EXOCLUSTER: Bridging the Gap Between Gas-Phase Molecules and Bulk Particles in Exoplanetary Cloud Formation

Understanding exoplanetary atmospheres is crucial for insights into planetary formation and the potential for life beyond our solar system. Many exoplanets are shrouded by clouds, obscuring telescope measurements and complicating efforts to determine their chemical composition and formation history. Clouds form around cloud condensation nuclei (CCN), solid or liquid particles in the atmosphere. A significant portion of CCN originates from the aggregation of gas-phase molecules into clusters, which grow further into bulk particles via condensation and coagulation. The initial stages of CCN formation are crucial to understand but difficult to study experimentally, making theoretical modeling essential. However, thermochemical data for clusters between gas-phase molecules and bulk particles is scarce, and while telescopes like the James Webb Space Telescope provide unprecedented infrared (IR) spectra, a lack of reference spectra complicates their interpretation. Obtaining accurate thermochemistry data and IR spectra, which require extensive configurational sampling and molecular dynamics, is computationally unfeasible with traditional quantum chemistry methods. EXOCLUSTER tackles this challenge by using neural network potentials to efficiently generate thermochemical data and IR spectra. These tools will be made freely available through a streamlined workflow, enabling the modeling of larger and more diverse clusters, and offering new insights into exoplanetary cloud formation.