The RL optimization was performed using the rlmolecule library (github.com/nrel/rlmolecule), which implements the AlphaZero approach for molecule and material design. In this study, the RL agent learned to select from a parametric action space, where the molecular structures resulting from possible next actions were passed through a trainable policy GNN. The policy GNN had a similar structure to that used for redox prediction (Fig. ), using only 3 message passing layers and a feature dimension of 64. The policy model was trained with the ADAM optimizer with a learning rate of 1E-3 and a batch size of 32 positions. Within each batch, the policy model is presented with a molecule state and a list of potential next actions from a recently played MCTS rollout. The policy model is trained to simultaneously predict the actual visitation frequency from MCTS, as well as the outcome of the resulting molecule rollout (0 or 1 as scored via ranked rewards). Starting at the root methane state, molecule rollouts consisted of conducting 250 MCTS samples (or for a maximum of 30 seconds) and selecting the subsequent molecule state with probability proportional to the softmax of the visit counts. This procedure is repeated until a terminal state is selected.