Quantum chemical calculations help us investigating the adsorption of S-bearing species on a crystalline water ice model, highlighting the importance of dispersive forces in determining the strenght of their interaction.
There are different environments in the interstellar medium (ISM), depending on the density, temperature and chemical composition. Among them, molecular clouds, often referred to as the cradle of stars, are paradigmatic environments relative to the chemical diversity and complexity in space. Indeed, there, radio to far-infrared observations revealed the presence of several molecules in the gas phase, while near-infrared spectroscopy detected the existence of submicron sized dust grains covered by H2O-dominated ice mantles. The interaction between gas-phase species and the surfaces of water ices is measured by the binding energy (BE), a crucial parameter in astrochemical modelling. In this work, the BEs of a set of sulphur- containing species on water ice mantles have been computed by adopting a periodic ab initio approach using a crystalline surface model. The Density Functional Theory (DFT)-based B3LYP-D3(BJ) functional was used for the prediction of the structures and energetics. DFT BEs were refined by adopting an ONIOM-like procedure to estimate them at CCSD(T) level toward complete basis set extrapolation, in which a very good correlation between values has been found. Moreover, we show that geometry optimization with the computationally cheaper HF-3c method followed by single point energy calculations at DFT to compute the BEs is a suitable cost-effective recipe to arrive at BE values of the same quality as those computed at full DFT level. Finally, computed data were compared with the available literature data.
This work is published as a book chapter in: Perrero J., Rimola A., Corno M., Ugliengo P. (2021) Ab initio Calculation of Binding Energies of Interstellar Sulphur-Containing Species on Crystalline Water Ice Models. In: Gervasi O. et al. (eds) Computational Science and Its Applications – ICCSA 2021. ICCSA 2021. Lecture Notes in Computer Science, vol 12953. Springer, Cham.
Link to the article: https://link.springer.com/chapter/10.1007%2F978-3-030-86976-2_41