Uncertainty analysis and estimation of robust AIREBO parameters for graphene

AIREBO is one of the commonly utilized interatomic potential (IP) for performing molecular dynamics (MD) simulations of graphene and carbon nanostructures. With its parameters fitted to a limited dataset, property prediction outside of the original training set can be challenging and can lead to uncertainty in the predicted values. This is especially important for 2D materials such as graphene which have limited experimental data and have widely varying predicted properties in the literature. In this study, we conducted a comprehensive Uncertainty Quantification (UQ) analysis of AIREBO potential parameters and their corresponding effect on the predicted properties of graphene. We found that computed output properties were highly sensitive to small variations in IP parameters. For instance, a 0.5% change in IP parameters led to a 66% change in the predicted elastic constants. Based on our UQ analysis, we developed a new robust IP parameter set for the AIREBO potential with significantly reduced sensitivity towards output properties. The robust parameters were derived using a Markov Chain Monte Carlo scheme, considering gaussian noise in available DFT data. We were also able to obtain realistic error bars on MD predictions by using posterior probability distributions and propagating the underlying variance to the final properties. (C) 2018 Elsevier Ltd. All rights reserved.