ACS Publications. Most Trusted. Most Cited. Most Read
Rational Designing of Bimetallic/Trimetallic Hydrogen Evolution Reaction Catalysts Using Supervised Machine Learning

OR SEARCH CITATIONS

My Activity
Publications

Figure 1Loading Img
Download Hi-Res ImageDownload to MS-PowerPointCite This:J. Phys. Chem. Lett. 2022, 13, 32, 7583-7593
    Physical Insights into Chemistry, Catalysis, and Interfaces

    Rational Designing of Bimetallic/Trimetallic Hydrogen Evolution Reaction Catalysts Using Supervised Machine Learning
    Click to copy article linkArticle link copied!

    Other Access OptionsSupporting Information (2)

    The Journal of Physical Chemistry Letters

    Cite this: J. Phys. Chem. Lett. 2022, 13, 32, 7583–7593
    Click to copy citationCitation copied!
    https://doi.org/10.1021/acs.jpclett.2c01401
    Published August 11, 2022
    Copyright © 2022 American Chemical Society
    Request reuse permissions

    Abstract

    Click to copy section linkSection link copied!
    Abstract Image

    Cost-efficient electrocatalysts to replace precious platinum group metals- (PGMs-) based catalysts for the hydrogen evolution reaction (HER) carry significant potential for sustainable energy solutions. Machine learning (ML) methods have provided new avenues for intelligent screening and predicting efficient heterogeneous catalysts in recent years. We coalesce density functional theory (DFT) and supervised ML methods to discover earth-abundant active heterogeneous NiCoCu-based HER catalysts. An intuitive generalized microstructure model was designed to study the adsorbate’s surface coverage and generate input features for the ML process. The study utilizes optimized eXtreme Gradient Boost Regression (XGBR) models to screen NiCoCu alloy-based catalysts for HER. We show that the most active HER catalysts can be screened from an extensive set of catalysts with this approach. Therefore, our approach can provide an efficient way to discover novel heterogeneous catalysts for various electrochemical reactions.

    Copyright © 2022 American Chemical Society

    Subjects

    what are subjects

    Read this article

    To access this article, please review the available access options below.

    Get instant access

    Purchase Access

    Read this article for 48 hours. Check out below using your ACS ID or as a guest.

    Recommended

    Access through Your Institution

    You may have access to this article through your institution.

    Your institution does not have access to this content. Add or change your institution or let them know you’d like them to include access.

    Supporting Information

    Click to copy section linkSection link copied!

    The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jpclett.2c01401.

    • Lattice parameters, microstructure combinations formula and calculations, feature description from the microstructure model, DFT calculated results of 63 selected catalysts, hyperparameter tuning of considered ML models, performance analysis of best ML models, DFT calculated results of reference catalyst, Pt(111), selected 27 catalysts after screening, formation energy analysis, free energy analysis, and coverage concentration analysis (PDF)

    • Transparent Peer Review report available (PDF)

    Terms & Conditions

    Electronic Supporting Information files are available without a subscription to ACS Web Editions. The American Chemical Society holds a copyright ownership interest in any copyrightable Supporting Information. Files available from the ACS website may be downloaded for personal use only. Users are not otherwise permitted to reproduce, republish, redistribute, or sell any Supporting Information from the ACS website, either in whole or in part, in either machine-readable form or any other form without permission from the American Chemical Society. For permission to reproduce, republish and redistribute this material, requesters must process their own requests via the RightsLink permission system. Information about how to use the RightsLink permission system can be found at http://pubs.acs.org/page/copyright/permissions.html.

    Cited By

    Click to copy section linkSection link copied!

    This article is cited by 29 publications.

