Information-theoretic entropy and topological descriptor analysis of tin oxide (SnO₂) for structural and property prediction.

In recent years, topological descriptors have emerged as powerful tools for exploring the structural complexity and physico-chemical behavior of molecular networks. While extensive studies have been devoted to nanostructures and dendrimer systems, the mathematical modeling of tin oxide (SnO₂)-a material of high importance in sensing, catalysis, and nanotechnology-remains largely unexplored. Motivated by this gap, we develop and compute some important topological descriptors of the graph representation of SnO₂ and employ them to investigate its information-theoretic entropies. Furthermore, a quantitative structure-property relationship (QSPR) analysis is carried out using linear regression models to establish correlations between the computed indices and entropy values. The statistical parameters including correlation coefficients, F-values, and standard errors confirm the robustness and predictive power of the proposed models. Both mathematical derivations and graphical line-fit representations validate the strong compatibility of the regression framework with the data. The results highlight the intricate relationship between molecular structure, entropy, and predictive modeling, thus providing new insights into the characterization of complex oxide systems. This study not only advances the theoretical understanding of SnO₂ but also sets the foundation for further applications of topological descriptors and entropy measures in materials science and nanotechnology.