Integrating Multiscale Simulation with Machine Learning to Screen and Design FIL@COFs for Ethane-Selective Separation

Efficient and economical separation of C2H6/C2H4 is an imperative and extremely challenging process in the petrochemical industry. The C2H6-selective adsorbents with high working capacity and high selectivity are highly desirable from a practical application standpoint. In this study, we constructed a database of fluorinated ionic liquid@covalent organic frameworks (FIL@COFs) and screened out the high-performing FIL@COFs for C2H6-selective separation. Utilizing the optimal machine learning (ML) algorithm (XGBoost) and hyperparameters, we further revealed the key factors influencing the separation performance. The multiscale simulation not only validated the prediction accuracy of ML but also demonstrated that adjusting the largest cavity diameter of COFs with FILs could yield FIL@COFs with high performance for C2H6-selective separation. Our work provides essential guidance for designing new FIL@COF adsorbents for value-added gas purification.