Robust Binding Energy Distribution Sampling on Amorphous Solid Water Models. Method testing and validation with NH3, CO and CH4

The astrochemically efficient icy mantles surrounding dust grains in molecular clouds have been shown to be of a water-rich amorphous nature. This therefore implies a distribution of binding energies (BE) per species instead of a single value. Methods proposed so far for inferring BE and their distributions on amorphous ices rely on different approaches and approximations, leading to disparate results or BE dispersions with partially overlapping ranges. This work aims to develop a method based on a structurally reliable ice model
and a statistically and physico-chemically robust approach for BE distribution
inference, with the aim to be applicable to various relevant interstellar
species. A multiscale computational approach is presented, with a Molecular
Dynamics (MD) Heat & Quench protocol for the amorphous water ice model, and an
ONIOM(B3LYP-D3(BJ)/6-311+G**:GFN2-xtb) scheme for the BE inference, with a
prime emphasis onto the BE/real system size convergence. The sampling of the
binding configurations is twofold, exploring both regularly spaced binding
sites, as well as various adsorbate-to-substrate orientations on each locally
distinct site. This second source of BE diversity accounts for the local
roughness of the potential energy landscape of the substrate. Three different
adsorbate test cases are considered, i.e. NH3, CO and CH4, owing to their
significance in dust icy mantles, and their distinct binding behavior with
water ices. The BE distributions for NH3, CO and CH4 have been inferred, with
converged statistics. The distribution for NH3 is better represented by a
double Gaussian component profile. Three starting adsorbate orientations per
site are required to reach convergence for both Gaussian components of NH3,
while 2 orientations are sufficient for CO, and one unique for CH4 (symmetric).
Further geometrical and molecular surrounding insights have been provided.
These results encompass previously reported results.