We trained the reinforcement learning framework using the pre-training generator and predictors, with the aim of generating novel MOFs that exhibit high CO 2 heat of adsorption and CO 2 /H 2 O selectivity. The performance of the optimized generator by the reinforcement learning algorithm was evaluated using three metrics: validity, scaffold, and uniqueness. The validity metric evaluates if the generator can generate MOFs which (1) match connection points of metal clusters and organic linkers for a given topology and (2) produce chemically valid organic linkers. To evaluate (2), we converted the generated SELFIES to canonical SMILES using the RDkit sanitizer.