In this section, we detail the methodology employed to predict CH4 adsorption based on four key descriptors: VF, LCD, PLD, and SA. We first discuss the Gaussian process regression (GPR) surrogate model used to learn the relationship between these descriptors and the methane uptake and selection of IPs. We then introduce our AL framework, which iteratively explores the descriptor space to identify candidate materials for the GP model. In this study, the AL strategies include GP STD, EI, and PI. Finally, we outline the iterative procedure that combines GP modeling, inducing points, and AL techniques to efficiently guide the search for top-performing materials within the CoRE MOF database.