Leveraging molecular simulations and machine learning to assess CO2, O2, and N2 adsorption and separation performances of diverse MOF databases

Abstract

We integrated molecular simulations and machine learning (ML) to comprehensively explore the gas adsorption and separation performances of both synthesized and hypothetical metal-organic frameworks (MOFs) available in five different MOF databases. Following the generation of CO2, O-2, and N-2 adsorption data for synthesized MOFs at varying pressures through grand canonical Monte Carlo (GCMC) simulations, we developed ML models that can swiftly and accurately predict the gas adsorption properties of any MOF based on its structural, chemical, and energetic characteristics. These ML models were then transferred to four distinct hypothetical MOF databases consisting of nearly 130,000 structures to assess their CO2, O-2, and N-2 adsorption properties in addition to CO2/N-2 and O-2/N-2 separation performances as a very efficient alternative to computationally time and resource demanding molecular simulations. We identified the top-performing materials from each database to uncover their structural, chemical, and topological properties leading to high selectivities and concluded that synthesized MOFs with narrow pores, lanthanide metals, and linkers featuring oxalate, pyridine dicarboxylate, and fumarate offer the highest CO2/N-2 selectivities. Our work presents the most extensive dataset produced for CO2, O-2, and N-2 gas adsorption in MOFs, composed of similar to 3.9 million data points for materials' structural, chemical, and energetic features, gas adsorption properties, and selectivities computed at different pressures to accelerate the materials design and discovery for CO2, O-2, and N-2 adsorption and separation.