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Computational Screening of Metal–Organic Framework Membranes for the Separation of 15 Gas Mixtures.
- Source :
- Nanomaterials (2079-4991); Mar2019, Vol. 9 Issue 3, p467-467, 1p
- Publication Year :
- 2019
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 20794991
- Volume :
- 9
- Issue :
- 3
- Database :
- Complementary Index
- Journal :
- Nanomaterials (2079-4991)
- Publication Type :
- Academic Journal
- Accession number :
- 135684412
- Full Text :
- https://doi.org/10.3390/nano9030467