The purpose of this project is to develop novel graph neural network methods to enhance solvers for the NP-complete boolean satisfiability problem (SAT). This is done by generating SAT problem instances, converting these to graphs, and training on those instances to generalize to industrially relevant instances such as those found in the SAT competition benchmarks. The end goal is to develop solvers that are faster than the current state of the art using these graph neural networks.