Exploring Propane/Propylene Separation through Molecular Dynamics Simulations of Flexible Metal-Organic Framework Models

Propylene is a vital component for the petrochemical industry. The overwhelming majority of the studies is focused on propylene/propane separation. Nevertheless, as the ultimate goal is to obtain propylene as the end product, propylene-selective MOFs necessitate a demanding and complex desorption process, which consumes a substantial amount of energy and involves intricate operations. Consequently, having an adsorbent that preferentially adsorbs propane instead would be more advantageous. This approach would enable the production of high-purity propylene in a single step and lead to significant reductions in energy consumption and the quantity of adsorbent required. The Computation-Ready Experimental Metal-Organic Framework (CoRE MOF) 2019 database is utilized for the computational screening of flexible MOFs using molecular dynamics simulations to identify materials with strong potential for separating propane/propylene via self-diffusion. This screening process is the first to fully consider the impact of framework flexibility on the discovery of guest self-diffusion coefficients. The study underscores the significance of accounting for framework flexibility in investigations of gas molecules transport in MOFs, showcases the potential of using data-driven approaches to identify high-performance materials, and suggests methods for enhancing the predictive capabilities of screening workflows. The top 5 MOFs from the CoRE MOF database for propane and propylene separation at a temperature of 298 K are identified. It is observed that the presence of carbonyl groups greatly enhances the separation properties between propane and propylene. Subsequently, a machine learning approach is utilized to develop a model and uncover important features. The machine learning model's predicted values for self-diffusion are reasonably consistent with the data obtained from molecular dynamics simulations.