膜科学与技术

机器学习预测筛选MOF气体分离膜的研究进展

作者：田凯，李东洋，段锟，王景，张亚涛

单位：郑州大学化工学院，郑州 450001

关键词： MOF膜；气体分离；机器学习；性能预测与筛选；分子模拟

出版年,卷(期):页码： 2023,43(6):149-158

摘要：

金属有机框架（MOFs）气体分离膜因其优异的分离性能在碳捕集、能源气体分离等领域得到广泛关注。尽管采用高通量分子模拟计算可以实现高性能MOF膜的筛选，但随着MOFs数量的激增，逐一计算MOF膜的气体分离性能需要消耗大量的计算资源，而基于机器学习方法可以快速进行MOF膜的性能预测与筛选，进而加速高性能MOF膜的设计制备流程。本综述系统介绍了机器学习预测筛选MOF膜的方法与流程，总结了当前的研究进展，分析了机器学习预测筛选MOF膜未来的方向与挑战。

Metal-organic frameworks (MOFs) gas separation membranes have attracted widespread attention in the field of carbon capture and energy gas separation due to their excellent separation performance. Although the screening of high-performance MOF membranes can be achieved using high-throughput molecular simulation calculations, with the proliferation of MOFs, calculating the gas separation performance of MOF membranes one by one requires significant computational resources. Machine learning-based methods can rapidly perform the performance projection and screening of MOF membranes, thereby accelerating the design and preparation process of high-performance MOF membranes. In this paper, the methods and procedures of machine learning prediction and screening of MOF membranes are systematically presented in four aspects: data preparation, feature engineering, model training and selection, and model evaluation and interpretation. Subsequently, the current research advances in machine learning screening of pure MOF membranes and MOF hybrid matrix membranes are summarized. Finally, the challenges and future directions of machine learning screening of MOF membranes are analyzed.

田凯（2000-），男，湖南浏阳人，硕士研究生，研究方向为机器学习辅助设计MOF气体分离膜，E-mail：tiankai2022@126.com。

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