The main objectives of this work are thus: to illustrate the use of a reinforcement learning framework to inverse-design MOFs with desired properties; (2) to highlight the use of these inverse-designed MOFs for an extremely important application like DAC. We first show the working of our reinforcement learning workflow by inverse designing MOFs with CO 2 heat of adsorption higher than 40 kJ/mol. To make this design even more challenging, we must consider the effect of H 2 O. Depending on the climate, there are different levels of water vapor in the atmosphere, and most materials that strongly bind CO 2 have a higher affinity for H 2 O. Hence, we must design MOFs that perform well in humid conditions. And for this reason, our second design criterion is that the material prefers CO 2 above H 2 O (i.e., CO 2 /H 2 O selectivity to be higher than 1). The successful inverse design of MOFs for a challenging application like DAC could significantly advance the rational design and discovery of materials for a wide range of applications.