Modeling the heat of formation of indium telluride via graph entropy descriptors and curve fitting techniques

This study presents an extensive graph-theoretic analysis of the thermodynamic properties of indium telluride (InTe). We analyze the significant relationship between the molecular structure of InTe and its macroscopic behavior through the application of graph entropy, a fundamental metric in information theory and statistical thermodynamics. In this framework, the chemical structure is depicted as a graph, with atoms as vertices and chemical bonds as edges. We calculate a set of topological indices for InTe, a material that is very useful in thermoelectrics, phase-change memory, and infrared optics. These indices encode the material's structural connectivity in numbers. These indices are used to figure out a range of graph entropies, which measure how complex and information-rich the molecular lattice is. The main part of this work shows strong quantitative links between these graph entropies and important thermodynamic properties, such as heat capacity and heat of formation. We use suitable quadratic surface-fitting models that show exactly how these thermodynamic responses depend on pairs of graph entropies in a nonlinear way. All computational modeling and analysis are conducted to enhance statistical fit, guaranteeing that the chosen models deliver the most dependable predictive capability for comprehending and anticipating the material's stability and thermal characteristics.