Machine learning assisted insights into the mechanical strength of nanocrystalline graphene oxide

The mechanical properties of graphene oxides (GOs) are of great importance for their practical applications. Herein, extensive first-principles-based ReaxFF molecular dynamics (MD) simulations predict the wrinkling morphology and mechanical properties of nanocrystalline GOs (NCGOs), with intricate effects of grain size, oxidation, hydroxylation, epoxidation, grain boundary (GB) hydroxylation, GB epoxidation, GB oxidation being considered. NCGOs show brittle failures initiating at GBs, obeying the weakest link principle. By training the MD data, four machine learning models are developed with capability in estimating the tensile strength of NCGOs, with sorting as eXtreme Gradient Boosting (XGboost) > multilayer perceptron > gradient boosting decision tree > random forest. In the XGboot model, it is revealed that the strength of NCGOs is greatly dictated by oxidation and grain size, and the hydroxyl group plays more critical role in the strength of NCGOs than the epoxy group. These results uncover the pivotal roles of structural signatures in the mechanical strength of NCGOs, and provide critical guidance for mechanical designs of chemically-functionalized nanostructures.

Automated summary

This study investigates the wrinkling morphology and mechanical properties of nanocrystalline graphene oxides (NCGOs) using molecular dynamics simulations and develops machine learning models to estimate their tensile strength. The results reveal the crucial role of structural signatures in the mechanical strength of NCGOs.