A computational study of entropy measures and topological indices for Magnesium Nitride using curve fitting...

This study presents a computational analysis of entropy measures and topological indices for the Magnesium Nitride network, utilizing mathematical modeling and curve fitting techniques to establish structure-property relationships. Various degree-based topological indices, including the Zagreb indices, Randić index, and Forgotten index, are calculated to characterize the molecular graph of Magnesium Nitride. In this work, the structural complexity of Magnesium Nitrogen networks is studied along with their topological indices and entropy measures. The work attempts to characterize the topological properties and dynamic behavior of Magnesium Nitrogen by using a variety of entropy measures, including degree entropy, Shannon entropy, and spectral entropy. In addition, rational curve fitting techniques are employed to model the complex relationships between entropy measures and structural parameters of the network. More precise predictions and a deeper comprehension of the system's behavior under various circumstances are made possible by the application of curve fitting. The results show the essential role of entropy measurements in the study of network connectivity and stability, providing information on the potential uses of the material in domains including materials science and network optimization.