面向乙烷/乙烯分离的金属有机框架膜的大规模计算筛选

Large-Scale Computational Screening of Metal-Organic Framework Membranes for Ethane/Ethylene Separation

相比于传统热驱动的低温蒸馏工艺,基于金属有机框架(Metal-organic frameworks,MOFs)的膜分离是一种在技术和成本上可行的乙烷/乙烯分离替代方案.为了加速MOF膜在这一气体分离领域中的应用,本工作提出了两步筛选策略对12,020个真实MOF膜材料进行了大规模计算筛选,其中MISQIQ04表现出最高的乙烷/乙烯膜选择系数(4.16)和较高的乙烷渗透率(4.35×10^(5)Barrer).通过结构-性能关系分析,可以发现窄孔径和低孔隙率的MOFs是选择性分离乙烷的最佳膜材料,并且乙烷的选择性吸附对乙烷/乙烯膜分离过程起着主导作用.与传统计算筛选方法相比,本工作所提出的筛选策略降低了约87.1%的计算时间成本.为了进一步缩短模拟时间,本工作还开发了机器学习分类模型以实现对高性能MOF膜的快速预筛选并探讨了该模型的可移植性.结果表明,增加数据集的多样性有助于提高所开发模型的可移植性和泛化能力.

Compared to the traditional heat-driven cryogenic distillation process,the membrane separation based on metalorganic frameworks(MOFs)is a technically and economically viable alternative for ethane/ethylene(CH/CH)separation.To accelerate the application of MOF membranes in this gas separation field,this study performed a large-scale computational screening of 12,020 real MOFs for the identification of optimal CH-selective MOF membrane materials.According to geometric and chemical analyses,2,192 MOFs without open metal sites and having pore limiting diameter no less than 0.38nm were first screened out.Grand canonical Monte Carlo and molecular dynamics simulations were subsequently carried out to mimic the adsorption and diffusion behaviors of ethane and ethylene in these MOFs respectively,based on which their CH/CHmembrane selectivities and CHpermeabilities were estimated.The results showed that MISQIQ04 exhibited the highest CH/CHmembrane selectivity(4.16)and moderate CHpermeability(4.35×10^(5) Barrer).Additionally,structureperformance relationships between the CH/CHmembrane selectivity and structural properties of MOFs were investigated,covering the largest cavity diameter(LCD),pore limiting diameter(PLD),density(ρ),gravimetric surface area(GSA),void fraction(VF),and pore volume(PV).The results indicated that MOFs with the structural characteristics of 0.4 nm≤LCD≤1nm,0.38 nm≤PLD≤0.75 nm,0.8 g/cm^(3)≤ρ≤2.5 g/cm^(3),GSA≤1,700 m^(2)/g,0.3≤VF≤0.73,and PV≤0.85 cm^(3)/g are optimal membrane materials for CH/CHseparation.Finally,a machine learning(ML)classifier was developed to achieve rapid prescreening of high-performing MOF membranes from a large MOF database,whose transferability was discussed on a hypothetical MOF database.Further t-Distributed Stochastic Neighbor Embedding analysis revealed that the ML model developed merely relying on a single MOF dataset generally exhibited poor transferability.Selecting the most representative and diverse MOFs from the entire MOF space for model development can help to improve the transferability and generalization ability of the developed model.