Chemical Kinetics and the Interstellar Carbon Budget

A chemical network connects molecules through thousands of reactions in the gas and on dust grains. Given physical conditions and an initial elemental inventory, a kinetics model follows how the abundances change with time. For many aromatic species in TMC-1, existing models predict far less material than we observe. We use that disagreement to investigate which reactions or physical assumptions need attention.

Our work combines updates to specific chemical families with sensitivity analyses of the network as a whole. These studies identify reactions involving small carbon species and the formation of the first aromatic rings as important bottlenecks. We also examine how the carbon-to-oxygen ratio changes the available carbon reservoirs and the growth of larger molecules. In that study, changing the C/O ratio alone did not reproduce the observed gas-phase carbon inventory, pointing to additional questions about freeze-out and the return of material to the gas.

The aim is to identify the measurements and calculations that will most usefully improve the chemistry. A reaction can be poorly known without controlling the answer, while an apparently minor channel can strongly change the predicted molecular population. Testing those sensitivities helps us choose where to put the next experimental effort.

Machine Learning for Chemical Discovery

We use machine learning to explore relationships between molecular identity, abundance, and detectability. Numerical representations place chemically related molecules near one another in a chemical space, allowing models trained on an observed inventory to recommend additional targets. These approaches have helped motivate successful searches, including the work on 2-methoxyethanol. They complement reaction-network models, but do not by themselves explain how a molecule forms.

A predicted abundance is only part of the problem. A molecule also needs observable transitions, reliable spectroscopic data, and signals that can be separated from other species. Our retrospective tests of inventory models and work connecting predictions with spectroscopy examine these limits and how they affect target selection. We also develop software such as ChemXploreML, a modular application for molecular property prediction.

A newer direction explores whether future quantum-computing methods, combined with machine learning, could improve molecular predictions and help assign complicated spectra. This is a research effort in development; experimentally measured frequencies remain central to our astronomical searches.