High-throughput and machine learning approaches for the discovery of metal organic frameworks

doi:10.1063/5.0147650

	High-throughput and machine learning approaches for the discovery of metal organic frameworks
	Zhang, Xiangyu; Xu, Zezhao; Wang, Zidi; Liu, Huiyu; Zhao, Yingbo; Jiang, Shan
	2023-06-01
发表期刊	APL MATERIALS (IF:5.3[JCR-2023],5.5[5-Year])
EISSN	2166-532X
卷号	11 期号:6
DOI	10.1063/5.0147650
摘要	Metal-organic frameworks (MOFs) are promising nanoporous materials with diverse applications. Traditional material discovery based on intensive manual experiments has certain limitations on efficiency and effectiveness when faced with nearly infinite material space. The current situation offers an opportunity for high-throughput (HT) and machine learning (ML) approaches, including computational and experimental methods, as they have greatly improved the efficiency of MOF screening and discovery and have the capacity to deal with the enormous growth of data. In this review, we discuss the research progress in HT computation and experiments and their effect on MOF screening and discovery. We also highlight how ML-based approaches and the integration of HT methods with ML algorithms accelerate MOF design. In addition, we provide our insights on the future capability of data-driven techniques for MOF discovery, despite facing some knowledge gaps as an obstacle. © 2023 Author(s).
关键词	Machine learning Metal-Organic Frameworks Porous materials Current situation Diverse applications Experimental methods High-throughput Infinite material Machine learning approaches Material space Metalorganic frameworks (MOFs) Nanoporous Materials Traditional materials
URL	查看原文
收录类别	EI
语种	英语
出版者	American Institute of Physics Inc.
EI入藏号	20232314194618
EI主题词	Efficiency
EI分类号	531.1 Metallurgy ; 723.4 Artificial Intelligence ; 804.1 Organic Compounds ; 913.1 Production Engineering ; 951 Materials Science
原始文献类型	Journal article (JA)
引用统计	正在获取...
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/312343
专题	物质科学与技术学院物质科学与技术学院_硕士生物质科学与技术学院_博士生物质科学与技术学院_PI研究组_姜珊组物质科学与技术学院_PI研究组_赵英博组
通讯作者	Zhang, Xiangyu; Jiang, Shan
作者单位	School of Physical Science and Technology, ShanghaiTech University, Shanghai; 201210, China
第一作者单位	物质科学与技术学院
通讯作者单位	物质科学与技术学院
第一作者的第一单位	物质科学与技术学院
推荐引用方式 GB/T 7714	Zhang, Xiangyu,Xu, Zezhao,Wang, Zidi,et al. High-throughput and machine learning approaches for the discovery of metal organic frameworks[J]. APL MATERIALS,2023,11(6).
APA	Zhang, Xiangyu,Xu, Zezhao,Wang, Zidi,Liu, Huiyu,Zhao, Yingbo,&Jiang, Shan.(2023).High-throughput and machine learning approaches for the discovery of metal organic frameworks.APL MATERIALS,11(6).
MLA	Zhang, Xiangyu,et al."High-throughput and machine learning approaches for the discovery of metal organic frameworks".APL MATERIALS 11.6(2023).