Topological Codescriptors for Predicting Lithium Clusters Binding Energy and Comparative Topological Study of Hexagonal and Rectangular Porous Nano Graphene

The stability of lithium-ion batteries is heavily impacted by the uncontrolled ‎formation of lithium dendrites, which pose a significant safety risk. The chemical and ‎structural properties of small lithium clusters are critical to controlling this phenomenon ‎and improving electrode potential. The use of traditional techniques, including large-scale ‎experiments and DFT calculations, provides useful insights but is often hindered by high ‎costs and long timeframes. This study addresses this challenge by proposing a novel ‎approach using the CoM Polynomial, a new tool in chemical graph theory. We construct ‎molecular graphs for minimized lithium cluster structures from previous studies and utilize ‎the CoM Polynomial to generate codescriptors for each cluster. This approach extracts ‎significant structural information, enabling a more robust analysis of cluster properties. ‎Curvilinear regression analysis then identifies highly significant regression equations and ‎informative codescriptors, revealing strong relationships between these codescriptors and ‎the binding energy of the clusters. Furthermore, lithium metal holds tremendous promise as ‎an anode material for lithium-ion batteries but faces challenges like dendritic growth and ‎unstable solid electrolyte interphase formation. Researchers are exploring porous graphene ‎frameworks as a potential solution. In this vein, we investigated the molecular structure of ‎hexagonal and rectangular porous nano graphene using the CoM Polynomial to obtain ‎analytical expressions for their topological codescriptors. This study offers insights that ‎can help researchers improve the safety, efficiency, and sustainability of energy storage ‎devices by better understanding the properties of lithium clusters and porous nano ‎graphene.