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Ding R, Chen J, Chen Y, Liu J, Bando Y, Wang X. Unlocking the potential: machine learning applications in electrocatalyst design for electrochemical hydrogen energy transformation. Chem Soc Rev 2024; 53:11390-11461. [PMID: 39382108 DOI: 10.1039/d4cs00844h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/10/2024]
Abstract
Machine learning (ML) is rapidly emerging as a pivotal tool in the hydrogen energy industry for the creation and optimization of electrocatalysts, which enhance key electrochemical reactions like the hydrogen evolution reaction (HER), the oxygen evolution reaction (OER), the hydrogen oxidation reaction (HOR), and the oxygen reduction reaction (ORR). This comprehensive review demonstrates how cutting-edge ML techniques are being leveraged in electrocatalyst design to overcome the time-consuming limitations of traditional approaches. ML methods, using experimental data from high-throughput experiments and computational data from simulations such as density functional theory (DFT), readily identify complex correlations between electrocatalyst performance and key material descriptors. Leveraging its unparalleled speed and accuracy, ML has facilitated the discovery of novel candidates and the improvement of known products through its pattern recognition capabilities. This review aims to provide a tailored breakdown of ML applications in a format that is readily accessible to materials scientists. Hence, we comprehensively organize ML-driven research by commonly studied material types for different electrochemical reactions to illustrate how ML adeptly navigates the complex landscape of descriptors for these scenarios. We further highlight ML's critical role in the future discovery and development of electrocatalysts for hydrogen energy transformation. Potential challenges and gaps to fill within this focused domain are also discussed. As a practical guide, we hope this work will bridge the gap between communities and encourage novel paradigms in electrocatalysis research, aiming for more effective and sustainable energy solutions.
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Affiliation(s)
- Rui Ding
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, IL 60637, USA.
- Chemical Sciences and Engineering Division, Physical Sciences and Engineering Directorate, Argonne National Laboratory, Lemont, IL 60439, USA.
| | - Junhong Chen
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, IL 60637, USA.
- Chemical Sciences and Engineering Division, Physical Sciences and Engineering Directorate, Argonne National Laboratory, Lemont, IL 60439, USA.
| | - Yuxin Chen
- Department of Computer Science, University of Chicago, Chicago, IL 60637, USA.
| | - Jianguo Liu
- Institute of Energy Power Innovation, North China Electric Power University, Beijing, 102206, China
| | - Yoshio Bando
- Chemistry Department, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia
| | - Xuebin Wang
- College of Engineering and Applied Sciences, Nanjing University, Nanjing, 210093, China.
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He S, Wu M, Li S, Jiang Z, Hong H, Cloutier SG, Yang H, Omanovic S, Sun S, Zhang G. Research Progress on Graphite-Derived Materials for Electrocatalysis in Energy Conversion and Storage. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27248644. [PMID: 36557778 PMCID: PMC9782663 DOI: 10.3390/molecules27248644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Revised: 12/05/2022] [Accepted: 12/05/2022] [Indexed: 12/12/2022]
Abstract
High-performance electrocatalysts are critical to support emerging electrochemical energy storage and conversion technologies. Graphite-derived materials, including fullerenes, carbon nanotubes, and graphene, have been recognized as promising electrocatalysts and electrocatalyst supports for the oxygen reduction reaction (ORR), oxygen evolution reaction (OER), hydrogen evolution reaction (HER), and carbon dioxide reduction reaction (CO2RR). Effective modification/functionalization of graphite-derived materials can promote higher electrocatalytic activity, stability, and durability. In this review, the mechanisms and evaluation parameters for the above-outlined electrochemical reactions are introduced first. Then, we emphasize the preparation methods for graphite-derived materials and modification strategies. We further highlight the importance of the structural changes of modified graphite-derived materials on electrocatalytic activity and stability. Finally, future directions and perspectives towards new and better graphite-derived materials are presented.
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Affiliation(s)
- Shuaijie He
- Engineering Research Center of Nano-Geomaterials of Ministry of Education, China University of Geosciences, Wuhan 430074, China
- Faculty of Materials Science and Chemistry, China University of Geosciences, Wuhan 430074, China
- School of Earth Sciences, China University of Geosciences, Wuhan 430074, China
| | - Mingjie Wu
- Department of Chemical Engineering, McGill University, 3610 University Street, Montreal, QC H3A 0C5, Canada
- Institut National de la Recherche Scientifique (INRS), Centre Énergie Matériaux Télécommunications, Varennes, QC J3X 1P7, Canada
- Correspondence: (M.W.); (H.Y.); (S.O.); (G.Z.)
