Physics-Informed Gaussian Process Inference of Liquid Structure from Scattering Data
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866908803647143936 |
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| author | Sullivan, Harry W. Shanks, Brennon L. Cervenka, Matej Hoepfner, Michael P. |
| author_facet | Sullivan, Harry W. Shanks, Brennon L. Cervenka, Matej Hoepfner, Michael P. |
| contents | We present a nonparametric Bayesian framework to infer radial distribution functions from experimental scattering measurements with uncertainty quantification using non-stationary Gaussian processes. The Gaussian process prior mean and kernel functions are designed to mitigate well-known numerical challenges with the Fourier transform, including discrete measurement binning and detector windowing, while encoding fundamental yet minimal physical knowledge of liquid structure. We demonstrate uncertainty propagation of the Gaussian process posterior to unmeasured quantities of interest. Experimental radial distribution functions of liquid argon and water with uncertainty quantification are provided as both a proof of principle for the method and a benchmark for molecular models. The full implementation is available on GitHub at: https://github.com/hoepfnergroup/LiquidStructureGP-Sullivan. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_07948 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Physics-Informed Gaussian Process Inference of Liquid Structure from Scattering Data Sullivan, Harry W. Shanks, Brennon L. Cervenka, Matej Hoepfner, Michael P. Chemical Physics Statistical Mechanics We present a nonparametric Bayesian framework to infer radial distribution functions from experimental scattering measurements with uncertainty quantification using non-stationary Gaussian processes. The Gaussian process prior mean and kernel functions are designed to mitigate well-known numerical challenges with the Fourier transform, including discrete measurement binning and detector windowing, while encoding fundamental yet minimal physical knowledge of liquid structure. We demonstrate uncertainty propagation of the Gaussian process posterior to unmeasured quantities of interest. Experimental radial distribution functions of liquid argon and water with uncertainty quantification are provided as both a proof of principle for the method and a benchmark for molecular models. The full implementation is available on GitHub at: https://github.com/hoepfnergroup/LiquidStructureGP-Sullivan. |
| title | Physics-Informed Gaussian Process Inference of Liquid Structure from Scattering Data |
| topic | Chemical Physics Statistical Mechanics |
| url | https://arxiv.org/abs/2507.07948 |