High-throughput computational screening

by Jianwen JIANG
High-throughput computational screening

We report a computational study to screen a large collection of metal-organic frameworks (MOFs) for the separation of a ternary gas mixture (CO2/N2/CH4) toward the upgrading of natural gas. By integrating Monte Carlo and molecular dynamics simulations, the adsorption, diffusion and permeation of the gas mixture are predicted. The structure-performance relationships are established between the geometrical descriptors of MOFs (pore liming diameter, density, void fraction and volumetric surface area) and the membrane performance metrics (permeability and permselectivity). Furthermore, principal component analysis is used to assess the interrelationships among the descriptors, then multiple linear regression is applied to quantitatively determine the respective effects of descriptors on performance. In addition, decision tree modelling is adopted to define a clear effective path for screening. Finally, seven best MOF membranes are identified for the single-step separation of both CO2 and N2 from CH4. The microscopic insights and structure-performance relationships from this computational study can facilitate the development of new MOF membranes for the upgrading of natural gas.


This work has been published in Journal of Membrane Science, 551 (2018) 47-54 on 12/01/2018 (https://doi.org/10.1016/j.memsci.2018.01.020)

Corresponding author(s): Jiang Jianwen (chejj@nus.edu.sg)

Corresponding authors(s) Webpage: http://cheed.nus.edu.sg/stf/chejj/