REFERENCES
1. Bushuyev, O. S.; De Luna, P.; Dinh, C. T.; et al. What should we make with CO2 and how can we make it? Joule 2018, 2, 825-32.
2. De Luna, P.; Hahn, C.; Higgins, D.; Jaffer, S. A.; Jaramillo, T. F.; Sargent, E. H. What would it take for renewably powered electrosynthesis to displace petrochemical processes? Science 2019, 364, eaav3506.
3. Jouny, M.; Luc, W.; Jiao, F. General techno-economic analysis of CO2 electrolysis systems. Ind. Eng. Chem. Res. 2018, 57, 2165-77.
4. 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.
5. Zhu, P.; Wang, H. High-purity and high-concentration liquid fuels through CO2 electroreduction. Nat. Catal. 2021, 4, 943-51.
6. 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.
7. Zhao, F.; Huang, B.; Zhang, Y.; Wei, T.; Zhang, J.; Zhao, D. A review of CO2 electroreduction to ethanol: C-C coupling mechanistic insights and catalyst design. Nanomicro. Lett. 2026, 18, 313.
8. Li, F.; Li, Y. C.; Wang, Z.; et al. Cooperative CO2-to-ethanol conversion via enriched intermediates at molecule–metal catalyst interfaces. Nat. Catal. 2020, 3, 75-82.
9. Luo, M.; Wang, Z.; Li, Y. C.; et al. Hydroxide promotes carbon dioxide electroreduction to ethanol on copper via tuning of adsorbed hydrogen. Nat. Commun. 2019, 10, 5814.
10. Zhan, C.; Dattila, F.; Rettenmaier, C.; et al. Key intermediates and Cu active sites for CO2 electroreduction to ethylene and ethanol. Nat. Energy. 2024, 9, 1485-96.
11. Liu, X.; Liang, J.; Wang, Z.; Li, Q.; Deng, Y.; Wang, H. Building catalyst exploration highways by integrating high‐throughput and machine learning technologies. Adv. Energy. Mater. 2026, 16, e05497.
12. Levy, O.; Hart, G. L.; Curtarolo, S. Uncovering compounds by synergy of cluster expansion and high-throughput methods. J. Am. Chem. Soc. 2010, 132, 4830-3.
13. 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.
14. Curtarolo, S.; Hart, G. L.; Nardelli, M. B.; Mingo, N.; Sanvito, S.; Levy, O. The high-throughput highway to computational materials design. Nat. Mater. 2013, 12, 191-201.
15. Pan, Y.; Shan, X.; Cai, F.; Gao, H.; Xu, J.; Zhou, M. Accelerating the discovery of oxygen reduction electrocatalysts: high-throughput screening of element combinations in Pt-based high-entropy alloys. Angew. Chem. Int. Ed. Engl. 2024, 63, e202407116.
16. Ivanciuc, O. Applications of support vector machines in chemistry. In Reviews in computational chemistry, Vol 23; Wiley, 2007; pp. 291-400.
17. Machado Cavalcanti, F.; Emilia Kozonoe, C.; André Pacheco, K.; Maria de Brito Alves, R. Application of artificial neural networks to chemical and process engineering. In Deep learning applications. IntechOpen; 2021.
18. Myles, A. J.; Feudale, R. N.; Liu, Y.; Woody, N. A.; Brown, S. D. An introduction to decision tree modeling. J. Chemom. 2004, 18, 275-85.
19. Butler, K. T.; Davies, D. W.; Cartwright, H.; Isayev, O.; Walsh, A. Machine learning for molecular and materials science. Nature 2018, 559, 547-55.
20. Ahneman, D. T.; Estrada, J. G.; Lin, S.; Dreher, S. D.; Doyle, A. G. Predicting reaction performance in C-N cross-coupling using machine learning. Science 2018, 360, 186-90.
21. Reid, J. P.; Sigman, M. S. Holistic prediction of enantioselectivity in asymmetric catalysis. Nature 2019, 571, 343-8.
22. Huang, B.; von Lilienfeld, O. A. Ab initio machine learning in chemical compound space. Chem. Rev. 2021, 121, 10001-36.
23. Han, Z.; Gao, R.; Wang, T.; et al. Machine-learning-assisted design of a binary descriptor to decipher electronic and structural effects on sulfur reduction kinetics. Nat. Catal. 2023, 6, 1073-86.
24. Esterhuizen, J. A.; Goldsmith, B. R.; Linic, S. Interpretable machine learning for knowledge generation in heterogeneous catalysis. Nat. Catal. 2022, 5, 175-84.
25. Song, Z.; Wang, X.; Liu, F.; et al. Distilling universal activity descriptors for perovskite catalysts from multiple data sources via multi-task symbolic regression. Mater. Horiz. 2023, 10, 1651-60.
