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

Abstract Adsorptive separation using C 2 H 6 ‐selective adsorbents can produce high‐purity C 2 H 4 directly, making it an energy‐efficient separation method with the potential to replace cryogenic distillation. Although many C 2 H 6 ‐selective MOFs have been reported, developing MOFs with both large C 2 H 6 adsorption capacity and high C 2 H 6 /C 2 H 4 selectivity remains challenging. Herein, we present a machine learning–assisted molecular simulation strategy to explore the C 2 H 6 /C 2 H 4 separation capability of pcu ‐MOFs isoreticular to MOF‐5. The eXtreme gradient boosting (XGBoost) algorithm showed high accuracy in predicting the C 2 H 6 /C 2 H 4 selectivity and C 2 H 6 uptake, where Henry coefficient ratio ( S 0 ) and Henry coefficient of C 2 H 6 ( K (C 2 H 6 )) were identified as key factors. We further synthesized the top‐performing MOF termed A‐66 and experimentally verified its large C 2 H 6 adsorption capacity and excellent C 2 H 6 /C 2 H 4 separation performance. This work provides a valuable strategy for exploring the chemical space of MOF‐5 analogs and identifying promising candidates for the efficient purification of C 2 H 4 from C 2 H 6 /C 2 H 4 mixtures.