Redox flow batteries (RFBs) are a promising technology for stationary energy storage applications due to their flexible design and scalability at low cost. In RFBs, energy is carried in flowable redox-active materials, or redoxmers, that can be stored externally and pumped to the cell during operation. Further improvements in the energy density of RFBs necessitates redoxmer designs with wider redox potential windows and higher solubility. Additionally, functionalizing redoxmers with a fluorescence-enabled self-reporting capability allows monitoring of the state of health of RFBs. To accelerate the discovery of redoxmers with multiple desired properties, state-of-the-art machine learning (ML) methods, such as multiobjective Bayesian optimization (MBO), are useful. Here, we first employed density functional theory calculations to generate a database of reduction potentials, solvation free energies, and absorption wavelengths for 1400 redoxmers based on a 2,1,3-benzothiadiazole (BzNSN) molecular scaffold. Then, we utilized the data to develop and benchmark an MBO approach to identify candidates quickly and efficiently with multiple targeted properties. With MBO, optimal candidates from the 1400-molecule dataset can be identified at least 15 times more efficiently compared to brute force or random selection. Importantly, we successfully employed this approach to discover several new promising redoxmers after evaluating only 100 molecules from an unseen database of 1 million. We anticipate that our method is general and can be utilized for the discovery of functional materials with multiple design criteria.

Multiobjective bayesian optimization for accelerated materials discovery: Applications in redox flow batteries

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