Citation: Liu Zhilu, Li Wei, Liu Hao, Zhuang Xudong, Li Song. Research Progress of High-throughput Computational Screening of Metal-Organic Frameworks[J]. Acta Chimica Sinica, ;2019, 77(4): 323-339. doi: 10.6023/A18120497 shu

Research Progress of High-throughput Computational Screening of Metal-Organic Frameworks

Liu Zhilu ^a,b ,
Li Wei ^a,b,† ,
Liu Hao ^b ,
Zhuang Xudong ^b ,
Li Song ^a,b,,

a.
China-EU Institute for Clean and Renewable Energy, Huazhong University of Science and Technology, Wuhan 430074
b.
State Key Laboratory of Coal Combustion, School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074

Corresponding author: Li Song, songli@hust.edu.cn
Received Date: 12 December 2018
Available Online: 8 April 2019
Fund Project: Double first-class research funding of China-EU Institute for Clean and Renewable Energy ICARE-RP-2018-HYDRO-001Project 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)the National Natural Science Foundation of China 51606081

Figures(13)

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.
- metal-organic framework,
- computational screening,
- molecular simulation,
- machine learning,
- adsorption and separation
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