干旱区生态环境知识资源中心(Arid): Machine Learning-Assisted Computational Screening of Metal-Organic Frameworks for Atmospheric Water Harvesting

Arid

DOI	10.3390/nano12010159
	Machine Learning-Assisted Computational Screening of Metal-Organic Frameworks for Atmospheric Water Harvesting
	Li, Lifeng; Shi, Zenan; Liang, Hong; Liu, Jie; Qiao, Zhiwei
通讯作者	Liang, H ; Qiao, ZW (corresponding author)，Guangzhou Univ, Sch Chem & Chem Engn, Guangzhou Key Lab New Energy & Green Catalysis, Guangzhou 510006, Peoples R China. ; Liu, J (corresponding author)，Wuhan Inst Technol, Sch Chem Engn & Pharm, Key Lab Green Chem Proc, Minist Educ, Wuhan 430073, Peoples R China.
来源期刊	NANOMATERIALS
EISSN	2079-4991
出版年	2022
卷号	12 期号:1
英文摘要	Atmospheric water harvesting by strong adsorbents is a feasible method of solving the shortage of water resources, especially for arid regions. In this study, a machine learning (ML)-assisted high-throughput computational screening is employed to calculate the capture of H2O from N-2 and O-2 for 6013 computation-ready, experimental metal-organic frameworks (CoRE-MOFs) and 137,953 hypothetical MOFs (hMOFs). Through the univariate analysis of MOF structure-performance relationships, Q(st) is shown to be a key descriptor. Moreover, three ML algorithms (random forest, gradient boosted regression trees, and neighbor component analysis (NCA)) are applied to hunt for the complicated interrelation between six descriptors and performance. After the optimizing strategy of grid search and five-fold cross-validation is performed, three ML can effectively build the predictive model for CoRE-MOFs, and the accuracy R-2 of NCA can reach 0.97. In addition, based on the relative importance of the descriptors by ML, it can be quantitatively concluded that the Q(st) is dominant in governing the capture of H2O. Besides, the NCA model trained by 6013 CoRE-MOFs can predict the selectivity of hMOFs with a R-2 of 0.86, which is more universal than other models. Finally, 10 CoRE-MOFs and 10 hMOFs with high performance are identified. The computational screening and prediction of ML could provide guidance and inspiration for the development of materials for water harvesting in the atmosphere.
英文关键词	metal-organic frameworks water harvesting molecular simulation algorithm absorption
类型	Article
语种	英语
开放获取类型	gold, Green Published
收录类别	SCI-E
WOS记录号	WOS:000748209500001
WOS关键词	STRUCTURE-PROPERTY RELATIONSHIP ; DRIVEN HEAT-PUMPS ; METHANE STORAGE ; ADSORPTION PERFORMANCE ; HYDROGEN STORAGE ; CO2 ; PREDICTION ; SEPARATION ; CAPACITY ; AMMONIA
WOS类目	Chemistry, Multidisciplinary ; Nanoscience & Nanotechnology ; Materials Science, Multidisciplinary ; Physics, Applied
WOS研究方向	Chemistry ; Science & Technology - Other Topics ; Materials Science ; Physics
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/376755
作者单位	[Li, Lifeng; Shi, Zenan; Liang, Hong; Qiao, Zhiwei] Guangzhou Univ, Sch Chem & Chem Engn, Guangzhou Key Lab New Energy & Green Catalysis, Guangzhou 510006, Peoples R China; [Liu, Jie] Wuhan Inst Technol, Sch Chem Engn & Pharm, Key Lab Green Chem Proc, Minist Educ, Wuhan 430073, Peoples R China
推荐引用方式 GB/T 7714	Li, Lifeng,Shi, Zenan,Liang, Hong,et al. Machine Learning-Assisted Computational Screening of Metal-Organic Frameworks for Atmospheric Water Harvesting[J],2022,12(1).
APA	Li, Lifeng,Shi, Zenan,Liang, Hong,Liu, Jie,&Qiao, Zhiwei.(2022).Machine Learning-Assisted Computational Screening of Metal-Organic Frameworks for Atmospheric Water Harvesting.NANOMATERIALS,12(1).
MLA	Li, Lifeng,et al."Machine Learning-Assisted Computational Screening of Metal-Organic Frameworks for Atmospheric Water Harvesting".NANOMATERIALS 12.1(2022).