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

Area of Science:

• Astrochemistry
• Computational Chemistry
• Astrobiology

Background:

• Small hydrocarbons are crucial in interstellar environments due to their abundance and reactivity.
• Understanding hydrocarbon chemistry is vital for interpreting astrochemical phenomena and identifying potential biosignatures.

Purpose of the Study:

• To introduce HydroMol, an open-access computational platform for systematic exploration and analysis of hydrocarbon chemical spaces.
• To generate and characterize a comprehensive database of neutral CnHm (n, m = 1-10) hydrocarbon structures.

Main Methods:

• Stochastic generation of over 2700 neutral CnHm hydrocarbon structures.
• Refinement of structures using density functional theory (DFT) at the B3LYP-D3BJ/def2-SVP level.
• Development of a client-side web application for rapid search and visualization of molecular data.

Main Results:

• The HydroMol database contains over 2700 hydrocarbon structures, with approximately 2000 previously unreported species.
• Detailed molecular geometries, thermodynamic parameters, and electronic properties (HOMO, LUMO, HOMO-LUMO gap) are provided.
• Statistical analysis reveals a predominance of simple monocyclic and bicyclic structures with characteristic electronic properties.

Conclusions:

• HydroMol serves as a valuable resource for astrochemical modeling, molecular spectroscopy, and computational hydrocarbon discovery.
• The platform's methodology is extendable to heavier elements and expanded property datasets.
• The freely accessible HydroMol web application facilitates research in interstellar chemistry and related fields.