A first-principles and machine learning combined method to investigate the interfacial friction between corrugated graphene

Abstract

Abstract Simulating the frictional properties of complex interfaces is computational resource consuming. In this paper, we propose a density functional theory (DFT) calculation combined machine learning (ML) strategy to investigate the sliding potential energy corrugation between geometrical corrugated graphene (Gr) sheets. By the aid of few DFT calculations and geometrical descriptors Σr −n (n = 1, 2, 6, 12), the trained ML models can accurately predict the sliding potential evolutions of Gr/Pt and Gr/Re systems. To be specific, based on DFT calculations of sliding along [110] direction, the trained linear regression (LIN) models can properly give out the potential energy evolution along the [100] direction with deviation less than 5%. By the dataset of given distances (9.3 Å, 9.65 Å and 10 Å) between two Re monolayers in Gr/Re systems, LIN and Bayesian ridge regression (BR) models can quantitatively predict the potential energy evolution of unknown distances (9.2 Å, 9.4 Å, 9.5 Å and 9.6 Å). The predicted magnitudes of potential energy corrugations by BR model divert less than 3 meV Å−2 from DFT calculations. The prediction results for extrapolated distances (9.0 Å and 9.1 Å) deviate notably, but the extension of training dataset effectively improves the predictive ability of ML models, especially for the LIN model. Thus, the supposed strategy could become an effective method to investigate the frictional characteristics of complex interfaces.