REFERENCES
1. Chang, B.; Pang, H.; Raziq, F.; et al. Electrochemical reduction of carbon dioxide to multicarbon (C2+) products: challenges and perspectives. Energy. Environ. Sci. 2023, 16, 4714-58.
2. Sun, H.; Liu, J. Advancing CO2RR with O-coordinated single-atom nanozymes: a DFT and machine learning exploration. ACS. Catal. 2024, 14, 14021-30.
3. Zhang, Z.; Wang, T.; Cai, Y.; et al. Probing electrolyte effects on cation-enhanced CO2 reduction on copper in acidic media. Nat. Catal. 2024, 7, 807-17.
4. Zhu, Q.; Gu, Y.; Liang, X.; Wang, X.; Ma, J. A machine learning model to predict CO2 reduction reactivity and products transferred from metal-zeolites. ACS. Catal. 2022, 12, 12336-48.
5. Wang, X.; Ye, S.; Hu, W.; et al. Electric dipole descriptor for machine learning prediction of catalyst surface-molecular adsorbate interactions. J. Am. Chem. Soc. 2020, 142, 7737-43.
7. Butler, K. T.; Davies, D. W.; Cartwright, H.; Isayev, O.; Walsh, A. Machine learning for molecular and materials science. Nature 2018, 559, 547-55.
8. Schmidt, J.; Marques, M. R. G.; Botti, S.; Marques, M. A. L. Recent advances and applications of machine learning in solid-state materials science. npj. Comput. Mater. 2019, 5, 83.
9. Back, S.; Yoon, J.; Tian, N.; Zhong, W.; Tran, K.; Ulissi, Z. W. Convolutional neural network of atomic surface structures to predict binding energies for high-throughput screening of catalysts. J. Phys. Chem. Lett. 2019, 10, 4401-8.
10. Zafari, M.; Kumar, D.; Umer, M.; Kim, K. S. Machine learning-based high throughput screening for nitrogen fixation on boron-doped single atom catalysts. J. Mater. Chem. A. 2020, 8, 5209-16.
11. Chen, A.; Zhang, X.; Chen, L.; Yao, S.; Zhou, Z. A machine learning model on simple features for CO2 reduction electrocatalysts. J. Phys. Chem. C. 2020, 124, 22471-8.
12. Del Rio, B. G.; Phan, B.; Ramprasad, R. A deep learning framework to emulate density functional theory. npj. Comput. Mater. 2023, 9, 158.
13. Sun, Z.; Yin, H.; Liu, K.; et al. Machine learning accelerated calculation and design of electrocatalysts for CO2 reduction. SmartMat 2022, 3, 68-83.
14. Bozal-Ginesta, C.; Pablo-García, S.; Choi, C.; Tarancón, A.; Aspuru-Guzik, A. Developing machine learning for heterogeneous catalysis with experimental and computational data. Nat. Rev. Chem. 2025, 9, 601-16.
15. Goldsmith, B. R.; Esterhuizen, J.; Liu, J. X.; Bartel, C. J.; Sutton, C. Machine learning for heterogeneous catalyst design and discovery. AIChE. J. 2018, 64, 2311-23.
16. Mok, D. H.; Li, H.; Zhang, G.; Lee, C.; Jiang, K.; Back, S. Data-driven discovery of electrocatalysts for CO2 reduction using active motifs-based machine learning. Nat. Commun. 2023, 14, 7303.
17. Ren, M.; Guo, X.; Zhang, S.; Huang, S. Design of graphdiyne and holey graphyne-based single atom catalysts for CO2 reduction with interpretable machine learning. Adv. Funct. Mater. 2023, 33, 2213543.
18. Wang, Y.; Wu, Z.; Jiang, Y.; et al. Bridging theory and experiment: machine learning potential-driven insights into pH‐Dependent CO2 Reduction on Sn-based catalysts. Adv. Funct. Mater. 2025, 35, e06314.
19. Zhang, Y.; He, J.; Lai, Z.; Ling, C.; Li, Q.; Wang, J. Machine learning-accelerated kinetic simulations of surface reactions with complex coverage effects. J. Phys. Chem. Lett. 2026, 17, 3307-15.
20. Zhu, J.; Cheng, J. How can machine learning facilitate computational electrochemistry. APL. Comput. Phys. 2026, 2, 020901.
21. Feng, C.; Jiang, B. Machine learning accelerated finite-field simulations for electrochemical interfaces. JACS. Au. 2025, 5, 5939-47.
22. Wang, L.; Zhou, X.; Luo, Z.; et al. Review of external field effects on electrocatalysis: machine learning guided design. Adv. Funct. Mater. 2024, 34, 2408870.
23. Shi, Y.; Kang, P.; Shang, C.; Liu, Z. Methanol synthesis from CO2/CO mixture on Cu-Zn catalysts from microkinetics-guided machine learning pathway search. J. Am. Chem. Soc. 2022, 144, 13401-14.
24. Tran, K.; Ulissi, Z. W. Active learning across intermetallics to guide discovery of electrocatalysts for CO2 reduction and H2 evolution. Nat. Catal. 2018, 1, 696-703.
25. Zhong, M.; Tran, K.; Min, Y.; et al. Accelerated discovery of CO2 electrocatalysts using active machine learning. Nature 2020, 581, 178-83.
26. Nitopi, S.; Bertheussen, E.; Scott, S. B.; et al. Progress and perspectives of electrochemical CO2 reduction on copper in aqueous electrolyte. Chem. Rev. 2019, 119, 7610-72.
27. Unke, O. T.; Chmiela, S.; Sauceda, H. E.; et al. Machine learning force fields. Chem. Rev. 2021, 121, 10142-86.
28. Behler, J.; Parrinello, M. Generalized neural-network representation of high-dimensional potential-energy surfaces. Phys. Rev. Lett. 2007, 98, 146401.
29. Clark, E. L.; Resasco, J.; Landers, A.; et al. Standards and protocols for data acquisition and reporting for studies of the electrochemical reduction of carbon dioxide. ACS. Catal. 2018, 8, 6560-70.
30. Burdyny, T.; Smith, W. A. CO2 reduction on gas-diffusion electrodes and why catalytic performance must be assessed at commercially-relevant conditions. Energy. Environ. Sci. 2019, 12, 1442-53.
31. Raccuglia, P.; Elbert, K. C.; Adler, P. D. F.; et al. Machine-learning-assisted materials discovery using failed experiments. Nature 2016, 533, 73-6.


