UC Berkeley

Often deemed as a "wonder material", graphene has exhibited remarkable promises in a broad range of research fields thanks to its exceptional electronic, thermal, and mechanical properties. However, issues such as the inevitable existence of defects and the complex microstructures of graphene-based materials stand as a bottleneck in realizing its full potential in real-life applications. With the fast growth of big data, machine learning has been widely applied in many fields such as finance, biology, and healthcare. The advent of machine learning approaches also offers solutions to learning patterns from complex data in material design and discovery, reducing the need for expensive, time-consuming, and tedious laboratory experiments or numerical simulations. In the present thesis, machine learning-assisted simulation and design approaches for functional nanomaterials are demonstrated, with a focus on the graphene family. Molecular dynamics simulations are conducted to numerically investigate the mechanical behavior of graphene-based materials such as graphene, graphene oxide and graphene aerogel, and various machine learning techniques including kernel ridge regression, Gaussian process metamodels, and deep reinforcement learning are used in the predictive and generative modeling of these materials. Finally, the concept and the promise of machine learning interatomic potentials in achieving efficient and accurate simulations for metal-organic framework materials are presented. The research constituting the present thesis may shed light on some new possibilities of simulating and designing functional nanomaterials, which may further improve the performances of applications such as stretchable electronics, supercapacitor devices, carbon sequestration technologies, among others.