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.