金属有机骨架的高通量计算筛选研究进展
收稿日期: 2018-12-12
网络出版日期: 2019-01-09
基金资助
项目受国家自然科学基金（No.51606081）和中欧清洁与可再生能源学院双一流研究生教学平台培育基金（No.ICARE-RP-2018-HYDRO-001）资助.
Research Progress of High-throughput Computational Screening of Metal-Organic Frameworks
Received date: 2018-12-12
Online published: 2019-01-09
Supported by
Project supported by the National Natural Science Foundation of China (No. 51606081) and Double first-class research funding of China-EU Institute for Clean and Renewable Energy (No. ICARE-RP-2018-HYDRO-001).
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.
刘治鲁 , 李炜 , 刘昊 , 庄旭东 , 李松 . 金属有机骨架的高通量计算筛选研究进展[J]. 化学学报, 2019 , 77(4) : 323 -339 . DOI: 10.6023/A18120497
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.
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