Description du sujet de thèse

This project enters the general context of the need to improve our understanding of the physics and chemistry of (nano)-grains of astrophysical interest through atomistic modeling. In this respect, it aims at providing a more realistic description of these grains in terms of size, chemical composition and dynamical processes of astrophysical interest such as formation, destruction and reactivity on their surfaces. This will be achieved through a state-of-the-art methodology based on Deep Potential Molecular dynamics (DPMD).
In this context, we propose to develop a potential based on deep neural networks (Deep Neural Network Potential; DNNP) that can treat large size systems at a limited computational cost while conserving an ab initio quality. These DNNPs will further be used to perform from molecular dynamics (MD)-based simulations in order to (i) explore the potential energy landscapes of (nano)-grains of astrophysical interest and to -(ii)- quantify the physical and chemical processes of astrophysical interest undergone by these nano-grains or occurring at their surface, with unprecedented accuracy and statistics. Taking into account NQEs is made affordable by the DPMD methodology and is planned in the project to achieve greater accuracy performing Path-Integral Molecular Dynamics simulations.
We could first focus on homogeneous clusters such as (H2O)n, (CO)n and (CO2)n, before tackling the study of mixed clusters of various stoichiometries: (H2O)m(CO)n and (H2O)m(CO)n(CO2)p, as they are interesting models for icy grains. Polycyclic aromatic hydrocarbon clusters, (PAH)n, will also be investigated as they are good candidates for carbon nanograins in both space and atmosphere. Mixed (PAH)m(H2O)n clusters are also of interest to account for the (photo)-chemistry in both dense molecular clouds and in the atmosphere. In all these systems, an impurity (H+, OH-) will be incorporated as experimental results are available. Regarding reactivity, the interaction of H with (CO)m(H2O)n grains will be of particular interest to investigate the formation of HCO, presumably precursor of methanol CH3-OH, on the ice surface.

Contexte de travail

The PhD student will be based in the LCPQ “Modélisation, Agrégats, Dynamique” team at the University of Toulouse, Paul Sabatier Campus. The MAD team currently comprises 4 researchers and 3 teaching researchers, as well as two post-docs and 3 PhD students. The PhD student's work will involve using quantum chemistry programs, performing data analysis, writing data processing scripts and possibly contributing to code interfacing. A sound knowledge of quantum chemistry and physical chemistry is essential.