Abstract
Graphene aerogels, a special class of 3D graphene assemblies, are well known for their exceptional combination of high strength, lightweightness, and high porosity. However, due to microstructural randomness, the mechanical properties of graphene aerogels are also highly stochastic, an issue that has been observed but insufficiently addressed. In this work, we develop Gaussian process metamodels to not only predict important mechanical properties of graphene aerogels but also quantify their uncertainties. Using the molecular dynamics simulation technique, graphene aerogels are assembled from randomly distributed graphene flakes and spherical inclusions, and are subsequently subject to a quasi-static uniaxial tensile load to deduce 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 the simulated graphene aerogel results as training data, we build Gaussian process metamodels that can efficiently predict the properties of unseen graphene aerogels. In addition, statistically valid confidence intervals centered around the predictions are established. This metamodel approach is particularly beneficial 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.
Synthesis of Aerogel with Graphene and Significance with Aerospace Industry
