Thesis title: Gas-grain models and artificial intelligence application to improve astrochemical reactions.
Supervisor: Dr. C. Ceccarelli (University of Grenoble Alpes - IPAG)
Co-Supervisor: Prof. P. Ugliengo (University of Torino) & S. Chiadò (Vastalla Srl)
Recruitment Institution: University of Grenoble Alpes, France, and University of Torino, Italy
Doctoral School: University of Grenoble Alpes, France
Mobility: The ESR will spend the first 12 months at IPAG, the following 18 months at University of Torino, and the last 6 months at IPAG.
Eligibility: European and non-European students who not have resided or carried out their main activity (work, studies, ect.) in France for more than 12 months in the 3 years immediately before the recruitment date.
Despite the harsh conditions (extremely low pressure and temperature) prevailing in the interstellar medium (ISM), more than 200 molecules have been detected so far. How these molecules form is often subject to debate, as the interstellar chemistry is substantial different from that occurring in terrestrial laboratories. Nonetheless, astrochemists have built up models which are able to reproduce some of the astronomical observations even though they unable to account for several simple and complex ones. These models rely on the so-called astrochemical networks, namely the networks of reactions supposed to occur in the ISM. Current astrochemical networks count more than 8000 reactions in the gas phase and a similar number on the grains surface. Unfortunately, however, only a small fraction (not more than 15%) of these reactions have been studied in the laboratories or theoretically. Verifying the reliability of all the reactions in the astrochemical networks via dedicated experiments and/or theoretical computations is such a gigantic challenge that is simply impossible. The approach of this thesis is to use artificial intelligence techniques in order to do this job. This will take advantage of the connection with the various experiments and theoretical computations carried out by the members of the ACO team. The goal of the thesis is not only to "clean" the present astrochemical networks as much as possible but also to propose new possible reactions not taken into account at present.
The thesis is part of the ACO network, whose ultimate goal is to reconstruct the early history of the Solar System by comparing presently forming solar-type planetary systems with its small bodies. The comparison will be based on the most advanced astrochemical knowledge, which will be developed by the interdisciplinary ACO team.