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While Bayesian inference is the gold standard for uncertainty quantificationand propagation, its use within physical chemistry encounters formidablecomputational barriers. These bottlenecks are magnified for modeling data withmany independent variables, such as X-ray/neutron scattering patterns andelectromagnetic spectra. To address this challenge, we apply a Bayesianframework accelerated via local Gaussian process (LGP) surrogate models. Weshow that the time-complexity of LGPs scales linearly in the number ofindependent variables, in stark contrast to the computationally expensive cubicscaling of conventional Gaussian processes. To illustrate the method, wetrained a LGP surrogate model on the experimental radial distribution functionof liquid neon, and observed a remarkable 288,000-fold speed-up compared tomolecular dynamics with insignificant loss in predictive accuracy. We concludethat LGPs are robust and efficient surrogate models, poised to expand theapplication of Bayesian inference in molecular simulations to a broad spectrumof ever-advancing experimental data.

Accelerated Bayesian Inference for Molecular Simulations using Local Gaussian Process Surrogate Models