Susan Gregurick (@sgregurick.bsky.social)

pubs.acs.org/doi/10.1021/... neat work on a probabilistic machine learning framework to estimate total or partial RDFs with uncertainty quantification from background and inelastic corrected scattering data. The results could help with folks interested in molecular force field optimization.

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Physics-Informed Gaussian Process Inference of Liquid Structure from Scattering Data 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. https://pubs.acs.org/doi/10.1021/acs.jpcb.5c05024