Solving the inverse problem of X-ray absorption spectroscopy via physics-informed deep learning
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866914430037524480 |
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| author | Zhong, Suyang Huang, Boying Xu, Pengwei Xu, Fanjie Zhao, Yuhao Cheng, Jun Tang, Fujie E, Weinan Tian, Zhong-Qun |
| author_facet | Zhong, Suyang Huang, Boying Xu, Pengwei Xu, Fanjie Zhao, Yuhao Cheng, Jun Tang, Fujie E, Weinan Tian, Zhong-Qun |
| contents | Resolving transient atomic configurations in non-crystalline or dynamic environments remains a fundamental bottleneck in the physical sciences. While X-ray absorption spectroscopy (XAS) is a premier probe of local structure, inverting spectra into structural descriptors is a notoriously ill-posed problem due to inherent many-to-one mapping. Here, we present the Spectral Pattern Translator (SPT), a physics-informed deep learning framework that establishes a robust bridge between large-scale theoretical datasets and experimental reality. Our strategy exploits the Fourier duality between spectral energy oscillations and spatial scattering paths to overcome the "simulation-to-experiment" gap. By decomposing spectra into frequency domains, SPT effectively isolates robust structural coordination signals from the destabilizing noise inherent in experimental data. Trained on a massive library of diverse atomic environments, this approach achieves state-of-the-art accuracy in resolving continuous phase transitions in battery cathodes and deciphering local order in amorphous materials. With millisecond-scale latency, SPT removes the primary computational barrier to autonomous materials discovery, establishing a robust, noise-resilient engine for closed-loop robotic chemistry. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_27684 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Solving the inverse problem of X-ray absorption spectroscopy via physics-informed deep learning Zhong, Suyang Huang, Boying Xu, Pengwei Xu, Fanjie Zhao, Yuhao Cheng, Jun Tang, Fujie E, Weinan Tian, Zhong-Qun Materials Science Computational Physics Data Analysis, Statistics and Probability Resolving transient atomic configurations in non-crystalline or dynamic environments remains a fundamental bottleneck in the physical sciences. While X-ray absorption spectroscopy (XAS) is a premier probe of local structure, inverting spectra into structural descriptors is a notoriously ill-posed problem due to inherent many-to-one mapping. Here, we present the Spectral Pattern Translator (SPT), a physics-informed deep learning framework that establishes a robust bridge between large-scale theoretical datasets and experimental reality. Our strategy exploits the Fourier duality between spectral energy oscillations and spatial scattering paths to overcome the "simulation-to-experiment" gap. By decomposing spectra into frequency domains, SPT effectively isolates robust structural coordination signals from the destabilizing noise inherent in experimental data. Trained on a massive library of diverse atomic environments, this approach achieves state-of-the-art accuracy in resolving continuous phase transitions in battery cathodes and deciphering local order in amorphous materials. With millisecond-scale latency, SPT removes the primary computational barrier to autonomous materials discovery, establishing a robust, noise-resilient engine for closed-loop robotic chemistry. |
| title | Solving the inverse problem of X-ray absorption spectroscopy via physics-informed deep learning |
| topic | Materials Science Computational Physics Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2603.27684 |