and local structure inference.
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Limitations:
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The source code will be made public after the paper is accepted.
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A more detailed evaluation of the model's performance and generalization ability is needed.
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Applicability to specific complex structures or amorphous systems needs to be verified.
