In this work, we have developed a deep reinforcement learning framework to inverse design MOFs. We illustrate this approach to design MOFs with important characteristics for direct air capture (DAC) of CO 2 . We successfully (inverse) designed a set of materials that have a high CO 2 heat of adsorption (>40 kJ/mol). In addition, we generated a set of materials that preferentially adsorb CO 2 from humid air. Subsequent analysis of the chemical design space shows that the top-performing structures populated two, separate subspaces concerning the two target properties. Yet, few of our structures satisfy both requirements.