Lost in Translation: Simulation-Informed Bayesian Inference Improves Understanding of Molecular Motion From Neutron Scattering

TL;DR

This paper introduces a Bayesian, simulation-informed framework for analyzing neutron scattering data, enabling precise resolution of molecular rotational and translational motions, exemplified by liquid benzene.

Contribution

It combines molecular dynamics, Bayesian model discrimination, and polarization analysis to improve interpretation of neutron scattering spectra.

Findings

Resolved anisotropic rotational motion in liquid benzene.

Found stronger anisotropy in diffusion coefficients than previously known.

Established a new Bayesian analytical paradigm for QENS data.

Abstract

Quasi-elastic neutron scattering (QENS) probes atomic and molecular motion on length and time scales central to catalysis, energy materials, and gas adsorption. However, conventional analytical fitting of QENS spectra often fails to uniquely determine the underlying dynamics. The flexibility of simplified line-shape models can make spectra generated by distinct physical processes statistically indistinguishable, leading to ambiguous or inaccurate mechanistic interpretation. By integrating molecular dynamics simulations, physically derived $Q$-dependent scattering models, Bayesian model discrimination, and polarisation analysis, we demonstrate that QENS can, for the first time, resolve anisotropic rotational motion in liquid benzene, a prototypical aromatic molecule relevant to microporous catalysis. The extracted spinning and tumbling diffusion coefficients suggest substantially stronger anisotropy than previously recognised. This integrated, Bayesian evidence-based analytical framework defines a new paradigm for QENS, enabling direct resolution of the rotational and translational dynamics that govern molecular interactions and transport; the fundamental processes and rate-limiting steps in confined hydrocarbon catalysis.