26. Weng, B.; Song, Z.; Zhu, R.; et al. Simple descriptor derived from symbolic regression accelerating the discovery of new perovskite catalysts. Nat. Commun. 2020, 11, 3513.
27. Zhong, M.; Tran, K.; Min, Y.; et al. Accelerated discovery of CO2 electrocatalysts using active machine learning. Nature 2020, 581, 178-83.
28. Ulissi, Z. W.; Tang, M. T.; Xiao, J.; et al. Machine-learning methods enable exhaustive searches for active bimetallic facets and reveal active site motifs for CO2 reduction. ACS. Catal. 2017, 7, 6600-8.
29. Sun, J.; Tu, R.; Xu, Y.; et al. Machine learning aided design of single-atom alloy catalysts for methane cracking. Nat. Commun. 2024, 15, 6036.
30. Zhang, Q.; Dong, Z.; Liu, X.; et al. Multiparameter machine learning quantifies electronic dominance in Pd-catalyzed formic acid dehydrogenation. Nano. Lett. 2026, 26, 7927-36.
31. Bertheussen, E.; Verdaguer-Casadevall, A.; Ravasio, D.; et al. Acetaldehyde as an intermediate in the electroreduction of carbon monoxide to ethanol on oxide-derived copper. Angew. Chem. Int. Ed. Engl. 2016, 55, 1450-4.
32. Piqué, O.; Low, Q. H.; Handoko, A. D.; Yeo, B. S.; Calle-Vallejo, F. Selectivity map for the late stages of CO and CO2 reduction to C2 species on copper electrodes. Angew. Chem. Int. Ed. Engl. 2021, 60, 10784-90.
33. Vasileff, A.; Zhu, Y.; Zhi, X.; et al. Electrochemical reduction of CO2 to ethane through stabilization of an ethoxy intermediate. Angew. Chem. Int. Ed. Engl. 2020, 59, 19649-53.
34. Goodfellow; IJ; Pouget-Abadie; J; Mirza, M.; et al. Generative adversarial nets. In Proceedings of the 28th International Conference on Neural Information Processing Systems, Montréal, Canada, December 8-13, 2014; Ghahramani, Z.; Welling, M.; Cortes, C.; Lawrence, N. D.; Weinberger, K. Q., Eds.; Curran Associates, Inc.: Red Hook, USA, 2014; Vol. 2, pp 2672-80.
35. Yan, D.; Smith, A. D.; Chen, C. C. Structure prediction and materials design with generative neural networks. Nat. Comput. Sci. 2023, 3, 572-4.
36. Zhao, Y.; Al-Fahdi, M.; Hu, M.; et al. High-throughput discovery of novel cubic crystal materials using deep generative neural networks. Adv. Sci. 2021, 8, e2100566.
37. Yao, Z.; Sánchez-Lengeling, B.; Bobbitt, N. S.; et al. Inverse design of nanoporous crystalline reticular materials with deep generative models. Nat. Mach. Intell. 2021, 3, 76-86.
38. Zeni, C.; Pinsler, R.; Zügner, D.; et al. A generative model for inorganic materials design. Nature 2025, 639, 624-32.
39. Bran, A. M.; Cox, S.; Schilter, O.; Baldassari, C.; White, A. D.; Schwaller, P. Augmenting large language models with chemistry tools. Nat. Mach. Intell. 2024, 6, 525-35.
40. Jablonka, K. M.; Schwaller, P.; Ortega-Guerrero, A.; Smit, B. Leveraging large language models for predictive chemistry. Nat. Mach. Intell. 2024, 6, 161-9.
41. Boiko, D. A.; MacKnight, R.; Kline, B.; Gomes, G. Autonomous chemical research with large language models. Nature 2023, 624, 570-8.
42. Antunes, L. M.; Butler, K. T.; Grau-Crespo, R. Crystal structure generation with autoregressive large language modeling. Nat. Commun. 2024, 15, 10570.
43. Mok, D. H.; Back, S. Generative pretrained transformer for heterogeneous catalysts. J. Am. Chem. Soc. 2024, 146, 33712-22.
44. Song, Z.; Fan, L.; Lu, S.; Ling, C.; Zhou, Q.; Wang, J. Inverse design of promising electrocatalysts for CO2 reduction via generative models and bird swarm algorithm. Nat. Commun. 2025, 16, 1053.
45. Li, R.; Zhang, S.; Tang, Q.; et al. Generative intelligence explores the chemical space of ten million catalysts. Chem. Sci. 2026, 17, 12996-3006.
46. Chanussot, L.; Das, A.; Goyal, S.; et al. Open catalyst 2020 (OC20) dataset and community challenges. ACS. Catal. 2021, 11, 6059-72.
