To ensure that the structures generated from our reinforcement learning framework are reasonable, we employed different structure feasibility tests to narrow down the ideal MOF candidates for DAC (the workflows for the same are summarized in Supplementary Fig. ). First, from the 10,000 test set, the valid MOFs, satisfying the validity metric, generated by the optimized generators with CO 2 heat of adsorption higher than 30 kJ/mol and CO 2 /H 2 O selectivity higher than 1 were selected. Then, to estimate the synthesizability of the organic linkers, we computed the synthetic accessibility (SA) score . The SA score is based on molecular complexity; molecules with a low SA score are less complex and are to be expected to have an easier synthesis route compared to those more complex molecules with a high SA score. The MOFs with an SA score of organic linkers higher than 6 were dismissed. In addition, the number of generated structures was restricted through the topological root mean squared deviation (RMSD) of the atomic positions between the building block node vectors and the target topology node vectors . Lower topological RMSD values indicate that the strain between the two vectors is low, and the resultant MOF structure is more stable. Given that the MOFs created by the PORMAKE are typically feasible when the topological RMSD is lower than 0.3 , the same constraint was adopted. Apart from these, the generated MOFs with more than 3000 atoms and higher than 60 Å cell lengths were also dismissed. high electronegativity, which leads to strong binding with CO 2 . The functionalization of the MOF with fluorine is also a characteristic of one of the few physisorbent MOFs synthesized for DAC by Bhatt et al .