Entropy is a thermodynamic function in chemistry that, based on the number of possible configurations for a given system or process, measures the randomness and disorder of molecules in that system or process. Many problems in arithmetic, biology, chemical graph theory, organic and inorganic chemistry, and other disciplines are resolved using distance-based entropy. A topological descriptor also connects certain physical aspects of the underlying chemical substances, in contrast to a topological index, which in chemical graph theory is a numerical representation of a chemical network. When building models for any chemical network, the graph is crucial. Simonraj et al. produced a brand-new category of graphs known as the third kind of hex-derived networks. In our work, we discuss the third class of hex-derived networks, which includes hex-derived networks HDN3, triangular hex-derived networks THDN3, rectangular hex-derived networks RHDN3, and chain hex-derived networks (CHDN3). We compute exact results for newly defined entropies by utilizing their topological indices based on end vertex degrees.
- Hex-derived network (HDNa)
- chain hex-derived network CHDNa)
- rectangular hex-derived network (RHDNa)
- triangular hex-derived network (THDNa)
ASJC Scopus subject areas
- Computer Science(all)
- Materials Science(all)