Atomistic Modeling and Uncertainty Quantification for Mechanical Properties of Graphene Aerogels

As 3D porous graphene assemblies, graphene aerogels inherent many properties of 2D graphene sheets and are well known for their exceptional combination of high strength and lightweight. However, the mechanical properties of graphene aerogels are also highly stochastic due to the randomness of the microstructure, which poses substantial difficulty for its practical use. Herein, we develop Gaussian process metamodels to not only predict important mechanical properties of GAs but also quantify their uncertainties. Using molecular dynamics simulations, graphene aerogels are firstly assembled from randomly distributed graphene flakes and spherical inclusions, and are subsequently subject to a quasi-static uniaxial tensile load to obtain their mechanical properties. Results show that given the same density, mechanical properties such as the Young’s modulus and the ultimate tensile strength can vary substantially. Treating density, Young’s modulus, and ultimate tensile strength as functions of the inclusion size, and using available simulation results as training data, we build Gaussian process metamodels that can efficiently predict properties of unsimulated graphene aerogels. Additionally, by establishing statistically principled confidence intervals, the uncertainty of graphene aerogel properties is quantified. This metamodel approach is particularly advantageous when the data acquisition requires expensive experiments or computation, which is the case for graphene aerogel simulations. The present research quantifies the uncertain mechanical properties of graphene aerogels, which may shed light on the statistical analysis of novel nanomaterials of a broad variety.