Data-driven discovery of small electroactive molecules for energy storage in aqueous redox flow batteries

A high-throughput virtual screening (HTVS)-guided experimental study is applied for the large-scale exploration of quinone-like anolytes for aqueous redox flow batteries (ARFBs). This includes the design of a focused virtual chemical library inspired by stable molecules, quantum chemical prediction of redox properties, machine learning prediction of aqueous solubility, automated search for commercial availability on vendor databases, and electrochemical characterization of the promising compounds. Screening in a chemical space of 3,257 redox pairs led to 205 predicted candidates with higher solubility and lower redox potential than that of anthraquinone-2,7-disulfonic acid (AQDS) anolyte used in ARFBs. Through electrochemical studies on commercially available compounds, the molecules that show good performance in a practical ARFB setup are identified. Among them, indigo trisulfonate [Indigo-3(SO3H)] showed higher solubility, capacity retention, and coulombic efficiency than AQDS and its predecessors. The data-driven material design approach is therefore highly effective for the future explorations of electrochemical energy storage compounds.