Machine learning accelerates the investigation of targeted MOFs: Performance prediction, rational design and intelligent synthesis

Metal-organic frameworks (MOFs) are a new class of nanoporous materials that are widely used in various emerging fields due to their large specific surface area, high porosity and tunable pore size. Its excellent chemical tunability provides a wide material space, in which tens of thousands of MOFs have been synthesized. However, it is impossible to explore such a vast chemical space through trial-and-error methods, making it difficult to achieve custom design of high-performance MOFs for specific applications. Machine learning (ML) is a powerful tool for guiding materials design and preparation by mining the hidden knowledge in data, and can even make prediction of material properties in seconds. This review aims to provide readers with a new perspective on how ML has been changing the research and development paradigm of MOFs. The four main data sources for MOFs and how to select the suitable features (descriptors) are firstly presented to enable the reader to quickly acquire data and carry out machine learning. Moreover, the application of ML in the development of MOFs is highlighted from the perspectives of performance prediction, rational design and intelligent synthesis. Finally, the future challenges and opportunities of combining ML with MOFs from the points of view of data and algorithms are proposed. This review will provide instructive guidance for ML-assisted MOFs research.