This research direction centers on the intelligent design and precise creation of metal–organic frameworks (MOFs) and covalent organic frameworks (COFs) for critical applications in gas adsorption, separation, and energy catalysis. Through multiscale simulations and high-throughput computational approaches, the research systematically elucidates microscopic mechanisms governing adsorption, diffusion, and structural responses in framework materials. By integrating machine learning and generative artificial intelligence, structure–property relationship models are constructed to enable rational regulation and inverse design of framework materials, thereby accelerating the discovery and optimization of high-performance porous materials.