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Gradient boosting decision tree algorithms for accelerating nanofiltration membrane design and discovery

文献类型：期刊论文

作者Gong, Weijia6; Xu, Hangbin5; Lu, Jinyan6; Kim, Jungbin4; Zhao, Yan3; Li, Ni2; Zhang, Yixuan1; Yang, Jiaxuan5; Xu, Daliang5; Liang, Heng5
刊名DESALINATION
出版日期2024-12-21
卷号592页码:12
关键词Nanofiltration membrane Interfacial polymerization Gradient boosting decision tree algorithms Permeance Salt rejection
ISSN号0011-9164
DOI10.1016/j.desal.2024.118072
通讯作者Gong, Weijia(gongweijia@126.com)
英文摘要Interfacial polymerization is the most widely used strategy for nanofiltration membrane fabrication. Despite extensive research on this technology, further improvement in permeance and salt rejection is still essential due to its multidimensional characteristics, including the types of membrane material and the conditions of membrane optimizing fabrication. Herein, we applied four gradient boosting decision tree algorithms to precisely identify the candidate monomers (represented by the molecular descriptors) and their fabrication conditions. The result of the model evaluation indicated the Extreme Gradient Boosting (XGBoost) algorithm had the best predictive performance in accuracy and generalization in predicting membrane permeance and salt rejection, with the corresponding determination coefficients on the test set being 0.76 and 0.88. Shapley additive explanation analysis showed that the aqueous monomer concentration was the most influential fabrication condition in membrane performance. Besides, the partition coefficient (Log P) and topological polar surface area were the most important molecular descriptors in water permeance and salt rejection, respectively. Overall, this study proposed innovative machine learning algorithms to disentangle the multidimensional interactions of various influencing factors on membrane performance, thus initiating a paradigm shift in the development of highperformance nanofiltration membranes.
资助项目National Natural Science Foundation of China[52300083] ; Heilongjiang Province Post-doctoral Fund[LBH-Z23181] ; China Postdoctoral Science Founda-tion[2023M740918] ; Postdoctoral Fellowship Program of CPSF[GZB20230965] ; Natural Science Foundation of the Hei-longjiang Province of China[LH2021E007] ; Fonds Wetenschappe-lijk Onderzoek-Vlaanderen (FWO)[12A6823N]
WOS研究方向Engineering ; Water Resources
语种英语
WOS记录号WOS:001310832400001
出版者ELSEVIER
资助机构National Natural Science Foundation of China ; Heilongjiang Province Post-doctoral Fund ; China Postdoctoral Science Founda-tion ; Postdoctoral Fellowship Program of CPSF ; Natural Science Foundation of the Hei-longjiang Province of China ; Fonds Wetenschappe-lijk Onderzoek-Vlaanderen (FWO)
源URL[http://ir.ieecas.cn/handle/361006/17781]  
专题地球环境研究所_黄土与第四纪地质国家重点实验室（2010~）
地球环境研究所_生态环境研究室
通讯作者Gong, Weijia
作者单位1.Chinese Acad Sci, Inst Earth Environm, State Key Lab Loess & Quaternary Geol, Xian 710061, Peoples R China
2.Vrije Univ Brussel, Dept Water & Climate, Brussels, Belgium
3.Katholieke Univ Leuven, Dept Chem Engn, Celestijnenlaan 200F, B-3001 Leuven, Belgium
4.Wenzhou Kean Univ, Coll Sci Math & Technol, Dept Environm Sci, Wenzhou 325060, Peoples R China
5.Harbin Inst Technol, Sch Environm, State Key Lab Urban Water Resource & Environm, Harbin 150090, Peoples R China
6.Northeast Agr Univ, Sch Engn, 600 Changjiang St, Harbin 150030, Peoples R China
推荐引用方式
GB/T 7714		 Gong, Weijia,Xu, Hangbin,Lu, Jinyan,et al. Gradient boosting decision tree algorithms for accelerating nanofiltration membrane design and discovery[J]. DESALINATION,2024,592:12.
APA Gong, Weijia.,Xu, Hangbin.,Lu, Jinyan.,Kim, Jungbin.,Zhao, Yan.,...&Liang, Heng.(2024).Gradient boosting decision tree algorithms for accelerating nanofiltration membrane design and discovery.DESALINATION,592,12.
MLA Gong, Weijia,et al."Gradient boosting decision tree algorithms for accelerating nanofiltration membrane design and discovery".DESALINATION 592(2024):12.

入库方式： OAI收割

来源：地球环境研究所

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