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In order to help prevent the climate crisis, actively reducing already-emitted CO₂ is essential. Accordingly, direct air capture (DAC)—a technology that directly extracts only CO₂ from the air—is gaining attention. However, effectively capturing pure CO₂ is not easy due to water vapor (H₂O) present in the air.

KAIST researchers have successfully used AI-driven machine learning techniques to identify the most promising COâ‚‚-capturing materials among metal-organic frameworks (MOFs), a key class of materials studied for this technology.

The research team, led by Professor Jihan Kim from the Department of Chemical and Biomolecular Engineering, in collaboration with a team at Imperial College London, their research in the journal Matter.

The difficulty in discovering high-performance materials is due to the complexity of structures and the limitations of predicting intermolecular interactions. To overcome this, the research team developed a machine learning force field (MLFF) capable of precisely predicting the interactions between COâ‚‚, water (Hâ‚‚O), and MOFs. This new method enables calculations of MOF adsorption properties with quantum-mechanics-level accuracy at vastly faster speeds than before.

Using this system, the team screened more than 8,000 experimentally synthesized MOF structures, identifying more than 100 promising candidates for COâ‚‚ capture. Notably, this included new candidates that had not been uncovered by traditional force-field-based simulations. The team also analyzed the relationships between MOF chemical structure and adsorption performance, proposing seven key chemical features that will help in designing new materials for DAC.

This research is recognized as a significant advance in the DAC field, greatly enhancing materials design and simulation by precisely predicting MOF-COâ‚‚ and MOF-Hâ‚‚O interactions.