High-throughput and machine learning approaches for the discovery of metal organic frameworks

Metal-organic frameworks (MOFs) are promising nanoporous materials with diverse applications. Traditional material discovery based on intensive manual experiments has certain limitations on efficiency and effectiveness when faced with nearly infinite material space. The current situation offers an opportunity for high-throughput (HT) and machine learning (ML) approaches, including computational and experimental methods, as they have greatly improved the efficiency of MOF screening and discovery and have the capacity to deal with the enormous growth of data. In this review, we discuss the research progress in HT computation and experiments and their effect on MOF screening and discovery. We also highlight how ML-based approaches and the integration of HT methods with ML algorithms accelerate MOF design. In addition, we provide our insights on the future capability of data-driven techniques for MOF discovery, despite facing some knowledge gaps as an obstacle.