To identify the optimal adsorption behavior, the target function is to maximize CO2 uptake at 1 bar and 298K and minimize N2 uptake at 1 bar and 298K. The direct application of high-fidelity GCMC simulations is computationally expensive for all offspring MOFs generated by the NSGA process. Instead, these values are predicted using trained machine learning models, referred to as MOF-NET, by Lee et al. . MOF-NET employs word embedding features, similar to Word2Vec 35 , to represent the topology and building blocks of structures. The relationship between topology, building blocks, and properties is characterized through a neural network to predict CO2 and N2 adsorption uptake. We use the initial set of MOFs from PORMAKE and their adsorption uptakes from GCMC simulations for MOF-NET training. More details on the training process are provided in Figure .