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September 28, 2025Journal of Computational Chemistry

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

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Authors

SGSandip GiriIndian Institute of Technology KharagpurSSSayon SatpatiNational Institute of Technology DurgapurTRTarun Kumar RoyUniversity of California, San Diego

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Implication

This platform analyzes small hydrocarbons' properties in astrochemistry, suggesting factors for observational targets.

Key Points

  • HydroMol reveals over 2000 new hydrocarbon structures, aiding astrophysical investigations and chemical understanding.
  • Statistical analysis shows the dominance of monocyclic and bicyclic hydrocarbons, with average HOMO-LUMO gaps around 4–5 eV.
  • This computational framework utilizes density functional theory for high-accuracy predictions of molecular properties.
  • The findings highlight potential implications for astrochemical modeling and future spectroscopic studies into hydrocarbon structures.

Cite This Study

Giri et al. (2025) studied this question.

synapsesocial.com/papers/68d90a0641e1c178a14f651chttps://doi.org/10.1002/jcc.70236
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