Redox flow batteries (RFBs) are a
promising technology for stationary
energy storage applications due to their flexible design, scalability,
and low cost. In RFBs, energy is carried in flowable redox-active
materials (redoxmers) which are 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, designing redoxmers with a fluorescence-enabled
self-reporting functionality allows monitoring of the state of health
of RFBs. To accelerate the discovery of redoxmers with 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 redoxmer molecules based on a 2,1,3-benzothiadiazole (BzNSN)
core structure. From the computed properties, we identified 22 Pareto-optimal
molecules that represent best trade-off among all of the desired properties.
We further utilized these 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 data
set can be identified at least 15 times more efficiently compared
to the brute force or random selection approach. Importantly, we utilized
this approach for discovering promising redoxmers from an unseen database
of 1 million BzNSN-based molecules, where we discovered 16 new Pareto-optimal
molecules with significant improvements in properties over the initial
1400 molecules. We anticipate that this active learning technique
is general and can be utilized for the discovery of any class of functional
materials that satisfies multiple desired property criteria.