REFERENCES

1. Chu, S.; Cui, Y.; Liu, N. The path towards sustainable energy. Nat. Mater. 2016, 16, 16-22.

2. De La Peña, L.; Guo, R.; Cao, X.; Ni, X.; Zhang, W. Accelerating the energy transition to achieve carbon neutrality. Resour. Conserv. Recycl. 2022, 177, 105957.

3. Gao, D.; Li, H.; Wei, P.; Wang, Y.; Wang, G.; Bao, X. Electrochemical synthesis of catalytic materials for energy catalysis. Chin. J. Catal. 2022, 43, 1001-16.

4. Ling, C.; Cui, Y.; Lu, S.; Bai, X.; Wang, J. How computations accelerate electrocatalyst discovery. Chem 2022, 8, 1575-610.

5. Centi, G.; Perathoner, S. Electrocatalysis: prospects and role to enable an E‐chemistry future. Chem. Rec. 2025, 25, e202400259.

6. Seifitokaldani, A.; Gabardo, C. M.; Burdyny, T.; et al. Hydronium-induced switching between CO2 electroreduction pathways. J. Am. Chem. Soc. 2018, 140, 3833-7.

7. Li, Y.; Wang, X.; Wang, Y.; et al. The decisive role of adsorbed OH* in low‐potential CO electro‐oxidation on single‐atom catalytic sites. Carbon. Energy. 2023, 5, e310.

8. He, R.; Wang, S.; Yang, L.; et al. Active site switching on high entropy phosphides as bifunctional oxygen electrocatalysts for rechargeable/robust Zn-air battery. Energy. Environ. Sci. 2024, 17, 7193-208.

9. Wu, H.; Singh-Morgan, A.; Qi, K.; Zeng, Z.; Mougel, V.; Voiry, D. Electrocatalyst microenvironment engineering for enhanced product selectivity in carbon dioxide and nitrogen reduction reactions. ACS. Catal. 2023, 13, 5375-96.

10. Gasteiger, H. A.; Kocha, S. S.; Sompalli, B.; Wagner, F. T. Activity benchmarks and requirements for Pt, Pt-alloy, and non-Pt oxygen reduction catalysts for PEMFCs. Appl. Catal. B. Environ. 2005, 56, 9-35.

11. Kibsgaard, J.; Chorkendorff, I. Considerations for the scaling-up of water splitting catalysts. Nat. Energy. 2019, 4, 430-3.

12. Fernández-Vidal, J.; Koper, M. T. The role of surface science in electrocatalysis. ACS. Catal. 2026, 16, 2925-34.

13. Fabbri, E.; Nachtegaal, M.; Binninger, T.; et al. Dynamic surface self-reconstruction is the key of highly active perovskite nano-electrocatalysts for water splitting. Nat. Mater. 2017, 16, 925-31.

14. Szymanski, N. J.; Rendy, B.; Fei, Y.; et al. An autonomous laboratory for the accelerated synthesis of inorganic materials. Nature 2023, 624, 86-91.

15. Boiko, D. A.; Macknight, R.; Kline, B.; Gomes, G. Autonomous chemical research with large language models. Nature 2023, 624, 570-8.

16. Zuo, X.; Yan, R.; Zhao, L.; et al. A hollow PdCuMoNiCo high-entropy alloy as an efficient bi-functional electrocatalyst for oxygen reduction and formic acid oxidation. J. Mater. Chem. A. 2022, 10, 14857-65.

17. Ma, X.; Liu, H.; Yang, W.; Mao, G.; Zheng, L.; Jiang, H. L. Modulating coordination environment of single-atom catalysts and their proximity to photosensitive units for boosting MOF photocatalysis. J. Am. Chem. Soc. 2021, 143, 12220-9.

18. Hammer, B.; Norskov, J. K. Why gold is the noblest of all the metals. Nature 1995, 376, 238-40.

19. Greiner, M. T.; Jones, T. E.; Beeg, S.; et al. Free-atom-like d states in single-atom alloy catalysts. Nat. Chem. 2018, 10, 1008-15.

