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Coupling machine learning with high-throughput computing study
the structure-performance correlation of hydrophobic MOFs for
membrane-based CO2/C2H2 separation

Authors： HAN Rongmei, HAN Qi, ZHANG Zhengqing, SUN Yuxiu, QIAO Zhihua

Units： State Key Laboratory of Advanced Separation Membrane Materials, School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, China

KeyWords： metal-organic frameworks; molecular simulation; machine learning; CO2/C2H2 separation

ClassificationCode：TQ051.8

year,volume(issue):pagination： 2025,45(3):136-144

Abstract：

The CO2/C2H2 separation in industrial processes has driven the development of highly efficient membrane materials. However, the presence of water under humidity environments can significantly impact the separation performance of these membranes. Thus, the development of separation membranes capable of maintaining superior performance under humidity conditions has become a critical area of focus. In this study, the hydrophobic MOF database was established, with the Henry coefficient of ZIF-8 serving as the reference. Then, the thermodynamic properties and diffusion coefficients of MOFs used for CO2/C2H2 separation were calculated using grand canonical Monte Carlo (GCMC) and molecular dynamics (MD) simulations. Based on these calculations, the membrane separation performance was evaluated, and the top 20 hydrophobic MOF membranes with high separation performance were identified. The MOF descriptors and simulation results were utilized as inputs to train and test five different machine learning (ML) models and the key factors influencing CO2/C2H2 separation performance were uncovered with the optimized ML model. Finally, the optimal range of MOF structural parameters for CO2/C2H2 separation was determined. This study provides theoretical foundations for the development and optimization of MOF membrane materials.

Funds：

国家自然科学基金项目(22108202)

AuthorIntro：

韩荣美(2000-)，女，山东德州人，硕士研究生，研究方向为MOF膜材料筛选与设计

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