We apply this reinforcement learning framework to design MOFs for direct air capture (DAC). DAC has been developed to reduce the CO 2 concentration in the atmosphere. As we have a large risk of overshooting these CO 2 levels, DAC has become a very active area of research . The main challenge of DAC is the low concentration of CO 2 in the air (compared to flue gas), and at such an extremely dilute concentration (∼400 ppm), one needs a material with a very high CO 2 enthalpy. Currently, a class of materials known as chemisorbents is used industrially to tackle this challenge. Chemisorbents can bind CO 2 very strongly, which is reflected in its high heat of adsorption (> 65 kJ/mol) . But such high heat of adsorption also leads to increased difficulty and costs to regenerate the materials . Physisorbents, on the other hand, usually have much lower CO 2 heat of adsorption (< 40 kJ/mol ), though there have been reports of few structures having CO 2 heat of adsorption higher than 50 kJ/mol . So a question that arises here is, could physiorbents be used for DAC? To this end, Findley et al. computationally screened the CoRE MOF database and several classes of zeolites to search for the best physisorbent MOF for DAC. Based on their analysis, they concluded that those structures are not viable for DAC. Hence, whether one can design a library of MOFs specifically for DAC is interesting.