In this study, we limited constructed molecules to a maximum of 12 heavy atoms (approximately the size of TEMPO), as lower molecular weight redox-active moieties are preferred for a higher charge-to-mass ratio. Including selecting of the location of the radical electron, this yields a search space of approximately 10 9 possible valid radicals, estimated via extrapolating from smaller maximum sizes and matching previous results. The computational cost of enumerating this grows exponentially with the maximum molecule size, motivating a more efficient strategy for finding top-performing molecules (Fig. ). A framework for MCTS optimization over the defined DAGs was implemented that allows for transpositions, where the same molecule can be reached through multiple paths. Following the approach of AlphaZero 43 , this framework was augmented with a policy model that replaces the simulation phase (using a random policy) of MCTS with a value score predicted from a GNN, which also initializes the prior scores for successor states from the given molecule. This policy model is trained in a concurrent process by maintaining a buffer of recent MCTS rollouts, sampling in-progress molecules, and minimizing a multi-objective loss function. The loss function contains both the difference between the predicted value score and the final rollout reward and the difference between predicted prior probabilities and the actual search probabilities for each of the molecule's successor nodes (see Methods). As MCTS and the AlphaZero framework were originally designed for competitive games, the ranked reward strategy was used to enable tabula rasa self-play for the single-player combinatorial optimization problem. In this strategy, the final reward of a rollout is rescaled to {0, 1} depending on whether the reward is greater than the 75 th percentile of the last 250 results. An overview of the connectivity between the rollouts, the data buffer, and the policy model is shown in Fig. . In this fashion, the policy-guided rollouts evolve from an initial random walk over molecular space to a highly targeted exploration of regions likely to contain high-reward molecules. To search for potential candidate radicals, 200 rollout workers were split across 50 compute nodes for four hours, with a single node equipped with dual Tesla V100 GPUs handling the continual training of the policy model. This approach resulted in a total of 34,626 rollouts and over 3.8 million terminal state radicals evaluated with the surrogate objective function. By maintaining a cache of all surrogate reward calculations performed during the search, we can easily query the data buffer for the top radical candidates found during the optimization. Of the 3.8 million radicals evaluated, 1,078 had a total surrogate reward greater than 195, corresponding to a minimum stability score of 95. The radical stability metric rewards molecules with highly delocalized electrons and bulky groups offering steric protection of the radical center. As such, the stability metric tends to have a higher maximum value for larger molecules. Known stable radicals in this size range include TEMPO (with a stability score of 93.9) and the phenoxy radical (77.2). The reward function includes a maximum of 100 points for meeting all redox and bond strength criteria in addition to the radical stability score. From the radical training database, no radicals were found that had a stability score greater than 90 while satisfying the redox criteria.