Modeling interstellar amorphous solid water grains by tight-binding based methods: comparison between GFN-XTB and CCSD(T) results for water clusters

TL;DR

This study benchmarks the accuracy of GFN-xTB semi-empirical methods against high-level CCSD(T) calculations for water clusters, enabling efficient modeling of interstellar amorphous solid water grains.

Contribution

It introduces GFN-xTB methods as a fast and accurate alternative for large-scale modeling of water clusters in astrochemistry, compared to traditional quantum mechanical approaches.

Findings

GFN-xTB results closely match CCSD(T) energies and structures.

GFN-xTB is two orders of magnitude faster than DFT methods.

Enables modeling of large amorphous solid water models for astrochemical studies.

Abstract

One believed path to Interstellar Complexes Organic Molecules (iCOMs) formation inside the Interstellar Medium (ISM) is through chemical recombination at the surface of amorphous solid water (ASW) mantle covering the silicate-based core of the interstellar grains. The study of these iCOMs formation and their binding energy to the ASW, using computational chemistry, depends strongly on the ASW models used, as different models may exhibit sites with different adsorbing features. ASW extended models are rare in the literature because large sizes require very large computational resources when quantum mechanical methods based on DFT are used. To circumvent this problem, we propose to use the newly developed GFN-xTB Semi-empirical Quantum Mechanical (SQM) methods from the Grimme's group. These methods are, at least, two orders of magnitude faster than conventional DFT, only require modest central memory, and in this paper we aim to benchmark their accuracy against rigorous and resource hungry quantum mechanical methods. We focused on 38 water structures studied by MP2 and CCSD(T) approaches comparing energetic and structures with three levels of GFN-xTB parametrization (GFN0, GFN1, GFN2) methods. The extremely good results obtained at the very cheap GFN-xTB level for both water cluster structures and energetic paved the way towards the modeling of very large AWS models of astrochemical interest.