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耦合机器学习与高通量计算研究疏水MOFs在CO2/C2H2膜分离中的构效关系

作者：韩荣美，韩琪，张政清，孙玉绣，乔志华

单位：天津工业大学先进分离膜材料全国重点实验室化学工程与技术学院，天津 300387

关键词：金属有机骨架；分子模拟；机器学习； CO2/C2H2分离

DOI号： 10.16159/j.cnki.issn1007-8924.2025.03.014

分类号： TQ051.8

出版年,卷(期):页码： 2025,45(3):136-144

摘要：

二氧化碳/乙炔(CO2/C2H2)分离在工业过程中的重要性推动了高效膜材料的开发，但膜材料在潮湿环境下的分离性能不稳定，因此开发能够在潮湿条件下保持优异性能的分离膜成为关键。本研究首先以ZIF-8的亨利系数为标准，建立了疏水性MOF数据库。通过巨正则蒙特卡罗(grand canonical Monte Carlo, GCMC)模拟和分子动力学(MD)模拟分别计算了用于CO2/C2H2分离的MOF的热力学性质和扩散性质，筛选出Top 20个高性能疏水MOF膜。随后，基于MOFs的特征描述符和模拟结果，训练和测试了5种不同的机器学习(ML)模型，并采用最优的ML模型揭示了影响CO2/C2H2分离性能的关键因素。最后，通过建立构效关系分析，确定了CO2/C2H2分离的最佳MOF结构参数范围，为MOF膜材料的开发与优化提供了理论依据。

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.

基金项目：

国家自然科学基金项目(22108202)

作者简介：

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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