Yang Wenyuan, Liang Hong, Peng Feng, Liu Zili, Liu Jie, Qiao Zhiwei
Guangzhou Key Laboratory for New Energy and Green Catalysis, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, China.
School of Chemistry and Chemical Engineering, South China University of Technology, Guangzhou 510640, China.
Nanomaterials (Basel). 2019 Mar 20;9(3):467. doi: 10.3390/nano9030467.
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 times repeated -fold cross-validation to predict and analyze the relationships between six structural feature descriptors and 10 principal components. Based on the linear correlation value 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.
采用蒙特卡罗和分子动力学模拟方法，对6013种可供计算的实验性金属有机骨架膜（CoRE-MOFMs）用于15种二元气体混合物的分离性能进行筛选。经过单变量分析后，使用主成分分析将15种混合物的44个性能指标缩减为一个10维集。然后，将四种机器学习算法（决策树、随机森林、支持向量机和反向传播神经网络）与重复k折交叉验证相结合，以预测和分析六个结构特征描述符与10个主成分之间的关系。基于机器学习算法预测的线性相关值和均方根误差，随机森林算法最适合预测CoRE-MOFMs的分离性能。一个描述符，即孔隙极限直径，对每个主成分指标具有最高的权重重要性。最后，筛选出每种二元气体混合物的30种最佳CoRE-MOFMs。高通量计算筛选和高维性能指标的微观分析可以通过多结构变量和多性能变量之间的关系为实验研究提供指导。
