High-Throughput, Multiscale Computational Screening of Metal–Organic Frameworks for Xe/Kr Separation with Machine-Learned Parameters

Accurate evaluation of adsorbent materials' performance requires carrying out process simulations that take an analytical isotherm model as an input. In this work, we report a machine learning (ML) approach to approximate the saturation loading of nanoporous materials, an essential parameter for modeling the adsorption-based process simulation. Large-scale grand canonical Monte Carlo (GCMC) simulations were carried out to compute the single-component isotherms for Xe and Kr from the Computation-Ready Experimental Metal–Organic Framework (CoRE MOF) Database 2019. The generated data were used to fit the Langmuir model equation to obtain the saturation loading parameters, which were used as a basis to train several ML models. The performance of trained ML models was then compared with the pore volume-based approach, typically used in the literature, to approximate the saturation loading of the adsorbent material. Ideal vacuum swing adsorption (IVSA) simulations were carried out to screen a large number of MOFs. We found that the ML model better estimates the saturation loading from the curve fitting compared to the pore volume approach. Finally, we carried out high-fidelity vacuum swing adsorption simulations on 15 Xe-selective MOFs. While the IVSA approach provides quantitative information about the process performance metrics, we found that the commonly used performance metrics, such as Xe/Kr IAST selectivity, work as well as the shortcut methods (IVSA simulation) in ranking the adsorbent materials for Xe/Kr separation.