Understanding the quantitative structure-property relationship (QSPR) of potential HED hydrocarbon candidates is crucial for the development of next-generation SAFs once the hydrocarbons are identified. Recently, machine learning (ML) has been employed to screen potential HED fuels from a large database of liquid hydrocarbons . For example, up to 28 highly potent polycyclic hydrocarbon compounds, possessing compatible properties with respect to JP-10 and quadricyclanes (QC, C7H8) fuels, have been identified from a database with as many as 319,895 hydrocarbon candidates . Li et al reveal that these candidates exhibit unique spatial structures dominated by optimal C7-C12 atoms, multiple C-rings particularly three-member triangular rings (C3, cyclopropanation) . However, further studies are warranted to elucidate why saturated polycyclic alkanes exhibit such spatial structures, and how high energy is stored in these structures.