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

Abstract

Owing to advancements in computing power and improvements in automation of modelling, high-throughput virtual screening (HTVS) has increasingly been used for materials data generation. Here, we applied a HTVS-guided experimental study 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 small colorant 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 most promising compounds. Screening efforts in a chemical space of 3,257 redox pairs led to 205 predicted candidates with higher solubility and lower redox potential than that of the state-of-the-art anthraquinone-2,7- disulfonic acid (AQDS) anolyte used in ARFBs. Through the electrochemical studies on the commercially available compounds, we identified the molecules that show good performance in an ARFB setup. 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 methodology presented here is flexible and applicable for the future exploration of small compounds for electrochemical energy storage.

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