Machine learning of metal-organic framework design for carbon dioxide capture and utilization

Abstract

Metal-organic frameworks (MOFs) are attractive materials with easily tunable porous structures. Their selective carbon dioxide (CO₂) capture ability can be varied by altering the functionality of the organic ligands. However, rule-based approaches to tuning and developing MOFs with high CO₂ capture and conversion abilities are hindered by the numerous possible combinations of metal ions and organic linkers. Recently, machine learning (ML) has been applied to unravel key descriptors in predicting the performance of MOFs. This review summarizes recent advancements in ML models for MOFs in CO₂ capture and utilization, including high-throughput screening, neural network interatomic potential, and generative models. The development of sophisticated ML models for designing high-performance MOFs will play a critical role in addressing climate change in the future. Finally, the main challenges and limitations of current approaches in designing high-performance MOFs are discussed.