[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118080-en":3,"doc-seo-118080-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118080,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Machine Learning‑Assisted Low‑Dimensional Electrocatalysts Design for Hydrogen Evolution Reaction - Review","Efficient electrocatalysts enable hydrogen generation from electrolysis of water, yet conventional “trial and error” synthesis is costly, slow, and labor-intensive. Machine learning offers data-driven prediction of hydrogen evolution reaction (HER) performance by learning from experimental and theoretical datasets. This review consolidates advances in machine learning for low-dimensional electrocatalysts, spanning zero-dimensional nanoparticles and nanoclusters, one-dimensional nanotubes and nanowires, two-dimensional nanosheets, and related catalysts. It emphasizes how descriptors and algorithms influence screening and HER evaluation, then discusses future directions for machine learning in electrocatalysis.","Chemistry Department: Faculty Publications Department of Chemistry  \n10-13-2023  \nMachine Learning‑Assisted Low‑Dimensional Electrocatalysts Design for Hydrogen Evolution Reaction  \nJin Li  \nNaiteng Wu Jian Zhang  \nHong‑Hui Wu  \nKunming Pan  \nSee next page for additional authors  \nFollow this and additional works at: [https://digitalcommons.unl.edu/chemfacpub](https://digitalcommons.unl.edu/chemfacpub)  \n Part of the Analytical Chemistry Commons, Medicinal-Pharmaceutical Chemistry Commons, and the Other Chemistry Commons  \nThis Article is brought to you for free and open access by the Department of Chemistry at  \nDigitalCommons@University of Nebraska-Lincoln. It has been accepted for inclusion in Chemistry Department: Faculty Publications by an authorized administrator of DigitalCommons@University of Nebraska-Lincoln.  \nAuthors  \nJin Li, Naiteng Wu, Jian Zhang, Hong‑Hui Wu, Kunming Pan, Yingxue Wang, Guilong Liu, Xianming Liu, Zhenpeng Yao, and Qiaobao Zhang  \ne-ISSN 2150-5551 CN 31-2103/TB  \nREVIEW [https://doi.org/10.1007/s40820-023-01192-5](https://doi.org/10.1007/s40820-023-01192-5)  \nCite as  \nNano-Micro Lett.  \n(2023) 15:227  \nReceived: 26 June 2023  \nAccepted: 10 August 2023 © The Author(s) 2023  \nMachine Learning‑Assisted Low‑Dimensional Electrocatalysts Design for Hydrogen Evolution Reaction  \nJin Li 1, Naiteng Wu 1, Jian Zhang2, Hong-Hui Wu3,4 *, Kunming Pan5, Yingxue Wang6 *, Guilong Liu 1, Xianming Liu 1 *, Zhenpeng Yao7,8, Qiaobao Zhang9 *  \nHIGHLIGHTS  \n• The process of machine learning is introduced in detail.  \n• Recent developments in machine learning for low-dimensional electrocatalysts are briefly reviewed.  \n• Future directions and perspectives for machine learning in hydrogen evolution reaction are critically discussed.  \nABSTRACT Efficient electrocatalysts are crucial for hydrogen generation from electrolyzing water. Nevertheless, the conventional \"trial and error\" method for producing advanced electrocatalysts is not only cost-ineffective but also time-consuming and labor-intensive. Fortunately, the advancement of machine learning brings new opportunities for electrocatalysts discovery and design. By analyzing experimental and theoretical data, machine learning can effectively predict their hydrogen evolution reaction (HER) performance. This review summarizes recent developments in machine learning for low-dimensional electrocatalysts, including zero-dimension nanoparticles and nanoclusters, one-dimensional nanotubes and nanowires, two-dimensional nanosheets, as well as other electrocatalysts. In particular, the effects of descriptors and algorithms on screening low-dimensional electrocatalysts and investigating their HER performance are highlighted. Finally, the future directions and perspectives for machine learning in electrocatalysis are discussed, emphasizing the potential for machine learning  \nJin Li and Naiteng Wu contributed equally to this work.  \n* Hong-Hui Wu, [wuhonghui@ustb.edu.cn](wuhonghui@ustb.edu.cn); Yingxue Wang, [wangyingxue@cetc.com.cn](wangyingxue@cetc.com.cn); Xianming Liu, [myclxm@163.com](myclxm@163.com); Qiaobao Zhang, [zhangqiaobao@xmu.edu.cn](zhangqiaobao@xmu.edu.cn)  \n1 College of Chemistry and Chemical Engineering, and Henan Key Laboratory of Function-Oriented Porous Materials, Luoyang Normal University, Luoyang 471934, People’s Republic of China  \n2 New Energy Technology Engineering Lab of Jiangsu Province, College of Science, Nanjing University of Posts and Telecommunications (NUPT), Nanjing 210023, People’s Republic of China  \n3 School of Materials Science and Engineering, University of Science and Technology Beijing, Beijing 100083, People’s Republic of China  \n4 Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 8588, USA  \n5 Henan Key Laboratory of High-Temperature Structural and Functional Materials, National Joint Engineering Research Center for Abrasion Control and Molding of Metal Materials, Henan University of Science and Technology, Luoyang 471","cbCaidjAtcJEvAH7","https://ap.wps.com/l/cbCaidjAtcJEvAH7","pdf",4988705,1,29,"English","en",105,"# Introduction\n## Carbon neutrality and green energy context\n## Hydrogen generation and the role of HER electrocatalysts\n## Limitations of trial-and-error electrocatalyst discovery\n# Machine Learning for Low-Dimensional Electrocatalysts\n## Data-driven prediction of HER performance\n## Coverage of 0D, 1D, and 2D electrocatalyst classes\n## Descriptor and algorithm effects on screening\n# Future Directions and Perspectives\n## Advancing electrocatalyst discovery and mechanism insight","[{\"question\":\"Why are efficient electrocatalysts essential for hydrogen generation?\",\"answer\":\"Hydrogen evolution reaction (HER) requires electrocatalysts with high activity to substantially reduce overpotential during water electrolysis.\"},{\"question\":\"What problem does machine learning address in electrocatalyst development?\",\"answer\":\"Machine learning reduces reliance on costly, time-consuming “trial and error” by learning from experimental and theoretical data to predict HER performance.\"},{\"question\":\"Which low-dimensional electrocatalyst forms are discussed in the review?\",\"answer\":\"The review covers zero-dimensional nanoparticles and nanoclusters, one-dimensional nanotubes and nanowires, two-dimensional nanosheets, and other related electrocatalysts.\"}]","Machine Learning‑Assisted Low‑Dimensional Electrocatalysts Design for Hydrogen Evolution Reaction - 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