摘要/Abstract

近年来，金属有机骨架（Metal-Organic Frameworks，MOFs）在气体吸附分离领域的研究获得爆发式增长.随着MOFs数量的剧增，高通量计算筛选成为从大量MOFs中发现高性能目标材料和挖掘其构效关系的最有效研究方法.本综述对MOFs的高通量计算筛选中所用到的数据库包括实验合成的MOFs组成的数据库（experimental MOFs，eMOFs）和计算机设计的MOFs数据库（hypothetical MOFs，hMOFs）、计算筛选方法包括基于分子模拟和机器学习的筛选方法，及其在CH₄储存、H₂储存、CO₂捕捉和其他气体分离领域的研究进展进行了总结.旨在通过梳理该领域的研究进展和思路，明确未来的研究方向和面临的挑战，加快MOFs的研发进程，促进MOFs的商业化应用.
关键词: 金属有机骨架, 高通量筛选, 分子模拟, 机器学习, 吸附分离
During the past decades, extensive investigations on metal-organic frameworks (MOFs) with ultrahigh surface area for gas adsorption and separation have been reported. With the increasing number of possible MOFs, it has been a great challenge to discover high-performing MOFs of interest from numerous structures. High-throughput computational screening (HTCS) is a powerful tool to accelerate the development of MOFs for application of interest and explores the quantitative structure-property relationship (QSPR) to facilitate the rational design of top-performing MOFs. In this review, we summarize the MOF databases used for HTCS, mainly including MOFs collected from experimentally synthesized MOFs (i.e. eMOFs), and the hypothetical MOFs constructed by computer-aided tools (i.e. hMOFs). Moreover, there are currently two important screening strategies, molecular simulation and machine learning-based HTCS. A vast majority of HTCS have been performed by molecular simulations including grand canonical Monte Carlo (GCMC) and molecular dynamics (MD) simulations, in which the accuracy of force field parameters play a criticl role in the reliability of HTCS. GCMC is able to predict the adsorption performance of MOFs such as adsorption capacity, selectivity and heat of adsorption, whereas MD is commonly used to estimate the dynamic property of adsorbates, e.g. diffusion coefficient and permeability. Additionally, lattice GCMC and classical density functional theory (cDFT) are also highlighted for computational screening of MOFs in this review. Machine learning consisting of various algorithms is a recently developed strategy with high efficiency and low computational cost, which is a more powerful and promising technique in future. At last, the investigations on the utilization of HTCS in CH₄ storage, H₂ storage, CO₂ capture and gas separation were outlined. By reviewing the recent research progress in HTCS, we pointed out the current challenges and opportunities for the furture development of HTCS for MOFs, which will be the major engine for the commercialization of MOFs in various applications of interests.
Key words: metal-organic framework, computational screening, molecular simulation, machine learning, adsorption and separation


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