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.

金属有机骨架材料(MOFs)是一类新型纳米多孔材料，由于其比表面积大、孔隙率高和孔径大小可调等特点，被广泛应用于各个新兴领域。其出色的化学可调性提供了广阔的材料空间，其中已合成了数以万计的 MOF。然而，不可能通过反复试验的方法探索如此广阔的化学空间，因此难以实现针对特定应用的高性能 MOF 的定制设计。机器学习（ML）是通过挖掘数据中隐藏的知识来指导材料设计和制备的强大工具，甚至可以在几秒钟内预测材料的特性。这篇综述旨在为读者提供一个新的视角，让他们了解 ML 如何改变 MOF 的研发范式。首先介绍了 MOF 的四个主要数据来源以及如何选择合适的特征（描述符），以使读者能够快速获取数据并进行机器学习。此外，从性能预测、合理设计和智能合成的角度，重点介绍了ML在MOFs开发中的应用。最后，从数据和算法的角度提出了 ML 与 MOFs 结合的未来挑战和机遇。这篇综述将为 ML 辅助的 MOFs 研究提供指导性指导。理性设计，智能综合。最后，从数据和算法的角度提出了 ML 与 MOFs 结合的未来挑战和机遇。这篇综述将为 ML 辅助的 MOFs 研究提供指导性指导。理性设计，智能综合。最后，从数据和算法的角度提出了 ML 与 MOFs 结合的未来挑战和机遇。这篇综述将为 ML 辅助的 MOFs 研究提供指导性指导。