Solubility Characterization of Organic Molecules for Aqueous Organic Redox Flow Batteries

The major obstacle to renewable energy sources is a lack of long-term energy storage capabilities. Energy produced during the day dissipates, leaving insufficient electricity for at night. The goal of the project is to design an Aqueous Organic Redox Flow Battery (AORFB) to act as long-term energy storage. Work has been done using machine learning to identify suitable compounds for the batteries. In this work there was no indication as to the aqueous solubility of the molecules; this controls the device’s energy storage capabilities. We used a machine learning model to determine the aqueous solubility of slightly more than 3000 compounds. An initial training dataset was made using NMR measurements to determine the aqueous solubility of ten* selected compounds. The trained dataset was used as validation data for the machine learning model described earlier and molecular dynamics simulations.