Machine learning for prediction of CO2/N2/H2O selective adsorption and separation in metal-zeolites

Abstract

Carbon dioxide (CO2) capture, utilization, and storage technologies are crucial in reducing global warming and producing various high-value chemicals and fuels. It is challenging to effectively separate the molecules of CO2, N2, and H2O, whose kinetic diameters are close to each other. Although zeolites have garnered considerable attention in gas separation, the huge chemical space of metal-doped zeolites (metal-zeolites) coming from the combination of different metal active sites, topology, and Si/Al ratios poses a difficulty in finding an optimal material for selectively trapping CO2. In this study, we build machine learning (ML) models to predict the selective adsorption of CO2/N2/H2O on metal-zeolites through the regulation of electrostatic polarization interaction. The stability of 208 metal-zeolites encompassing five distinct topological structures is estimated through the formation energy (E f), indicating the potential accessibility in the experiment for most of the studied systems, especially for Sc-, Y-, and Zr-zeolites. Adsorption of CO2 on metal sites has two possible configurations: linear vs. bent CO2, depending on different embedded metals. The concerted binding of CO2 with both carbon and oxygen atoms on the metal center leads to the bent geometry and larger binding energies on metal-zeolites (Zr-, Nb-, Mo-zeolites). Accessible descriptors associated with the zeolites, adsorbates, and metals are selected to train the adsorption strength index (I ), showing good performance [mean absolute error (MAE) = 0.04, R2 = 0.88]. The predicted adsorption selectivity is in agreement with the experimental systems (Co-, Zn-, Cu-, Fe-SSZ-13). It is found that medium-pore-sized zeolites [pore limiting diameter (PLD) = ~7 Å] anchored with Zr, Nb, or Mo are promising materials for the CO2 adsorption and separation. The proposed ML scheme may also be applicable to give a fast prediction of CO2 adsorption and separation ability in other porous metal-organic frameworks or amorphous materials.