20. Wang, S.; Pillai, H. S.; Xin, H. Bayesian learning of chemisorption for bridging the complexity of electronic descriptors. Nat. Commun. 2020, 11, 6132.

21. Saini, S.; Halldin Stenlid, J.; Abild-Pedersen, F. Electronic structure factors and the importance of adsorbate effects in chemisorption on surface alloys. npj. Comput. Mater. 2022, 8, 163.

22. Kumar, A.; Tüysüz, H. Modulating local coordination in single‐atom catalysts: from fundamental concepts to emerging breakthroughs in electrocatalysis. Angew. Chem. Int. Ed. Engl. 2026, 65, e3059499.

23. Chen, C.; Jin, H.; Wang, P.; et al. Local reaction environment in electrocatalysis. Chem. Soc. Rev. 2024, 53, 2022-55.

24. Li, R.; Ma, W.; Liu, Y.; Zhang, L.; Zhou, Z. Towards sustainable electrochemical ammonia synthesis. J. Mater. Chem. A. 2023, 11, 18626-45.

25. Parsa, S. M.; Miraboutalebi, S. M. R.; Shoeibi, S.; et al. Advances on multi-heteroatom doped carbon catalysts: synthesis, mechanistic insight, synergies, and application in microbial electrochemical energy, fuel and chemical production. Carbon 2026, 251, 121315.

26. Yang, J.; Deng, L.; Wu, Y.; Peng, S. Charge state evolution in electrocatalysts for bridging the activity-stability gap in acidic oxygen evolution. Acc. Chem. Res. 2026, 59, 838-48.

27. Ma, W.; Yao, J.; Xie, F.; et al. Optimizing electronic structure through point defect engineering for enhanced electrocatalytic energy conversion. Green. Energy. Environ. 2025, 10, 109-31.

28. Wang, Y.; Huang, H.; Wu, J.; et al. Charge‐polarized selenium vacancy in nickel diselenide enabling efficient and stable electrocatalytic conversion of oxygen to hydrogen peroxide. Adv. Sci. 2023, 10, 2205347.

29. Ling, T.; Yan, D. Y.; Jiao, Y.; et al. Engineering surface atomic structure of single-crystal cobalt (II) oxide nanorods for superior electrocatalysis. Nat. Commun. 2016, 7, 12876.

30. Zhang, Y.; Chen, D.; Yu, S.; Feng, Y.; Zhang, C.; Hu, J. Al‐induced local coordination engineering of Ni: steering the two‐electron oxygen reduction process. Adv. Funct. Mater. 2025, 35, e13626.

31. Gao, X.; Huang, G.; Jing, S.; et al. Strategies for enhancing catalytic performance of single-atom catalysts in 2e- oxygen reduction reaction: a review. The. Innovation. Mater. 2025, 3, 100141.

32. Xue, D.; Zhao, Y.; Cao, J.; Wang, Y.; Li, X.; Ma, T. Spin engineering of dual‐atom site catalysts for efficient electrochemical energy conversion. Adv. Mater. 2025, 37, 2504213.

33. Luo, B. Y.; Li, C.; Yang, Y. M.; Du, W.; Wang, Y. M. Impact of halogen axial coordination on the electronic and magnetic properties of MN4 single-atom catalysts for oxygen reduction. Phys. Chem. Chem. Phys. 2026, 28, 4188-93.

34. Zeng, R.; Li, Y.; Wan, Q.; et al. Orbital coupling of dual‐atom sites boosts electrocatalytic NO oxidation and dynamic intracellular response. Adv. Mater. 2025, 37, 2416371.

35. Cao, L.; Huang, J.; Wu, X.; et al. Active-site stabilized Bi metal-organic framework-based catalyst for highly active and selective electroreduction of CO2 to formate over a wide potential window. Nanoscale 2023, 15, 19522-32.

36. Hao, J.; Wu, K.; Lyu, C.; et al. Recent advances in interface engineering of Fe/Co/Ni-based heterostructure electrocatalysts for water splitting. Mater. Horiz. 2023, 10, 2312-42.

37. Zhao, X.; Liu, M.; Wang, Y.; et al. Designing a built-in electric field for efficient energy electrocatalysis. ACS. Nano. 2022, 16, 19959-79.

