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               (2021B1515130002 and 2023A1515011391), and the Major Science and Technology Infrastructure Project of
               Material Genome Big-science Facilities Platform supported by Municipal Development and Reform
               Commission of Shenzhen.


               Conflicts of interest
               Pan, F. is an Associate Editor of the journal Journal of Materials Informatics but was not involved in any
               steps  of  the editorial  process,  including  the  selection  of  reviewers,  manuscript  handling,  or  decision-
               making. The other authors declare that there are no conflicts of interest.

               Ethical approval and consent to participate
               Not applicable.

               Consent for publication
               Not applicable.


               Copyright
               © The Author(s) 2025.


               REFERENCES
               1.       Beck, A.; Paunović, V.; van, B. J. A. Identifying and avoiding dead ends in the characterization of heterogeneous catalysts at the gas-
                   solid interface. Nat. Catal. 2023, 6, 873-84.  DOI
               2.       Li, X.; Mitchell, S.; Fang, Y.; Li, J.; Perez-Ramirez, J.; Lu, J. Advances in heterogeneous single-cluster catalysis. Nat. Rev. Chem.
                   2023, 7, 754-67.  DOI  PubMed
               3.       Bruix, A.; Margraf, J. T.; Andersen, M.; Reuter, K. First-principles-based multiscale modelling of heterogeneous catalysis. Nat. Catal.
                   2019, 2, 659-70.  DOI
               4.       Handoko, A. D.; Wei, F.; Jenndy; Yeo, B. S.; Seh, Z. W. Understanding heterogeneous electrocatalytic carbon dioxide reduction
                   through operando techniques. Nat. Catal. 2018, 1, 922-34.  DOI
               5.       Tran, K.; Ulissi, Z. W. Active learning across intermetallics to guide discovery of electrocatalysts for CO  reduction and H  evolution.
                                                                                       2
                                                                                                  2
                   Nat. Catal. 2018, 1, 696-703.  DOI
               6.       Greeley, J.; Stephens, I. E.; Bondarenko, A. S.; et al. Alloys of platinum and early transition metals as oxygen reduction
                   electrocatalysts. Nat. Chem. 2009, 1, 552-6.  DOI  PubMed
               7.       Wu, Z.; Li, Z.; Li, Y.; Zhang, Y.; Li, J. Improving the DFT computational accuracy for CO activation on Fe surfaces by Bayesian error
                   estimation functional with van der Waals correlation. Comput. Theor. Chem. 2023, 1219, 113968.  DOI
               8.       Araujo, R. B.; Rodrigues, G. L. S.; Dos, S. E. C.; Pettersson, L. G. M. Adsorption energies on transition metal surfaces: towards an
                   accurate and balanced description. Nat. Commun. 2022, 13, 6853.  DOI  PubMed  PMC
               9.       Nørskov, J. K.; Bligaard, T.; Logadottir, A.; et al. Trends in the exchange current for hydrogen evolution. J. Electrochem. Soc. 2005,
                   152, J23.  DOI
               10.      Nørskov, J. K.; Rossmeisl, J.; Logadottir, A.; et al. Origin of the overpotential for oxygen reduction at a fuel-cell cathode. J. Phys.
                   Chem. B. 2004, 108, 17886-92.  DOI  PubMed
               11.      Greeley,  J.;  Jaramillo,  T.  F.;  Bonde,  J.;  Chorkendorff,  I.  B.;  Nørskov,  J.  K.  Computational  high-throughput  screening  of
                   electrocatalytic materials for hydrogen evolution. Nat. Mater. 2006, 5, 909-13.  DOI  PubMed
               12.      Liu, X.; Xiao, J.; Peng, H.; Hong, X.; Chan, K.; Nørskov, J. K. Understanding trends in electrochemical carbon dioxide reduction rates.
                   Nat. Commun. 2017, 8, 15438.  DOI  PubMed  PMC
               13.      Zhan, C.; Dattila, F.; Rettenmaier, C.; et al. Revealing the CO coverage-driven C–C coupling mechanism for electrochemical CO   2
                   reduction on Cu O nanocubes via operando raman spectroscopy. ACS. Catal. 2021, 11, 7694-701.  DOI  PubMed  PMC
                             2
               14.      Cave, E. R.; Shi, C.; Kuhl, K. P.; et al. Trends in the catalytic activity of hydrogen evolution during CO  electroreduction on transition
                                                                                      2
                   metals. ACS. Catal. 2018, 8, 3035-40.  DOI
               15.      Ding, H.; Zheng, S.; Yang, X.; et al. Role of surface hydrogen coverage in C–C coupling process for CO  electroreduction on Ni-based
                                                                                      2
                   catalysts. ACS. Catal. 2024, 14, 14330-8.  DOI
               16.      Zheng, S.; Liang, X.; Pan, J.; Hu, K.; Li, S.; Pan, F. Multi-center cooperativity enables facile C–C coupling in electrochemical CO
                                                                                                         2
                   reduction on a Ni P catalyst. ACS. Catal. 2023, 13, 2847-56.  DOI
                              2
               17.      Zheng, S.; Ding, H.; Yang, X.; Li, S.; Pan, F. Automating discovery of electrochemical urea synthesis reaction paths via active
                   learning and graph theory. https://www.chinesechemsoc.org/doi/full/10.31635/ccschem.024.202404955 (accessed 2024-02-07).
               18.      Ghanekar, P. G.; Deshpande, S.; Greeley, J. Adsorbate chemical environment-based machine learning framework for heterogeneous
                   catalysis. Nat. Commun. 2022, 13, 5788.  DOI  PubMed  PMC
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