▎ 摘　　要

Graphene oxides have exhibited alluring potential for state-of-the-art applications such as biomedical devices and functional nanocomposites. The types and concentrations of oxygen-containing functional groups are fingerprints of graphene oxides, dictating the properties and usage of the nanomaterial. Compared to pure graphene, the properties of graphene oxides are more challenging to model from a theoretical perspective, mainly because of the profound but implicit influences of the functional groups within. Machine learning is a potent method to uncover the hidden structure-property relations and to accelerate material discovery. Here, we develop a machine learning-based strategy to determine the functionalization properties of monolayer graphene oxides, labeled by the oxygen-to-carbon ratio and relative concentrations of functional groups. Trained by mechanical responses upon uniaxial tension computed by reactive molecular dynamics simulations, our proposed gradient boosting machine learning model can accurately identify the chemical composition of graphene oxides in the reserved data set. The difference in prediction accuracies between oxygen coverage and functional group composition is rationalized by graphene oxide molecular mechanisms. The proposed data-driven strategy can contribute to the predictive modeling of functionalized two-dimensional materials of a broad variety.