The overview of the computational discovery workflow is presented in Figure . We employ POREMAKE MOF constructor by Lee and co-workers to generate MOF structures using a library of building blocks and topologies. Next, the Non-dominated Sorting Genetic Algorithm (NSGA) is used to generate new MOFs with low N2 uptake and high CO2 uptake. Evaluating the adsorption properties of MOF offspring generations during the NSGA process using high-fidelity GCMC simulations is computationally expensive. To accelerate this process, we employ an efficient, approximate ML model called MOF-NET, developed by Lee and co-workers based on word embeddings and previously employed to sift through vast numbers of MOFs for the purpose of optimizing Xe/Kr separation and hydrogen storage. In this work, we use MOF-NET, trained for CO2 and N2 adsorption, in tandem with GCMC simulations to propagate MOF materials via NSGA.