Redox flow batteries can easily be scaled up to store large amounts of energy, making them a promising technology for electrical grid storage. The batteries work by storing energy in large tanks of electrolyte solutions, but they are currently limited by the performance of available electrolyte materials. With tens of millions of potential candidate molecules, scientists need an improved method to speed the discovery of optimal materials for redox flow batteries. The goal of this project is to build an autonomous AI application for supercomputers that can select and perform the simulation and machine learning tasks needed to identify better-performing molecules. Achieving this goal will require scaling individual tasks, such as computing material properties and training AI models, and then combining them into a cohesive application that will remove humans from the materials design process.