Role of Pore Chemistry and Topology in the CO2 Capture Capabilities of MOFs: From Molecular Simulation to Machine Learning

Open framework materials (OFMs) such as metal–organic frameworks (MOFs) can provide structurally and chemically tailorable nanopores. This exceptional tunability has allowed for careful positioning of optimal adsorption sites within MOF pores to enable selective CO2 physisorption, making these materials promising for energy-efficient CO2 capture. However, given the multitude of features that can be simultaneously altered within the thousands of MOFs synthesized to date, it can be daunting to elucidate the most critical features for boosting CO2 capture capabilities. Here we use a multiscale approach—density functional theory (DFT), grand canonical Monte Carlo (GCMC), and machine learning (ML)—to investigate the role of various pore chemical and topological features in the enhancement of CO2 capture metrics of MOFs. To enable a thorough “sweep” of a target region of MOF structure-space, we used computational synthesis methods to create sets of MOFs encompassing all possible combinations of 16 topologies and 13 functionalized molecular building blocks. The adsorption of pure CO2, and CO2/H2 and CO2/N2 mixtures for the resulting 31 parent MOFs and its derivatives was then simulated, and CO2 capture metrics were calculated. Functionalization with hydroxyl, thiol, cyano, amino, or nitro chemistries was found to often improve CO2 capture metrics of the parent MOFs, but the efficacy of this strategy depended strongly on the pore topology. Decision trees were trained to predict the improvement or decline of CO2 capture metrics upon functionalization of parent MOFs, whereas five additional machine learning algorithms were trained to predict absolute metrics for all MOFs. The training of these algorithms allowed us to determine, without human bias, the relative importance of various pore chemical and structural/topological factors on the CO2 capture capabilities of MOFs.