Topological Characterization and Graph Entropies of Tessellations of Kekulene Structures: Existence of Isentropic Structures and Applications to Thermochemistry, Nuclear Magnetic Resonance, and Electron Spin Resonance.
S Ruth Julie KavithaJessie AbrahamMicheal ArockiarajJoseph JencyKrishnan BalasubramanianPublished in: The journal of physical chemistry. A (2021)
Tessellations of kekulenes and cycloarenes are of considerable interest as nanomolecular belts in trapping and transportation of heavy metal ions and chloride ions, as they possess optimal electronic features and pore sizes. A class of cycloarenes called kekulenes have been the focus of several experimental and theoretical studies from the stand point of aromaticity, superaromaticity, chirality, and novel electrical and magnetic properties. In the present study, we investigate the entropies and topological characterization of different tessellations of kekulenes through topological computations of superaromatic structures with pores. We introduce the self-powered vertex degree-based topological indices and then derive the graph entropy measures for three different tessellations (zigzag, armchair, and rectangular) via various molecular descriptors that we derive here. Several applications to computing the molecular properties are pointed out. We demonstrate the existence of isentropic and yet nonisomorphic tessellations of kekulenes for the first time. The two tessellations are predicted to be quite close in energy with comparable energy gaps. Graph theory-based PPP methods with parameters derived from higher levels of theory are proposed to be promising tools for the predictions of relative stabilities of kekulene tessellations. We show that the developed techniques can be applied in the general context of artificial intelligence for the machine generation of nuclear magnetic resonance and electron spin resonance spectroscopic patterns as well as in robust computations of thermochemistry of a large combinatorial libraries of tessellations of kekulenes through the generation of bond-equivalence classes.
Keyphrases
- magnetic resonance
- artificial intelligence
- deep learning
- convolutional neural network
- heavy metals
- single molecule
- high resolution
- machine learning
- big data
- energy transfer
- quantum dots
- room temperature
- neural network
- density functional theory
- contrast enhanced
- magnetic resonance imaging
- molecular docking
- transition metal
- health risk
- computed tomography
- mass spectrometry
- aqueous solution
- solar cells
- health risk assessment
- ionic liquid
- simultaneous determination