A comparative study of entropy measure using logarithmic and power regression models for rhenium disulfide network

Two-dimensional rhenium disulfide is a new member of the transition metal dichalcogenides family and has some unique structural properties, including weak band renormalization, limited interlayer coupling, and absence of interlayer registry due to Peierls distortion within its framework. These properties make it an attractive material for various applications in nanotechnology and materials science. The objective of the current study has gone into computing a number of connection-based invariants useful for the computations of different various types of Zagreb indices, using rhenium disulfide as mathematical descriptor carrying the main ideas on the material’s structural property. Further, from these calculated indices, it comes out on a method of measuring its entropy that is the real key value on quantification for disorderliness or structure complexity of that matter. We consider here a logarithmic regression model along with the power one so as to show the entropy computed index variation analysis. Results indicated a much better fit of the logarithmic regression model than that of power regression. This finding confirmed that the logarithmic model described the rhenium disulfide connection characteristics properly and provided important insights into its structural behavior and functional uses. The results of this work have important consequences in nanotechnology and in materials science, particularly in relation to optimizing rhenium disulfide’s structure analysis for electronic and optoelectronic application. Details regarding variation in entropy through topological indices can have consequences in terms of theoretical modeling of behavior in a material in semiconductor technology and in technology for storing energy.