    1. Rahul Kumar Sharma, Milan Kumar Jena, Harpriya Minhas, Biswarup Pathak. Machine-Learning-Assisted Screening of Nanocluster Electrocatalysts: Mapping and Reshaping the Activity Volcano for the Oxygen Reduction Reaction. ACS Applied Materials & Interfaces 2024, 16 (46) , 63589-63601. https://doi.org/10.1021/acsami.4c14076
    2. Nana Zhou, Yaling Zhao, Qingzhang Lv, Yahong Chen. Using Machine Learning to Forecast the Conductive Substrate-Supported Heteroatom-Doped Metal Compound Electrocatalysts for Hydrogen Evolution Reaction. The Journal of Physical Chemistry C 2024, 128 (41) , 17274-17281. https://doi.org/10.1021/acs.jpcc.4c03846
    3. Hao Sun, Jing-yao Liu. Advancing CO2RR with O-Coordinated Single-Atom Nanozymes: A DFT and Machine Learning Exploration. ACS Catalysis 2024, 14 (18) , 14021-14030. https://doi.org/10.1021/acscatal.4c02799
    4. Qingxin Wang, Xinrui Liu, Siying Tao, Haining Wang, Shanfu Lu, Yan Xiang, Jing Zhang. Machine Learning Study on Microwave-Assisted Batch Preparation and Oxygen Reduction Performance of Fe–N–C Catalysts. The Journal of Physical Chemistry Letters 2023, 14 (40) , 9082-9089. https://doi.org/10.1021/acs.jpclett.3c02308
    5. Timothy T. Yang, Wissam A. Saidi. Simple Approach for Reconciling Cyclic Voltammetry with Hydrogen Adsorption Energy for Hydrogen Evolution Exchange Current. The Journal of Physical Chemistry Letters 2023, 14 (18) , 4164-4171. https://doi.org/10.1021/acs.jpclett.3c00534
    6. Allan Abraham B. Padama, Marianne A. Palmero, Koji Shimizu, Tongjai Chookajorn, Satoshi Watanabe. Machine learning and density functional theory-based analysis of the surface reactivity of high entropy alloys: The case of H atom adsorption on CoCuFeMnNi. Computational Materials Science 2025, 247 , 113480. https://doi.org/10.1016/j.commatsci.2024.113480
    7. Yunzhe Zheng, Si Sun, Jiali Liu, Qingyu Zhao, Heng Zhang, Jing Zhang, Peng Zhou, Zhaokun Xiong, Chuan-Shu He, Bo Lai. Application of machine learning for material prediction and design in the environmental remediation. Chinese Chemical Letters 2024, 361 , 110722. https://doi.org/10.1016/j.cclet.2024.110722
    8. Mohd Nur Ikhmal Salehmin, Sieh Kiong Tiong, Hassan Mohamed, Dallatu Abbas Umar, Kai Ling Yu, Hwai Chyuan Ong, Saifuddin Nomanbhay, Swee Su Lim. Navigating challenges and opportunities of machine learning in hydrogen catalysis and production processes: Beyond algorithm development. Journal of Energy Chemistry 2024, 99 , 223-252. https://doi.org/10.1016/j.jechem.2024.07.045
    9. Rui Ding, Junhong Chen, Yuxin Chen, Jianguo Liu, Yoshio Bando, Xuebin Wang. Unlocking the potential: machine learning applications in electrocatalyst design for electrochemical hydrogen energy transformation. Chemical Society Reviews 2024, 53 (23) , 11390-11461. https://doi.org/10.1039/D4CS00844H
    10. Anastasiia P. Dmitrieva, Anna S. Fomkina, Chantal T. Tracey, Elizaveta A. Romanenko, Ali Ayati, Pavel V. Krivoshapkin, Elena F. Krivoshapkina. AI and ML for selecting viable electrocatalysts: progress and perspectives. Journal of Materials Chemistry A 2024, 12 (45) , 31074-31102. https://doi.org/10.1039/D4TA04991H
