Lost in Translation: Simulation-Informed Bayesian Inference Improves Characterization of Molecular Motion from Neutron Scattering

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 an ambiguous or inaccurate mechanistic interpretation. By integrating molecular dynamics simulations, physically derived Q-dependent scattering models, Bayesian model discrimination, and polarization 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 stronger anisotropy than previously recognized. 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.