Tang Hongjian, Duan Lunbo, Jiang Jianwen
Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education, School of Energy & Environment, Southeast University, Nanjing 210096, China.
Department of Chemical and Biomolecular Engineering, National University of Singapore, 117576 Singapore.
Langmuir. 2023 Nov 14;39(45):15849-15863. doi: 10.1021/acs.langmuir.3c01964. Epub 2023 Nov 3.
Metal-organic frameworks (MOFs) have attracted tremendous interest because of their tunable structures, functionalities, and physiochemical properties. The nearly infinite combinations of metal nodes and organic linkers have led to the synthesis of over 100,000 experimental MOFs and the construction of millions of hypothetical counterparts. It is intractable to identify the best candidates in the immense chemical space of MOFs for applications via conventional trial-to-error experiments or brute-force simulations. Over the past several years, machine learning (ML) has substantially transformed the way of MOF discovery, design, and synthesis. Driven by the abundant data from experiments or simulations, ML can not only efficiently and accurately predict MOF properties but also quantitatively derive structure-property relationships for rational design and screening. In this Perspective, we summarize recent achievements in leveraging ML for MOFs from the aspects of data acquisition, featurization, model training, and applications. Then, current challenges and new opportunities are discussed for the future exploration of ML to accelerate the development of new MOFs in this vibrant field.
金属有机框架材料（MOFs）因其可调控的结构、功能及物理化学性质而备受关注。金属节点和有机连接体几乎无穷无尽的组合方式已促成了超过100,000种实验性MOFs的合成以及数百万种假想对应物的构建。通过传统的试错实验或强力模拟在MOFs巨大的化学空间中识别出最佳候选材料是难以做到的。在过去几年里，机器学习（ML）极大地改变了MOF发现、设计和合成的方式。受来自实验或模拟的大量数据驱动，ML不仅能够高效且准确地预测MOF性质，还能定量推导结构-性质关系以进行合理设计和筛选。在这篇综述中，我们从数据获取、特征提取、模型训练及应用等方面总结了利用ML研究MOFs的近期成果。然后，针对在这个充满活力的领域中利用ML加速新型MOFs开发的未来探索，讨论了当前面临的挑战和新机遇。
