Topological analysis of entropy measure using regression model for terpyridine complex nanosheet

TL;DR

This paper investigates the relationship between topological indices and entropy measures in terpyridine complex nanosheets, employing regression models to analyze their structural and physicochemical properties.

Contribution

The study introduces a novel analysis of Nirmala indices and entropy measures for terpyridine nanosheets using regression models, expanding the understanding of their structural relationships.

Findings

Correlation between Nirmala indices and entropy measures established

Regression models effectively predict entropy from topological indices

Visual and numerical comparisons enhance structural insights

Abstract

A numerical parameter, known as a topological index, is employed to represent the molecular structure of a compound by considering its graph-theoretical properties. In the study of quantitative structure-activity relationships (QSAR) and quantitative structure-property relationships (QSPR), topological indices are used to predict the physicochemical properties of chemical compounds. Graph entropies have evolved as information-theoretic tools to investigate the structural information of a molecular graph. In this research work, we compute the Nirmala index, the first and second inverse Nirmala index for terpyridine complex nanosheet $TCN_{n,m}$ with the help of its M-polynomial. Further, entropy measures based on Nirmala indices are calculated for terpyridine complex nanosheets. We expand this analysis to include visual comparisons, which could be useful in refining the structure for more effective implementation. Furthermore, using graphical and numerical methods, the correlation and comparison between the Nirmala indices and the corresponding entropy measurements are shown. The relationship between the Nirmala indices and associated entropy measurements is then examined using a regression model.