Computational Exploration of Small Hydrocarbon Networks: A Stochastic Generation and DFT Refinement Framework

Small hydrocarbons are central to astrochemistry due to their prevalence and chemical reactivity across diverse interstellar environments. Here, we present HydroMol, an open-access computational platform designed for systematic exploration and analysis of hydrocarbon chemical spaces, featuring over 2700 neutral ${\text{C}}_n{\text{H}}_m(n,m=1-10)$ hydrocarbon structures generated stochastically and refined via density functional theory at B3LYP-D3BJ/def2-SVP level. HydroMol provides detailed molecular geometries, thermodynamic parameters, and electronic properties critical for interpreting astrochemical phenomena and identifying promising observational targets. Our database, hosted within a lightweight client-side web application for rapid search and visualization, introduces approximately 2000 previously unreported hydrocarbons absent in major chemical repositories such as PubChem. Statistical analysis highlights the predominance of structurally simple monocyclic and bicyclic species, characterized by HOMO and LUMO energies centered at approximately $-6$ and $-2$ eV, respectively, and typical HOMO-LUMO gaps around 4-5 eV. The methodology is readily extendable to heavier elements and expanded property datasets, providing a valuable resource for astrochemical modeling, molecular spectroscopy, and computational hydrocarbon discovery. The HydroMol web application is freely accessible at https://hydromol.github.io.