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

doi:10.6023/A18120497

Acta Chim. Sinica ›› 2019, Vol. 77 ›› Issue (4): 323-339.DOI: 10.6023/A18120497 Previous Articles Next Articles

Special Issue: 多孔材料：金属有机框架(MOF)

Review

金属有机骨架的高通量计算筛选研究进展

刘治鲁^a,b, 李炜^a,b, 刘昊^b, 庄旭东^b, 李松^a,b

a 华中科技大学中欧清洁与可再生能源学院武汉 430074;
b 华中科技大学能源与动力工程学院煤燃烧国家重点实验室武汉 430074

投稿日期:2018-12-12 发布日期:2019-01-09
通讯作者: 李松 E-mail:songli@hust.edu.cn
作者简介:刘治鲁,2018年6月于东北大学获得学士学位,2018年9月至今在华中科技大学李松副教授课题组攻读硕士学位.主要研究兴趣为多孔材料的高通量计算筛选及其在能源储存转化方面的应用;李炜,2015年于华中科技大学获得学士学位,2015年至今师从李松副教授攻读博士学位.主要研究兴趣为高通量筛选技术在多孔材料研究中的应用;李松,博士,副教授.2006年和2009年自山东大学和韩国成均馆大学获得学士和硕士学位.2009年至2014年在美国范德堡大学化工系学习并获得博士学位.2014年至2015年8月在美国西北大学化工系Randall Q.Snurr教授课题组进行博士后研究.2015年8月加入华中科技大学能源与动力工程学院.主要研究兴趣为金属有机骨架的高通量计算筛选及其在能源储存转化和环境领域的应用.
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

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

Received:2018-12-12 Published:2019-01-09
Contact: 10.6023/A18120497 E-mail:songli@hust.edu.cn
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).

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

Cite this article

Liu Zhilu, Li Wei, Liu Hao, Zhuang Xudong, Li Song. Research Progress of High-throughput Computational Screening of Metal-Organic Frameworks[J]. Acta Chim. Sinica, 2019, 77(4): 323-339.

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金属有机骨架的高通量计算筛选研究进展

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

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[1]	Yang Liu, Fengqin Gao, Zhanying Ma, Yinli Zhang, Wuwu Li, Lei Hou, Xiaojuan Zhang, Yaoyu Wang. Co-based Metal-organic Framework for High-efficiency Degradation of Methylene Blue in Water by Peroxymonosulfate Activation [J]. Acta Chimica Sinica, 2024, 82(2): 152-159.
[2]	Bo Sun, Wenwen Ju, Tao Wang, Xiaojun Sun, Ting Zhao, Xiaomei Lu, Feng Lu, Quli Fan. Preparation of Highly-dispersed Conjugated Polymer-Metal Organic Framework Nanocubes for Antitumor Application [J]. Acta Chimica Sinica, 2023, 81(7): 757-762.
[3]	Fengbin Zheng, Kun Wang, Tian Lin, Yinglong Wang, Guodong Li, Zhiyong Tang. Research Progress on the Preparation of Metal-Organic Frameworks Encapsulated Metal Nanoparticle Composites and Their Catalytic Applications^★ [J]. Acta Chimica Sinica, 2023, 81(6): 669-680.
[4]	Kaiqing Wang, Shuo Yuan, Wangdong Xu, Dan Huo, Qiulin Yang, Qingxi Hou, Dehai Yu. Preparation and Adsorption Properties of ZIF-8@B-CNF Composite Aerogel [J]. Acta Chimica Sinica, 2023, 81(6): 604-612.
[5]	Zheng Yin, Yingbo Zhao, Minghua Zeng. Challenge, Advance and Emerging Opportunities for Metal-Organic Framework Glasses: from Dynamic Chemistry to Material Science and Noncrystalline Physics [J]. Acta Chimica Sinica, 2023, 81(3): 246-252.
[6]	Xingyi Qi, Yaofeng Hu, Ruoyu Wang, Yaqing Yang, Yufei Zhao. Recent Advance of Machine Learning in Selecting New Materials [J]. Acta Chimica Sinica, 2023, 81(2): 158-174.
[7]	Junchang Chen, Mingxing Zhang, Shuao Wang. Research Progress of Synthesis Methods for Crystalline Porous Materials [J]. Acta Chimica Sinica, 2023, 81(2): 146-157.
[8]	Yizhi Han, Jianhui Lan, Xue Liu, Weiqun Shi. Advances in Molecular Dynamics Studies of Molten Salts Based on Machine Learning [J]. Acta Chimica Sinica, 2023, 81(11): 1663-1672.
[9]	Xiaojuan Li, Ziyu Ye, Shuhan Xie, Yongjing Wang, Yonghao Wang, Yuancai Lv, Chunxiang Lin. Study on Performance and Mechanism of Phenol Degradation through Peroxymonosulfate Activation by Nitrogen/Chlorine Co-doped Porous Carbon Materials [J]. Acta Chimica Sinica, 2022, 80(9): 1238-1249.
[10]	Min Cheng, Shihui Wang, Lei Luo, Li Zhou, Kexin Bi, Yiyang Dai, Xu Ji. Large-Scale Computational Screening of Metal-Organic Framework Membranes for Ethane/Ethylene Separation [J]. Acta Chimica Sinica, 2022, 80(9): 1277-1288.
[11]	Shaobing Yan, Long Jiao, Chuanxin He, Hailong Jiang. Pyrolysis of ZIF-67/Graphene Composite to Co Nanoparticles Confined in N-Doped Carbon for Efficient Electrocatalytic Oxygen Reduction [J]. Acta Chimica Sinica, 2022, 80(8): 1084-1090.
[12]	Xu Yan, Hemi Qu, Ye Chang, Xuexin Duan. Application of Metal-Organic Frameworks in Gas Pre-concentration, Pre-separation and Detection [J]. Acta Chimica Sinica, 2022, 80(8): 1183-1202.
[13]	Linan Cao, Min Wei. Recent Progress of Electric Conductive Metal-Organic Frameworks Thin Film [J]. Acta Chimica Sinica, 2022, 80(7): 1042-1056.
[14]	Ben Niu, Zhenyu Zhai, Xiaoke Hao, Tingli Ren, Congju Li. Flexible Acetone Gas Sensor based on ZIF-8/Polyacrylonitrile (PAN) Composite Film [J]. Acta Chimica Sinica, 2022, 80(7): 946-955.
[15]	Fang Liu, Tingting Pan, Xiurong Ren, Weiren Bao, Jiancheng Wang, Jiangliang Hu. Research on Preparation and Benzene Adsorption Performance of HCDs@MIL-100(Fe) Adsorbents [J]. Acta Chimica Sinica, 2022, 80(7): 879-887.