Exploring topological indices and power curve fitting models for predicting heat of formation in magnesium aluminate network

Abstract

The prediction of thermodynamic properties using mathematical and graph-theoretical approaches has secured significant attention in materials science. The current paper is a statistical investigation of the relationship between various topological indices and the heat of formation (HOF) of the Magnesium aluminate \[Formula: see text\] network. The study fills the current gap in the systematic relationship between graph-theoretical descriptors and thermodynamic stability of structured \[Formula: see text\] networks. The HOF data is computed using a computational simulation of the related network structures under homogeneous reference conditions whereby consistency is applied in the energy assessment mechanism. Moreover the present study show a statistical insight into how different topological indices express the heat of formation in the Magnesium aluminate \[Formula: see text\] network. By taking into account various topological indices, we use a power curve-fitting technique to speculate and describe the heat of formation-an major thermodynamic element that directly act on the stability and reactivity of \[Formula: see text\]. We compute and investigate the Randic index, the Atom-Bond Connectivity (ABC) index, the Geometric-Arithmetic (GA) index, and the Zagreb index based on the chemical graph depiction in case of the HOF data. Results indicate significant predictive efficiency, with the value of R1 having the best fit (\[Formula: see text\], \[Formula: see text\]), followed by \[Formula: see text\] and GA indices. All other indices have shown to have strong correlations (\[Formula: see text\]). It is thus evident that power fitting helps in estimating the value of HOF effectively and successfully. We also found some important connections among heat of formation and topological indicators applying the power curve-fitting method. Our outcome gives proof that the curve-fitted model not only provides accurate results of the data points but also assists a good perception of the nature of the chemical interlinking within the \[Formula: see text\] network.