Machine Learning‐Assisted Exploration of Chemical Space of MOF‐5 Analogues for Enhanced C2H6/C2H4 Separation

Adsorptive separation using C2H6‐selective adsorbents can produce high‐purity C2H4 directly, making it an energy‐efficient separation method with the potential to replace cryogenic distillation. While many C2H6‐selective MOFs have been reported, developing MOFs with both large C2H6 adsorption capacity and high C2H6/C2H4 selectivity remains challenging. Herein, we present a machine learning‐assisted molecular simulation strategy to explore the C2H6/C2H4 separation capability of pcu‐MOFs isoreticular to MOF‐5. The eXtreme Gradient Boosting (XGBoost) algorithm showed high accuracy in predicting the C2H6/C2H4 selectivity and C2H6 uptake, where Henry coefficient ratio (S0) and Henry coefficient of C2H6 (K(C2H6)) were identified as key factors. We further synthesized the top‐performing MOF termed A‐66 and experimentally verified its large C2H6 adsorption capacity and excellent C2H6/C2H4 separation performance. This work provides a valuable strategy for exploring the chemical space of MOF‐5 analogues and identifying promising candidates for the efficient purification of C2H4 from C2H6/C2H4 mixtures.