High-Throughput, Multiscale Computational Screening of Metal–Organic Frameworks for Xe/Kr Separation with Machine-Learned Parameters
Guobin Zhao, Yu Chen, Yongchul G. Chung
Abstract
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.