    11. Rahul Golder, Shraman Pal, Sathish Kumar C., Koustuv Ray. Machine learning-enhanced optimal catalyst selection for water-gas shift reaction. Digital Chemical Engineering 2024, 12 , 100165. https://doi.org/10.1016/j.dche.2024.100165
    12. Xinyu Fan, Letian Chen, Dulin Huang, Yun Tian, Xu Zhang, Menggai Jiao, Zhen Zhou. From Single Metals to High‐Entropy Alloys: How Machine Learning Accelerates the Development of Metal Electrocatalysts. Advanced Functional Materials 2024, 34 (34) https://doi.org/10.1002/adfm.202401887
    13. Xinyan Liu, Hong-Jie Peng. Toward Next-Generation Heterogeneous Catalysts: Empowering Surface Reactivity Prediction with Machine Learning. Engineering 2024, 39 , 25-44. https://doi.org/10.1016/j.eng.2023.07.021
    14. Yuefei Zhang, Xuefei Liu, Wentao Wang. Theoretical Calculation Assisted by Machine Learning Accelerate Optimal Electrocatalyst Finding for Hydrogen Evolution Reaction. ChemElectroChem 2024, 11 (13) https://doi.org/10.1002/celc.202400084
    15. Carmen Martínez-Alonso, Valentin Vassilev-Galindo, Benjamin M. Comer, Frank Abild-Pedersen, Kirsten T. Winther, Javier LLorca. Application of machine learning to discover new intermetallic catalysts for the hydrogen evolution and the oxygen reduction reactions. Catalysis Science & Technology 2024, 14 (13) , 3784-3799. https://doi.org/10.1039/D4CY00491D
    16. Meena Rittiruam, Pisit Khamloet, Annop Ektarawong, Chayanon Atthapak, Tinnakorn Saelee, Patcharaporn Khajondetchairit, Björn Alling, Supareak Praserthdam, Piyasan Praserthdam. Screening of Cu-Mn-Ni-Zn high-entropy alloy catalysts for CO2 reduction reaction by machine-learning-accelerated density functional theory. Applied Surface Science 2024, 652 , 159297. https://doi.org/10.1016/j.apsusc.2024.159297
    17. Muhammad Shoaib, Fen Qiao, Xiangchao Xu, Taihang Zhou, Yanzhen Liu. Influence of Mo concentration on the structural and electrochemical properties of double-doped Mo–Co–Ni 3 S 2 /NF composites. CrystEngComm 2024, 26 (13) , 1884-1891. https://doi.org/10.1039/D3CE01246H
    18. Lin-Ping Wang, Kuang Li, Hua-Long Ding, Le Xu, Chen Huang, Jiao-Jiao Zhou, Cong-Tao Wen, Pei-Lin Zhang, Wei-Wei Wang, Lu-Yang Chen. Honeycomb-like MoCo alloy on 3D nitrogen-doped porous graphene for efficient hydrogen evolution reaction. Rare Metals 2024, 43 (3) , 1072-1082. https://doi.org/10.1007/s12598-023-02500-z
    19. Hongyu Liu, Kangyu Liu, Hairuo Zhu, Weiqing Guo, Yuming Li. Explainable machine-learning predictions for catalysts in CO 2 -assisted propane oxidative dehydrogenation. RSC Advances 2024, 14 (11) , 7276-7282. https://doi.org/10.1039/D4RA00406J
    20. Diptendu Roy, Shyama Charan Mandal, Amitabha Das, Biswarup Pathak. Unravelling CO 2 Reduction Reaction Intermediates on High Entropy Alloy Catalysts: An Interpretable Machine Learning Approach to Establish Scaling Relations. Chemistry – A European Journal 2024, 30 (6) https://doi.org/10.1002/chem.202302679
    21. Aarti Devi, Harpriya Minhas, Lipipuspa Sahoo, Rashi, Saniya Gratious, Amitabha Das, Sukhendu Mandal, Biswarup Pathak, Amitava Patra. Insights of the efficient hydrogen evolution reaction performance in bimetallic Au 4 Cu 2 nanoclusters. Nanoscale 2024, 16 (4) , 1758-1769. https://doi.org/10.1039/D3NR05445D
