Prediction of cluster energy of lithium clusters from codescriptors with application in 2D porous graphene

Abstract

A lithium metal anode has a high energy density, but its applications face numerous challenges, particularly the irregular deposition of lithium dendrites, which form due to the aggregation of lithium clusters, posing serious safety risks. Research has revealed that small lithium clusters have the potential to enhance the electrode potential in Lithium-ion batteries (LIBs), and cluster energy affects the stability of lithium clusters. Modern computational tools and DFT calculations of lithium clusters can provide invaluable insights into their chemical, physical, and structural properties. However, calculating structural properties through large-scale experiments can be time-consuming and expensive. To overcome the challenges inherent in studying lithium clusters, this study employs a novel approach. We construct molecular graphs for the most stable structures and leverage CoM Polynomial computations to generate codescriptors, enabling efficient and accurate predictions of cluster energy for diverse lithium cluster configurations. The curvilinear regression analysis filters out highly significant regression equations and highly predictive codescriptors. Additionally, we investigated two molecular structures of porous graphene, namely linear and triangular, and obtained topological codescriptors’ analytical expressions by utilizing the CoM Polynomial in each case. A deeper understanding of both lithium clusters and porous graphene properties is essential for optimizing LIBs’ performance and stability.