Multi-Scale Computational Design of Metal-Organic Frameworks for Carbon Capture Using Machine Learning and Multi-Objective Optimization

In this article, we computationally design a series of Metal-Organic Frameworks (MOFs) optimized for post-combustion carbon capture. Our computational workflow includes assembling building blocks and topologies into an initial set of hypothetical MOFs, using genetic algorithms to optimize this initial set for high CO2/N2 selectivity, and further evaluating the top materials through process-level modeling of their performance in a modified Skarstrom cycle. We identify two groups of MOFs that exhibit excellent process performance: one with relatively small pores in the range of 3-5Å and another with larger pores of 6-30Å. The performance of the first group is driven effectively by the exclusion of N2 from adsorption, with binding sites able to accommodate only CO2 molecules. The second group, with larger pores, features binding sites where CO2 molecules form multiple interactions with oxygen and functional groups of several building blocks, leading to high CO2/N2 selectivity. Within the employed process model and its assumptions, the materials generated in this study substantially outperform 13X reference zeolites, in silico optimized ion-exchanged LTA zeolites, and CALF-20. Although this study does not make any statements regarding the synthesizability, stability, or interaction with water of the proposed materials, the discovery of several hundred MOFs with promising characteristics gives us hope that some may advance to laboratory testing and possible scale-up.