The spectroscopic study of interstellar and circumstellar molecules has been long ongoing, with researchers concluding already in the 1970s that interstellar dust contains large numbers of complex organic molecules (COMs). Since then, ever improving searches have found about 50 such COMs. Besides being of great interest for astrochemistry and some researchers even pointing out their potential role regarding the origin of life, COMs also serve as valuable probes for the physical conditions of the surrounding medium.
The build-up of molecular complexity in a given system can be studied with chemical reaction networks (CRNs), mathematical models of the concentrations of various molecules based on a fixed set of reactions and an initial set of reactant concentrations. By expanding the previously studied CRNs with additional dust grain-surface reactions, we tried to improve the description of COM formation in protoplanetary disks. Trying to automate some time-consuming manual tasks necessary for studies of the influence of physical and chemical parameters, I wrote an analysis framework that will enable future students to conduct similar studies much more efficiently, thereby opening new research possibilities.
Complex Organic Molecules (COMs) in protoplanetary disks have been the subject of extensive studies using chemical reaction networks (CRNs) (e.g. Walsh et al., 2014). The accuracy of these models depends on our knowledge of the relevant chemical processes. Some classes of reactions have been comprehensively studied, resulting in large databases like the UMIST database of astrochemistry, which lists more than 6000 gas-phase reactions. However, other classes of reactions, such as grain-surface reactions, still pose challenges.
By expanding the previously studied CRNs with additional grain-surface reactions that are currently studied in new laboratory experiments (and have so far mostly been considered in the context of meteorites), we tried to improve the description of COM formation in protoplanetary disks. More specifically, I have been using the existing simulation code to investigate the influence of physical and chemical parameters, such as temperature, density and activation energies, on the time evolution of the chemistry found on grains. Trying to automate some time-consuming manual tasks necessary for such studies, I wrote a framework to repeatedly run the simulation with different settings and to visualize the resulting datasets. This framework will enable future students to conduct similar studies much more efficiently, thereby opening new research possibilities.