Multiscale modeling of graphene and carbon nanostructures: advances in atomistic, coarse-grained, and machine learning approaches

Abstract

Graphene, its derivatives such as graphene oxide and reduced graphene oxide, and related carbon nanostructures including carbon nanotubes, possess exceptional mechanical, thermal, and electronic properties. These features make them highly attractive for applications ranging from energy storage and filtration to flexible electronics and biomedical systems. Molecular simulation methods, including atomistic molecular dynamics, coarse grained modeling, and dissipative particle dynamics, have become essential tools for investigating the behavior of these materials at multiple length and time scales. This review presents recent advances in the modeling of graphene-based systems, highlighting how different simulation techniques are employed to explore mechanical deformation, defect evolution, thermal transport, and interfacial behavior. Special attention is given to the development of coarse-grained models, the role of force field selection, and the integration of machine learning to improve computational efficiency and predictive accuracy. Future directions include the use of high-performance computing, artificial intelligence assisted simulations, and scalable frameworks for large scale modeling. Overall, molecular simulations continue to play a critical role in the rational design and optimization of graphene-derived materials for advanced technological applications.