Advances in machine learning-based materials research for MOFs: energy gas adsorption separation

被引:0
作者
Wen Y. [1 ]
Fu J. [1 ]
Liu D. [1 ,2 ]
机构
[1] State Key Laboratory of Organic-Inorganic Composites, Beijing University of Chemical Technology, Beijing
[2] College of Chemical Engineering, Qinghai University, Qinghai, Xining
来源
Huagong Xuebao/CIESC Journal | 2024年 / 75卷 / 04期
关键词
adsorption; computer simulation; machine learning; MOFs; separation;
D O I
10.11949/0438-1157.20231381
中图分类号
学科分类号
摘要
Metal-organic frameworks (MOFs) have attracted much attention in the field of gas adsorption and separation due to their high porosity and ultra-high specific surface area, and the database of MOFs has been enriched as a result. The use of high-throughput computational screening methods can provide rich structural properties and performance data, which is beneficial to screening materials with high performance from a large number of metal-organic framework materials. In order to fully explore the information within the data, machine learning is used as an auxiliary tool that can reveal the implicit metal-organic framework structure and property relationships. To gain a greater understanding of the performance trends of metal-organic framework materials in different applications, especially in gas storage and separation, machine learning methods are also widely used. The latest research progress in machine learning prediction and design of metal-organic framework materials applied to the adsorption and separation of combustible gases is reviewed in terms of the descriptors of metal-organic frameworks suitable for machine learning work, and the screening and prediction of material properties by using machine learning methods, which accelerates the pace of the design and development of metal-organic frameworks, and guides the direction and rules of material synthesis, reducing the cost of manpower and material resources. © 2024 Materials China. All rights reserved.
引用
收藏
页码:1370 / 1381
页数:11
相关论文
共 83 条
[1]  
Liu Z X, Wang Y J, Hao C L, Et al., Metal-organic frameworks: metathesis of zinc(Ⅱ) with copper(Ⅱ) for efficient CO<sub>2</sub>∕CH<sub>4</sub> separation, CIESC Journal, 72, pp. 546-553, (2021)
[2]  
Pei R H, Wang Y H, Zhang X R, Et al., Synergistic of carbon nanotube∕cyclodextrin metal organic framework for enhancing CO<sub>2</sub> separation of mixed matrix membranes, CIESC Journal, 73, 9, pp. 3904-3914, (2022)
[3]  
Zhang H H, Wu X L, Chen C C, Et al., Preparation of 2D lamellar CD-MOF membranes for accurate separation of mixed solvents, CIESC Journal, 73, 10, pp. 4539-4550, (2022)
[4]  
Wang Y, Xiong Q Z, Chen Y, Et al., Research on Zr-based metal-organic frameworks for NH<sub>3</sub> adsorption, CIESC Journal, 73, 4, pp. 1772-1780, (2022)
[5]  
Xi G J, Liu Z H, Lei G P., Enhanced adsorption and separation of low concentration coalbed methane based on synergistic effect between FeTPPs and CuBTC, CIESC Journal, 73, 9, pp. 3940-3949, (2022)
[6]  
Zhang X Q, Zhang C, Zhang D Y, Et al., Study on the carbon capture performance of highly selective PEI@MOF-808 adsorbent in humid flue gas, CIESC Journal, 74, 10, pp. 4330-4342, (2023)
[7]  
Wang L, Jiang Y, Zhong D Z, Et al., Carbonized metal-organic framework for carbon dioxide reduction to ethylene and ethanol, CIESC Journal, 73, 8, pp. 3576-3585, (2022)
[8]  
Bernini M C, Fairen-Jimenez D, Pasinetti M, Et al., Screening of bio-compatible metal-organic frameworks as potential drug carriers using Monte Carlo simulations, Journal of Materials Chemistry. B, 2, 7, pp. 766-774, (2014)
[9]  
Campbell M G, Liu S F, Swager T M, Et al., Chemiresistive sensor arrays from conductive 2D metal-organic frameworks, Journal of the American Chemical Society, 137, 43, pp. 13780-13783, (2015)
[10]  
Wang Y J, Li S H, Zhao Z P., Molecular simulation study on adsorption and separation of H<sub>2</sub>∕He mixtures by M-MOF-74, CIESC Journal, 73, 10, pp. 4507-4517, (2022)