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Computational Screening of Metal–Organic Framework Membranes for the Separation of 15 Gas Mixtures

1
Guangzhou Key Laboratory for New Energy and Green Catalysis, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, China
2
School of Chemistry and Chemical Engineering, South China University of Technology, Guangzhou 510640, China
3
School of Chemistry and Chemical Engineering, Wuhan University of Technology, Wuhan 430072, China
*
Author to whom correspondence should be addressed.
Nanomaterials 2019, 9(3), 467; https://doi.org/10.3390/nano9030467
Received: 26 January 2019 / Revised: 7 March 2019 / Accepted: 17 March 2019 / Published: 20 March 2019
(This article belongs to the Special Issue Computational Materials Design for Renewable Energy Applications)
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Abstract

The Monte Carlo and molecular dynamics simulations are employed to screen the separation performance of 6013 computation-ready, experimental metal–organic framework membranes (CoRE-MOFMs) for 15 binary gas mixtures. After the univariate analysis, principal component analysis is used to reduce 44 performance metrics of 15 mixtures to a 10-dimension set. Then, four machine learning algorithms (decision tree, random forest, support vector machine, and back propagation neural network) are combined with k times repeated k-fold cross-validation to predict and analyze the relationships between six structural feature descriptors and 10 principal components. Based on the linear correlation value R and the root mean square error predicted by the machine learning algorithm, the random forest algorithm is the most suitable for the prediction of the separation performance of CoRE-MOFMs. One descriptor, pore limiting diameter, possesses the highest weight importance for each principal component index. Finally, the 30 best CoRE-MOFMs for each binary gas mixture are screened out. The high-throughput computational screening and the microanalysis of high-dimensional performance metrics can provide guidance for experimental research through the relationships between the multi-structure variables and multi-performance variables. View Full-Text
Keywords: metal–organic framework; gas separation; machine learning; molecular simulation; linear dimension reduction metal–organic framework; gas separation; machine learning; molecular simulation; linear dimension reduction
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Yang, W.; Liang, H.; Peng, F.; Liu, Z.; Liu, J.; Qiao, Z. Computational Screening of Metal–Organic Framework Membranes for the Separation of 15 Gas Mixtures. Nanomaterials 2019, 9, 467.

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