| | - Song Li
- Engineering Research Center of Nano-Geomaterials of Ministry of Education, China University of Geosciences, Wuhan 430074, China
- Faculty of Materials Science and Chemistry, China University of Geosciences, Wuhan 430074, China
| | - Zhiyi Jiang
- Engineering Research Center of Nano-Geomaterials of Ministry of Education, China University of Geosciences, Wuhan 430074, China
- Faculty of Materials Science and Chemistry, China University of Geosciences, Wuhan 430074, China
| | - Hanlie Hong
- Engineering Research Center of Nano-Geomaterials of Ministry of Education, China University of Geosciences, Wuhan 430074, China
- School of Earth Sciences, China University of Geosciences, Wuhan 430074, China
| | - Sylvain G. Cloutier
- Department of Electrical Engineering, École de Technologie Supérieure (ÉTS), Montreal, QC H3C 1K3, Canada
| | - Huaming Yang
- Engineering Research Center of Nano-Geomaterials of Ministry of Education, China University of Geosciences, Wuhan 430074, China
- Faculty of Materials Science and Chemistry, China University of Geosciences, Wuhan 430074, China
- Hunan Key Laboratory of Mineral Materials and Application, School of Minerals Processing and Bioengineering, Central South University, Changsha 410083, China
- Correspondence: (M.W.); (H.Y.); (S.O.); (G.Z.)
| | - Sasha Omanovic
- Department of Chemical Engineering, McGill University, 3610 University Street, Montreal, QC H3A 0C5, Canada
- Correspondence: (M.W.); (H.Y.); (S.O.); (G.Z.)
| | - Shuhui Sun
- Institut National de la Recherche Scientifique (INRS), Centre Énergie Matériaux Télécommunications, Varennes, QC J3X 1P7, Canada
| | - Gaixia Zhang
- Department of Electrical Engineering, École de Technologie Supérieure (ÉTS), Montreal, QC H3C 1K3, Canada
- Correspondence: (M.W.); (H.Y.); (S.O.); (G.Z.)
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Evaluation of polymer electrolyte membrane electrolysis by explainable machine learning, optimum classification model, and active learning. J APPL ELECTROCHEM 2022. [DOI: 10.1007/s10800-022-01786-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Zhang X, Tian Y, Chen L, Hu X, Zhou Z. Machine Learning: A New Paradigm in Computational Electrocatalysis. J Phys Chem Lett 2022; 13:7920-7930. [PMID: 35980765 DOI: 10.1021/acs.jpclett.2c01710] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Designing and screening novel electrocatalysts, understanding electrocatalytic mechanisms at an atomic level, and uncovering scientific insights lie at the center of the development of electrocatalysis. Despite certain success in experiments and computations, it is still difficult to achieve the above objectives due to the complexity of electrocatalytic systems and the vastness of the chemical space for candidate electrocatalysts. With the advantage of machine learning (ML) and increasing interest in electrocatalysis for energy conversion and storage, data-driven scientific research motivated by artificial intelligence (AI) has provided new opportunities to discover promising electrocatalysts, investigate dynamic reaction processes, and extract knowledge from huge data. In this Perspective, we summarize the recent applications of ML in electrocatalysis, including the screening of electrocatalysts and simulation of electrocatalytic processes. Furthermore, interpretable machine learning methods for electrocatalysis are discussed to accelerate knowledge generation. Finally, the blueprint of machine learning is envisaged for future development of electrocatalysis.
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Affiliation(s)
- Xu Zhang
- School of Chemical Engineering, Zhengzhou University, Zhengzhou 450001, P. R. China
| | - Yun Tian
- School of Chemical Engineering, Zhengzhou University, Zhengzhou 450001, P. R. China
| | - Letian Chen
- School of Materials Science and Engineering, Institute of New Energy Material Chemistry, Renewable Energy Conversion and Storage Center (ReCast), Key Laboratory of Advanced Energy Chemistry (Ministry of Education), Nankai University, Tianjin 300350, P. R. China
| | - Xu Hu
- School of Materials Science and Engineering, Institute of New Energy Material Chemistry, Renewable Energy Conversion and Storage Center (ReCast), Key Laboratory of Advanced Energy Chemistry (Ministry of Education), Nankai University, Tianjin 300350, P. R. China
| | - Zhen Zhou
- School of Chemical Engineering, Zhengzhou University, Zhengzhou 450001, P. R. China
- School of Materials Science and Engineering, Institute of New Energy Material Chemistry, Renewable Energy Conversion and Storage Center (ReCast), Key Laboratory of Advanced Energy Chemistry (Ministry of Education), Nankai University, Tianjin 300350, P. R. China
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Massaro A, Pecoraro A, Hernández S, Talarico G, Muñoz-García AB, Pavone M. Oxygen evolution reaction at the Mo/W-doped bismuth vanadate surface: Assessing the dopant role by DFT calculations. MOLECULAR CATALYSIS 2022. [DOI: 10.1016/j.mcat.2021.112036] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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