Topological analysis of entropy measure using regression models for diamond structure

TL;DR

This paper explores the relationship between topological indices and entropy measures in diamond molecular structures using regression models, providing insights into their structural information and predictive capabilities.

Contribution

It introduces a novel analysis of Nirmala indices and entropy measures for diamond structures, utilizing regression models to understand their interrelation.

Findings

Nirmala index and entropy measures are computed for diamond structures.

Regression models reveal significant relationships between indices and entropy.

Numerical and graphical analyses support the correlation findings.

Abstract

A numerical parameter, referred to as a topological index, is used to represent the molecular structure of a compound by analyzing its graph-theoretical characteristics. Topological indices are predictive methods for the physicochemical properties of chemical compounds in the context of quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) analysis. Graph entropies have evolved into information-theoretic tools for exploring the structural information of molecular graphs. In this paper, we compute the Nirmala index, as well as the first and second inverse Nirmala index, for the diamond structure using its M-polynomial. Furthermore, entropy measures based on Nirmala indices are derived for the diamond structure. The comparison of the Nirmala indices and corresponding entropy measures is presented through numerical computation and 2D line plots. A regression model is built to investigate the relationship between the Nirmala indices and corresponding entropy measures.