As we have a relatively small number of structures with the desired properties, we carried out the reinforcement learning for three rounds. After each round, we of the reinforcement learning framework are provided in the Methods section. Supplementary Fig. shows the parity plots of the predictors for each round. In Fig. , the property distributions of MOFs generated by the pre-training generator (i.e., scratch) and generators optimized by reinforcement learning for three rounds are illustrated, where the estimated property values are based on the predictors. Notably, it is observed that the average target values increase as the rounds progress, indicating the success of the reinforcement learning approach in optimizing the generator to produce MOFs with improved properties. Supplementary Fig. reveals that the property distribution improves significantly as training progresses within a particular round. Table shows that the overall scaffold metric decreased after optimization compared to scratch because the optimized generator generated organic linkers without the scaffolds used as input to maximize rewards. After optimization, the metric for uniqueness also exhibits a decrease, suggesting that the generator favors generating organic linkers with desirable properties. Based on the predictors' estimations, the most frequently observed topologies, metal clusters, and organic linkers for rounds 1, 2, and 3 are presented in Supplementary Fig.