47. Wood, B. M.; Dzamba, M.; Fu, X.; et al. UMA: a family of universal models for atoms. arXiv 2025, arXiv:2506.23971. Available online: https://doi.org/10.48550/arXiv.2506.23971. (accessed 2026-09-23).
48. Kresse, G.; Furthmüller, J. Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set. Comput. Mater. Sci. 1996, 6, 15-50.
49. Kresse, G.; Furthmüller, J. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys. Rev. B. Condens. Matter. 1996, 54, 11169-86.
50. Hammer, B.; Hansen, L. B.; Nørskov, J. K. Improved adsorption energetics within density-functional theory using revised Perdew-Burke-Ernzerhof functionals. Phys. Rev. B. 1999, 59, 7413-21.
51. Kresse, G.; Joubert, D. From ultrasoft pseudopotentials to the projector augmented-wave method. Phys. Rev. B. 1999, 59, 1758-75.
53. Lemmerich, F.; Becker, M. pysubgroup: Easy-to-use subgroup discovery in Python. In Machine learning and knowledge discovery in databases. Cham: Springer International Publishing, 2019; Vol. 11053, pp. 658-62.
54. Shapley, L. S. 17. A VALUE for n-person games. In Contributions to the theory of games (AM-28), Volume II; Princeton University Press, 1953; pp. 307-18.
55. Mcinnes, L.; Healy, J.; Saul, N.; Großberger, L. UMAP: uniform manifold approximation and projection. J. Open. Source. Softw. 2018, 3, 861.
56. Peng, C.; Ma, J.; Luo, G.; et al. (111) Facet-oriented Cu2Mg intermetallic compound with Cu3-Mg sites for CO2 electroreduction to ethanol with industrial current density. Angew. Chem. Int. Ed. Engl. 2024, 63, e202316907.
57. Zhang, L.; Feng, J.; Wu, L.; et al. Oxophilicity-controlled CO2 electroreduction to C2+ alcohols over Lewis acid metal-doped Cuδ+ catalysts. J. Am. Chem. Soc. 2023, 145, 21945-54.
58. Hou, Y.; Li, P.; Wang, Y.; et al. Steering acidic CO2 electroreduction to multicarbon alcohols with high efficiency and selectivity over calcium-induced bicrystalline Cu architecture. ACS. Catal. 2025, 15, 19227-37.
59. Zhu, Y.; Zhu, J.; Li, H.; et al. Confinement effect and hydrogen species modulation toward enhanced electrochemical CO2 reduction to ethanol. Research 2025, 8, 0796.
60. Hoang, T. T. H.; Verma, S.; Ma, S.; et al. Nanoporous copper-silver alloys by additive-controlled electrodeposition for the selective electroreduction of CO2 to ethylene and ethanol. J. Am. Chem. Soc. 2018, 140, 5791-7.
61. Kuang, S.; Su, Y.; Li, M.; et al. Asymmetrical electrohydrogenation of CO2 to ethanol with copper-gold heterojunctions. Proc. Natl. Acad. Sci. U. S. A. 2023, 120, e2214175120.
62. Zhou, X.; Zheng, Z.; Zhang, J.; Ji, J.; Zhang, X. Electronic state modulation of Cu by Ge for post C–C steps in electrochemical CO2 reduction. Adv. Funct. Mater. 2026, 36, e75284.
63. Bae, S.; Yun, G.; Gwon, Y.; Kim, S. Y.; Sohn, Y. Interface engineering of Pt-deposited Cu electrodes via laser ablation for enhanced electrochemical CO2 reduction to multi-carbon products. Adv. Ind. Eng. Chem. 2025, 1, 19.
64. Varandili, S. B.; Stoian, D.; Vavra, J.; et al. Elucidating the structure-dependent selectivity of CuZn towards methane and ethanol in CO2 electroreduction using tailored Cu/ZnO precatalysts. Chem. Sci. 2021, 12, 14484-93.
65. Guo, C.; Guo, Y.; Shi, Y.; et al. Electrocatalytic reduction of CO2 to ethanol at close to theoretical potential via engineering abundant electron-donating Cuδ+ species. Angew. Chem. Int. Ed. Engl. 2022, 61, e202205909.
66. Crandall, B. S.; Qi, Z.; Foucher, A. C.; et al. Cu based dilute alloys for tuning the C2+ selectivity of electrochemical CO2 reduction. Small 2024, 20, e2401656.
67. Li, J.; Ozden, A.; Wan, M.; et al. Silica-copper catalyst interfaces enable carbon-carbon coupling towards ethylene electrosynthesis. Nat. Commun. 2021, 12, 2808.