    22. Jin Li, Naiteng Wu, Jian Zhang, Hong-Hui Wu, Kunming Pan, Yingxue Wang, Guilong Liu, Xianming Liu, Zhenpeng Yao, Qiaobao Zhang. Machine Learning-Assisted Low-Dimensional Electrocatalysts Design for Hydrogen Evolution Reaction. Nano-Micro Letters 2023, 15 (1) https://doi.org/10.1007/s40820-023-01192-5
    23. Shuyi Cao, Yuhong Luo, Tianhang Li, Jingde Li, Lanlan Wu, Guihua Liu. Machine learning assisted screening of doped metals phosphides electrocatalyst towards efficient hydrogen evolution reaction. Molecular Catalysis 2023, 551 , 113625. https://doi.org/10.1016/j.mcat.2023.113625
    24. Shiny Golda A, Ajith P. Varghese, Navid Rabiee, Bernaurdshaw Neppolian, Sandeep Kumar Lakhera. Synergistic hydrogen generation through a 2D-2D NiCuInS2:In2S3/g-C3N4 dual S-scheme heterojunction nanosheets. Carbon 2023, 215 , 118441. https://doi.org/10.1016/j.carbon.2023.118441
    25. Souvik Manna, Surya Sekhar Manna, Sandeep Das, Biswarup Pathak. Metal-solvent interaction contribution on voltage for metal ion battery: An interpretable machine learning approach. Electrochimica Acta 2023, 467 , 143148. https://doi.org/10.1016/j.electacta.2023.143148
    26. Carina Yi Jing Lim, Riko I Made, Zi Hui Jonathan Khoo, Chee Koon Ng, Yang Bai, Jianbiao Wang, Gaoliang Yang, Albertus D. Handoko, Yee-Fun Lim. Machine learning-assisted optimization of multi-metal hydroxide electrocatalysts for overall water splitting. Materials Horizons 2023, 10 (11) , 5022-5031. https://doi.org/10.1039/D3MH00788J
    27. Jingzi Zhang, Yuelin Wang, Xuyan Zhou, Chengquan Zhong, Ke Zhang, Jiakai Liu, Kailong Hu, Xi Lin. Accurate and efficient machine learning models for predicting hydrogen evolution reaction catalysts based on structural and electronic feature engineering in alloys. Nanoscale 2023, 15 (26) , 11072-11082. https://doi.org/10.1039/D3NR01442H
    28. Jie Feng, Yujin Ji, Youyong Li. In silico design of copper-based alloys for ammonia synthesis from nitric oxide reduction accelerated by machine learning. Journal of Materials Chemistry A 2023, 11 (26) , 14195-14203. https://doi.org/10.1039/D3TA01883K
    29. Meena Rittiruam, Sorawee Setasuban, Jakapob Noppakhun, Tinnakorn Saelee, Annop Ektarawong, Nuttanon Aumnongpho, Suphawich Boonchuay, Patcharaporn Khajondetchairit, Supareak Praserthdam, Björn Alling, Piyasan Praserthdam. First‐Principles Density Functional Theory and Machine Learning Technique for the Prediction of Water Adsorption Site on PtPd‐Based High‐Entropy‐Alloy Catalysts. Advanced Theory and Simulations 2023, 6 (4) https://doi.org/10.1002/adts.202200926
    Download PDF
    Go to The Journal of Physical Chemistry Letters
    Get e-Alerts
    Get e-Alerts

    The Journal of Physical Chemistry Letters

    Cite this: J. Phys. Chem. Lett. 2022, 13, 32, 7583–7593
    Click to copy citationCitation copied!
    https://doi.org/10.1021/acs.jpclett.2c01401
    Published August 11, 2022
    Copyright © 2022 American Chemical Society
    Request reuse permissions

    Article Views

    3047

    Altmetric

    -

    Citations

    29
    Learn about these metrics

    Article Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.

    Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.

    The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated.