Speaker
Description
For a long time, space was thought to be a hostile environment characterized by extreme conditions, in which the formation of any molecular system was highly unlikely, if not impossible. However, advances in three fundamental areas of molecular astrophysics (theoretical modeling, experimental laboratories, and observational missions), as well as, their joint effort are reponsible for more than 290 molecules [1] have been already detected up to now. In that way, the intrigue grows with each new discovery, and the question "What comes next?" becomes more complex as the number of viable species increases. From this point of view, in the last decade, two of the most fascinated detection have been noble gas hydride cation complexes, HeH$^{+}$ and ArH$^{+}$, due to their well-known high electronic stability [2,3,4,5]. All this has generated a great deal of interest and a desire to learn more about the interstellar-chemistry of noble gases.
In this vein, our main goal is to exploe trends and models using quantum chemistry computations in order to collect astrochemically relevant data that could be serve to facilitate the possible detection of new molecules containing noble gases in the interstellar medium [4,5,6]. To accomplish this, we propose a machine learning-based approach to construct new accurate potential energy surfaces from ab initio electronic structure calculations. Such computational techniques and tools allow to computationally characterize species like [Ng$_{n}$H$_{m}$]$^{+}$ aiming to understand their chemical binding and electron exchange in clusters of noble gas hydride cations.
