The surrogate objective function successfully guided molecule optimization towards regions meeting the desired criteria at the DFT level. However, approximately half of the radicals predicted to meet the desired criteria ultimately fell short upon DFT confirmation. Understanding the primary modes by which the surrogate objective fails will help better understand the limitations of machine-learning guided molecular design and further improve predictive methods for subsequent rounds of optimization. In Fig. , we show optimized radicals with a substantially lower DFT-calculated stability than that predicted with the surrogate model. A significant reason for such failure is the incorrect prediction of the maximum spin location, with a higher fraction of spin residing on an atom that is not highly shielded by bulky substituents. This failure partly represents a weakness of the chosen stability metric, as minor differences in predicted resonance form can often lead to large swings in the combined score. However, erroneously predicted loci of maximum spin were chemically reasonable, generally corresponding to the location of the second highest DFT spin density. Extended conjugated thiocarbonyl-based radicals are encountered frequently in these outliers. With DFT relaxation, the maximum spin typically locates on the terminal S atom, while the surrogate objective model predicts greater spin at aor gpositions, in accordance with the general principle of vinylogy. Retraining the surrogate objective with additional examples of these systems may improve predictions in subsequent generation rounds. Errors in redox predictions also tended to occur for functional groups absent from the training data. In Fig. we show the structure of one such outlier. Using the embeddings assigned by the surrogate model's penultimate prediction layer, we can explore which training set molecules are closest in structure to the target prediction. A nearest-neighbors search on this latent space reveals several cyclopentadienyl radicals with calculated redox potentials close to the erroneous prediction. The thioether substituent on a cyclopentadienyl, which is not found in any of the molecules in the redox potentials training database, strongly influences redox behavior in a way not captured by the surrogate model. The sulfur atom provides additional stabilization of the oxidized form through resonance and of the reduced form through inductive effects (i.e., stabilization of the a-anion resonance structure). These types of prediction outliers could be remedied by augmenting the redox potential database with additional structural diversity. Learned strategies for precisely tuning redox potential Searching for a symmetric electrolyte candidate places a challenging constraint on the electronic properties of optimized molecules, as both the oxidation and reduction potentials must be precisely and independently tuned. To explore the strategies used by the RL algorithm, we plot the relationship between oxidation potential (derived from the ionization energy) and the reduction potential (derived from the electron affinity) in Fig. . Electron-rich, more readily oxidized (e.g., planar aminal) radicals are found in the lower-left corner, while electron-deficient, more readily reduced (e.g., heterocyclic sp 2 ) radicals are found in the top right corner of this plot (Fig. ). For open-shell molecules studied with spin-unrestricted Kohn-Sham DFT, the analog to Koopmans' theorem relates the energy of the highest singly-occupied molecular orbital (SOMO) with the vertical ionization energy. Further, the lowest unoccupied molecular orbital (LUMO) is linked closely to vertical electron affinity, mainly when using longrange corrected density functionals. From our computations we observe a correlation between a radical's SOMO energy and both redox potentials (Fig. ). This interdependence of the SOMO energy and both redox potentials illustrates the challenge of independently tuning the anode and cathode half-reactions. Qualitatively, electron-poor (low SOMO energy, electrophilic) radicals tend to be easily reduced and difficult to oxidize, while electron-rich (high SOMO energy, nucleophilic) radicals are easily oxidized and hard to reduce. However, the RL algorithm still manages to find candidates that meet both redox criteria. Radicals with the required redox properties for aqueous batteries have a SOMO energy in the range of -6.5 to -7 eV (gray region in Fig. ). To independently optimize oxidation and reduction potentials at a fixed SOMO energy, AlphaZero learns to harness captodative stabilization of the radical center. Captodative stabilization involves the incorporation of conjugated electrondonating and electron-withdrawing groups, and provides enhanced stability to all three important redox states: the radical, oxidized, and reduced states. Interestingly, this strategy mirrors the use of bipolar redox-active molecules (BRMs), an emerging strategy in the development of nonaqueous RFBs such as 2-phenyl-4,4,5,5-tetramethylimidazoline-1-oxyl-3-oxide (PTIO). The algorithm thus rediscovers a fundamental concept in radical chemistry that has shown promise in the development of symmetric RFBs. However, unlike existing BRMs with relatively bulky functional groups, those discovered by RL more efficiently blend all required functionality into a much lower molecular weight moiety.