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Atmaca, Sümeyye; Sikorska, Celina, 2026, "Supporting data for the assessment of superatom-like fullerenes via an unsupervised machine learning approach", https://doi.org/10.18150/ASF6SV, RepOD, V1
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The molecular descriptors for twenty-six superatom-like fullerenes (FDs) have been obtained at the semi-empirical theory level using the open-source MOPAC package. The dataset contains constitutional and quantum-mechanical (QM) descriptors. The standard enthalpy of formation (HOF, in kcal/mol), dipole moment (u, in D), energy of the highest occupied molecular orbital (EHOMO, in eV), energy of the lowest unoccupied molecular orbital (ELUMO, in eV), molecular volume (CV, in Å3); solvent accessible surface area (CA, in Å2), molecular weight (MW), total number of atoms (nAT), number of carbon atoms (nC), number of hydrogen atoms (nH), number of heteroatoms (nHet), absolute hardness (η=(ELUMO–EHOMO)/2, in eV), absolute electronegativity (χ=–(ELUMO+EHOMO)/2, in eV), reactivity index (S=χ2/2η), EHOMO/ELUMO ratio, difference between total molecular weight and molecular weight of isolated unsubstituted carbon cage (MW–MWcage), and difference between total molecular volume and molecular volume of isolated unsubstituted carbon cage (CV–CVcage).
superatoms, electronic structure, fullerene derivatives, molecular descriptors, principal component analysis (PCA), hierarchical cluster analysis (HCA)
Sümeyye Atmaca, Celina Sikorska, Efficiency of an unsupervised machine learning approach for assessing superatom-like fullerenes, TASK Quarterly, Vol. 29 No. 4 (2025), DOI: https://doi.org/10.34808/tq2025/29.4/a :
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