Abstract
The efficient separation of carbon dioxide (CO2) and methane (CH4) is crucial for chemical processes, including biogas upgrading and natural gas purification. Metal-organic frameworks (MOFs) have gained significant attention as promising adsorbents for these processes due to their high porosity and tunable structures. Estimating the adsorption capacity of MOFs is essential for screening high performing adsorbents. While molecular simulations are commonly used to estimate the adsorption capacities, their computational intensity acts as a bottleneck in screening MOF adsorbents. In this study, we propose a machine learning (ML)-based framework for the high-throughput prediction of adsorption isotherms for CO2 and CH4 in MOFs. A graph neural network (GNN) model was developed to predict adsorption capacities, effectively replacing the time-consuming molecular simulations. The GNN model processes the structural graphs of MOFs, capturing their spatial configurations, such as surface structure and pore characteristics, which are closely related to adsorption performance. Based on the adsorption capacities predicted by the GNN model, an isotherm model was derived to characterize the adsorption behavior of the MOFs. This framework enables the rapid and reliable screening of MOFs with superior adsorption performance for CO2/CH4 separation, offering a computationally efficient approach that paves the way for broader applications of MOFs in carbon dioxide separation technologies.