In recent years, the use of machine learning (ML) has been on the rise in the field of materials, from material discovery to predictive tasks . This surge in ML applications has significantly contributed to the accelerated pace of materials research and design, offering innovative solutions to challenges that were once considered insurmountable. Among the materials garnering attention, metal-organic frameworks (MOFs) have emerged as a particularly promising class of materials due to their tunable porous structures and versatile chemical composition. MOFs, characterized by their crystalline structures comprising metal nodes interconnected by organic ligands, can exhibit exceptional surface areas and tailorable functionalities. These attributes make MOFs highly desirable for applications in gas adsorption, separation, and storage. However, the extensive database of MOFs calls for the adoption of more advanced computational methods to efficiently screen these materials for their potential applications.