Predictive Modeling of Structural and Conductivity Properties of Honeycomb Benzenoid Systems via Topological Indices

Chemical graph theory is a foundational field of mathematical chemistry and provides strong methods for studying the structures of molecules using numerical data descriptors called topological indices. It is one of the key factors in determining stability and defining the molecular connectivity among other types including degree‐based and neighborhood degree–based topological indices. We discuss the Kulli–Basava (KB) family of indices, which comprises K B ₁ , K B ₂ , H K B ₁ , H K B ₂ , S K B , P K B , A B C K B , G A K B , and R K B , and neighborhood degree–based topological indices, in the current study to systematize the assessment of the stability of the honeycomb benzenoid (HB) system and its structural complexity. Regression analysis, analysis of variance (ANOVA), and regression coefficients are used to create sound statistical correlations, which demonstrate the usefulness of these indices in predicting the molecular properties. In addition to this, the indices are again reevaluated to study the conductivity and voltaic behavior of the HB system and show improved charge transport and delocalization with an increase in system size. The statistical correlation analysis is further broadened to understand the relationship between the indices computed and to provide better insight into the predictability of the indices. The result indicates that the HB system has graphene‐like electronic properties; thus, it can be a promising candidate for the development of advanced nanomaterials and electronics. This overall work highlights the significance of the degree‐based topological indices of neighborhoods in molecular modeling and points to their prospective uses in nanoelectronics and material science.