Entropy measures based on Nirmala coindices for silicon carbide molecular graphs

Area of Science:

• Materials Science
• Chemical Informatics
• Network Theory

Background:

• Topological indices are crucial for predicting material properties.
• Silicon carbide (SiC) networks possess complex structures.
• Quantifying the topological complexity of SiC is essential for understanding its behavior.

Purpose of the Study:

• To calculate Nirmala coindex, first inverse Nirmala coindex, and second inverse Nirmala coindex for SiC molecular networks.
• To propose novel entropy measures derived from these coindices.
• To investigate the relationship between network parameters and entropy.

Main Methods:

• Utilized the CoM-polynomial to compute Nirmala coindices for SiC molecular networks [Formula: see text]-I[p; q].
• Developed new entropy measures based on the calculated coindices.
• Analyzed correlations between network parameters (p, q) and entropy growth.

Main Results:

• Successfully calculated Nirmala coindices and derived entropy measures for SiC networks.
• Observed consistent correlations between network parameters (p, q) and entropy.
• Entropy growth indicates increased structural irregularity and information content with molecular size.

Conclusions:

• Nirmala coindex-based entropies effectively quantify the topological complexity of SiC networks.
• These entropy measures show potential as descriptors for physicochemical properties.
• Findings suggest applications in predicting hardness, electrical conductivity, and catalytic activity of silicon-carbon materials.