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

Key Points

• The aim is to explore how various modeling techniques can effectively predict properties of graphene and carbon nanostructures.
• Utilized atomistic simulations to analyze material behaviors at the molecular level.
• Employed coarse-grained models to simplify complex systems for easier computations.
• Integrated machine learning approaches to enhance predictive accuracy across scales.
• Predicted mechanical properties showed strong alignment with experimental data.
• Transport behavior indicated significant scalability across different modeling levels.
• Electrokinetic characteristics were effectively modeled, revealing insights into material assembly.