Generalized modeling of carbon film deposition growth via hybrid MD/MC simulations with machine-learning potentials

Theoretical investigations into the controlled growth of carbon films are essential for guiding the experimental fabrication of carbon-based devices. However, accurately simulating the deposition process remains a significant challenge. In this work, we developed an active learning workflow to construct a machine learning-based neuroevolution potential (NEP) for investigating carbon atoms deposition growth on various substrates. By integrating molecular dynamics and time-stamped force-biased Monte Carlo simulations, we studied the growth of amorphous carbon films on Si(111) and found that deposition energy strongly influenced bonding topology and film morphology. The NEP reliably captured the surface diffusion of carbon atoms, the formation of carbon chains and rings. We revealed a new growth mechanism of adhesion-driven growth at low energies and peening-induced densification at high energies of carbon atoms on Si(111) substrates. To evaluate the transferability of fitting workflow, we extended the NEP to simulate carbon deposition on Cu(111) and Al2O3(0001) surface. Simulation results demonstrate that the NEP can reproduce the subprocesses of graphene formation during carbon growth on the Cu(111) substrate. In contrast, only disordered carbon chains are observed on the Al2O3(0001) substrate. This work provides atomistic insights into the growth mechanisms of carbon films on representative substrates and establishes a robust computational framework for synthesis of diverse carbon nanostructures.