Work

Physics-Informed Gaussian Process Inference of Liquid Structure from Scattering Data

Year: 2025

Type: article

Abstract: We present a nonparametric Bayesian framework to infer radial distribution functions from experimental scattering measurements with uncertainty quantification using nonstationary 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 the 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.

Source: The Journal of Physical Chemistry B

DOI: 10.1021/acs.jpcb.5c05024

Authors: Harry Winston Sullivan, Matej Cervenka, Brennon L. Shanks, Michael P. Hoepfner

Institutions: University of Minnesota, Czech Academy of Sciences, Institute of Organic Chemistry and Biochemistry, University of Utah

Language: English

Topic: Gaussian Processes and Bayesian Inference

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: Basic Energy Sciences

Award: G2877323426