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

Metal-organic frameworks (MOFs) are attractive materials with easily tunable porous structures. Their selective carbon dioxide (CO 2 ) capture ability can be varied by altering the functionality of the organic ligands. However, rule-based approaches to tuning and developing MOFs with high CO 2 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 CO 2 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. • This review addresses the machine learning applications for the discovery of metal-organic frameworks for CO 2 capture. • Available data sources for machine learning applications based on both simulations and experiments are introduced. • Applications of machine learning, deep learning, generative modeling, and machine learning force fields are investigated. • Challenges and limitations of the current machine learning paradigm are discussed.