Neural-Network Assisted Study of Nitrogen Atom Dynamics on Amorphous Solid Water. I. Adsorption & Desorption

TL;DR

This study uses machine learning-enhanced molecular dynamics to analyze nitrogen atom adsorption and desorption on amorphous water ice, revealing rapid energy dissipation and temperature-dependent sticking behavior relevant to interstellar chemistry.

Contribution

It introduces a computational approach combining machine-learned potentials with molecular dynamics to efficiently simulate adsorption dynamics of radicals on dust surfaces.

Findings

Sticking coefficient near unity at 10 K

Desorption time scale of 1 microsecond at 28 K

Energy dissipation occurs within picoseconds at 10 K

Abstract

Dynamics of adsorption and desorption of (4S)-N on amorphous solid water are analyzed using molecular dynamics simulations. The underlying potential energy surface was provided by machine-learned interatomic potentials. Binding energies confirm the latest available theoretical and experimental results. The nitrogen sticking coefficient is close to unity at dust temperatures of 10 K but decreases at higher temperatures. We estimate a desorption time scale of 1 {\mu}s at 28 K. The estimated time scale allows chemical processes mediated by diffusion to happen before desorption, even at higher temperatures. We found that the energy dissipation process after a sticking event happens on the picosecond timescale at dust temperatures of 10 K, even for high energies of the incoming adsorbate. Our approach allows the simulation of large systems for reasonable time scales at an affordable computational cost and ab-initio accuracy. Moreover, it is generally applicable for the study of adsorption dynamics of interstellar radicals on dust surfaces.