Hydrocarbon Species on the Cu(111) Surface Studied with a Neural Network Potential

Hydrocarbon species are involved in various surface processes, such as graphene growth on Cu surfaces. A systematic examination of their structures, stability, and reactivity from first principles is essential for understanding the atomic mechanisms of these processes. However, this approach has a substantial computational cost. In this study, we train an accurate Cu–C–H neural network (NN) potential using a homemade deep potential learning platform, DPTorch, which exhibits good linear scalability over thousands of CPU cores and multiple GPUs. The obtained NN potential can accurately reproduce the reaction energy and activation barrier for elementary reactions with hydrocarbon species involved. With such an accurate NN potential, the stochastic surface walking global optimization algorithm is then used to explore stable hydrocarbon structures on the Cu(111) surface. It turns out that hydrogen plays an important role in stabilizing small carbon ring structures. Free energy surfaces are constructed via enhanced sampling using NN potential-based molecular dynamics simulations, which gives a revision to statistically not well-converged free energy barriers predicted previously using ab initio molecular dynamics. At the same time, new pathways are found for both CH and C2 dissociation reactions. These results provide valuable insights into the chemistry of hydrocarbons on the Cu surface.