Gaussian Process Model for Collision Dynamics of Complex Molecules

TL;DR

This paper demonstrates that Gaussian Process models, trained on a limited set of classical scattering calculations, can accurately predict complex collision dynamics and observables for molecules, enabling efficient inverse problems and sensitivity analysis.

Contribution

The authors introduce a Gaussian Process approach that combines classical and quantum calculations to model multi-dimensional scattering dependencies with few simulations.

Findings

200 classical trajectories suffice for 10D hypersurface modeling

Model accurately predicts quantum scattering cross sections near resonances

Enables efficient inverse scattering and sensitivity analysis

Abstract

We show that a Gaussian Process model can be combined with a small number (of order 100) of scattering calculations to provide a multi-dimensional dependence of scattering observables on the experimentally controllable parameters such as the collision energy or temperature) as well as the potential energy surface (PES) parameters. For the case of Ar - C$_6$H$_6$ collisions, we show that 200 classical trajectory calculations are sufficient to provide a 10-dimensional hypersurface, giving the dependence of the collision lifetimes on the collision energy, internal temperature and 8 PES parameters. This can be used for solving the inverse scattering problem, the efficient calculation of thermally averaged observables, for reducing the error of the molecular dynamics calculations by averaging over the PES variations, and the analysis of the sensitivity of the observables to individual parameters determining the PES.Trained by a combination of classical and quantum calculations, the model provides an accurate description of the quantum scattering cross sections, even near scattering resonances.