Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties

Abstract: Machine learning interatomic potentials (MLIPs) offer an efficient and accurate framework for large-scale molecular dynamics (MD) simulations, effectively bridging the gap between classical force fields and \textit{ab initio} methods. In this work, we present a reactive MLIP for graphene, trained on an extensive dataset generated via \textit{ab initio} molecular dynamics (AIMD) simulations. The model accurately reproduces key mechanical and vibrational properties, including stress-strain behavior, elastic constants, phonon dispersion, and vibrational density of states. Notably, it captures temperature-dependent fracture mechanisms and the emergence of linear acetylenic carbon chains upon tearing. The phonon analysis also reveals the expected quadratic ZA mode and excellent agreement with experimental and DFT benchmarks. Our MLIP scales linearly with system size, enabling simulations of large graphene sheets with \textit{ab initio}-level precision. This work delivers a robust and transferable MLIP, alongside an accessible training workflow that can be extended to other materials.