Advancing CH4/H2 separation with covalent organic frameworks by combining molecular simulations and machine learning

Abstract

A high-throughput computational screening approach combined with machine learning (ML) was introduced to unlock the potential of both synthesized and hypothetical COFs (hypoCOFs) for adsorption-based CH₄/H₂ separation. We studied 597 synthesized COFs for adsorption of a CH₄/H₂ mixture using Grand Canonical Monte Carlo (GCMC) simulations under pressure-swing adsorption (PSA) and vacuum-swing adsorption (VSA) conditions. Based on the simulation results, the CH₄/H₂ selectivities, CH₄ working capacities, adsorbent performance scores, and regenerabilities of the synthesized COFs were assessed and the structural properties of the top-performing COFs were identified. The hypoCOF database composed of 69 840 materials was then filtered to identify 7737 hypothetical materials having similar structural properties to the top synthesized COFs. These hypothetical COFs were then examined for CH₄/H₂ separation using molecular simulations and the results showed that the top hypoCOFs have CH₄ selectivities and working capacities in the ranges of 21.9–28.7 (64.7–128.6) and 5.8–7.6 (1.3–3.1) mol kg⁻¹ under PSA (VSA) conditions, respectively, outperforming the synthesized COFs and metal–organic frameworks (MOFs). ML models were then developed based on the hypoCOF simulation results to accurately predict the CH₄/H₂ mixture adsorption properties of all remaining hypothetical materials when their structural and chemical properties are fed into the models. These models accurately assessed the CH₄/H₂ mixture separation performances of any hypoCOF within seconds without performing computationally demanding molecular simulations. The computational approach that we have proposed in this study will provide an accurate and efficient assessment of COF materials for CH₄/H₂ separation and significantly accelerate the experimental efforts towards the design and discovery of new high-performing COF adsorbents.