Mechanical properties of graphene oxide from machine-learning-driven simulations

Abstract

Graphene oxide (GO) materials have complex chemical structures that are linked to their macroscopic properties. Here we show that first-principles simulations with a machine-learned interatomic potential can predict the mechanical properties of GO sheets in agreement with experiment and provide atomistic insights into the mechanisms of strain and fracture. Our work marks a step towards understanding and controlling mechanical properties of carbon-based materials with the help of atomistic machine learning.