Machine-Learning-Aided Computational Study of Covalent Organic Frameworks for Reversed C₂H₆/C₂H₄ Separation

Abstract

The efficient separation of ethane/ethene (C2H6/C2H4) is imperative yet challenging in industrial processes. We herein combine machine learning (ML) and molecular simulation to predict optimal covalent organic frameworks (COFs) for reversed C2H6/C2H4 separation before experimental efforts. Using molecular simulations, two out of 601 CoRE COFs were identified with excellent separation performance, and eight CoRE COFs exhibit high C2H6/C2H4 selectivity surpassing all of the reported values, although these COFs have a relatively low working capacity. As for ML, we found that the random forest (RF) algorithm displays the highest accuracy (R2 = 0.97) among the four different models, and the density (ρ) of COFs was identified as the key factor that influences the C2H6/C2H4 selectivity. Moreover, the 10 best hypothetical COFs (hCOFs) with excellent selectivity were further predicted. Ultimately, the competitive adsorption behaviors of guests in COF-303 were disclosed, and the adsorption selectivity of COF-303 was enhanced by introducing the fluorine group. Results of this work could provide molecular-level insights for future design and synthesis of novel COFs that can directly remove low-concentration ethane from the C2H4/C2H6 mixture.