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Research Progress of Machine Learning in MOF Gas Separation Membranes Prediction and Screening
Authors: TIAN Kai, LI Dongyang, DUAN Kun, WANG Jing, ZHANG Yatao
Units: School of Chemical Engineering, Zhengzhou University, Zhengzhou, 450001, China
KeyWords: MOF membrane; gas separation; machine learning; performance prediction and screening; molecular simulation
ClassificationCode:O604;TQ028.8
year,volume(issue):pagination: 2023,43(6):149-158

Abstract:
 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.

Funds:
国家自然科学基金青年项目(22108258);河南省自然科学基金优秀青年基金项目(22300420085);河南省高校科技创新人才支持计划(24HASTIT004)

AuthorIntro:
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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