38. Wang, K.; Shen, Z.; Qi, F.; Yuan, Y.; Xiao, C.; Pan, H. Recent advances in built‐in electric field for efficient energy electrocatalysis. Carbon. Neutralization. 2025, 4, e70029.

39. Nwaji, N.; Fikadu, B.; Osial, M.; et al. Advanced functional NiCo2S4@CoMo2S4 heterojunction couple as electrode for hydrogen production via energy‐saving urea oxidation. Small 2025, 21, 2410848.

40. Yang, L.; Liu, H.; Li, Y.; et al. Customizing bonding affinity with multi‐intermediates via interfacial electron capture to boost hydrogen evolution in alkaline water electrolysis. Angew. Chem. Int. Ed. Engl. 2024, 64, e202414518.

41. Hammes-Schiffer, S. Proton-coupled electron transfer: moving together and charging forward. J. Am. Chem. Soc. 2015, 137, 8860-71.

42. Zhao, Q.; Martirez, J. M. P.; Carter, E. A. Revisiting understanding of electrochemical CO2 reduction on Cu(111): competing proton-coupled electron transfer reaction mechanisms revealed by embedded correlated wavefunction theory. J. Am. Chem. Soc. 2021, 143, 6152-64.

43. Sundararaman, R.; Goddard, W. A.; Arias, T. A. Grand canonical electronic density-functional theory: algorithms and applications to electrochemistry. J. Chem. Phys. 2017, 146, 114104.

44. Bonnet, N.; Morishita, T.; Sugino, O.; Otani, M. First-principles molecular dynamics at a constant electrode potential. Phys. Rev. Lett. 2012, 109, 266101.

45. Reed, S. K.; Lanning, O. J.; Madden, P. A. Electrochemical interface between an ionic liquid and a model metallic electrode. J. Chem. Phys. 2007, 126, 084704.

46. Sundararaman, R.; Vigil-Fowler, D.; Schwarz, K. Improving the accuracy of atomistic simulations of the electrochemical interface. Chem. Rev. 2022, 122, 10651-74.

47. Ringe, S.; Hörmann, N. G.; Oberhofer, H.; Reuter, K. Implicit solvation methods for catalysis at electrified interfaces. Chem. Rev. 2022, 122, 10777-820.

48. Melander, M. M.; Wu, T.; Weckman, T.; Honkala, K. Constant inner potential DFT for modelling electrochemical systems under constant potential and bias. npj. Comput. Mater. 2024, 10, 5.

49. Zhang, X.; Chen, J.; Wang, H.; et al. Dynamic structural evolution of single-atom catalysts at the catalyst-electrolyte interface: insights from electrochemical coupled field. Nano. Lett. 2025, 25, 6332-9.

50. Cheng, D.; Wei, Z.; Zhang, Z.; Broekmann, P.; Alexandrova, A. N.; Sautet, P. Restructuring and activation of Cu(111) under electrocatalytic reduction conditions. Angew. Chem. Int. Ed. Engl. 2023, 62, e202218575.

51. Hörmann, N. G.; Beinlich, S. D.; Reuter, K. Converging divergent paths: constant charge vs constant potential energetics in computational electrochemistry. J. Phys. Chem. C. 2024, 128, 5524-31.

52. Yang, W.; Jia, Z.; Zhou, B.; Wei, L.; Gao, Z.; Li, H. Surface states of dual-atom catalysts should be considered for analysis of electrocatalytic activity. Commun. Chem. 2023, 6, 6.

53. Li, Y.; Lu, L.; Hu, K.; et al. Scalable and customizable single‐atom coatings for pH‐universal H2O2 electrosynthesis. Adv. Mater. 2026, 38, e21237.

54. Mathew, K.; Kolluru, V. S. C.; Mula, S.; Steinmann, S. N.; Hennig, R. G. Implicit self-consistent electrolyte model in plane-wave density-functional theory. J. Chem. Phys. 2019, 151, 234101.

55. Liu, T.; Wang, Y.; Li, Y. Two‐dimensional organometallic frameworks with pyridinic single‐metal‐atom sites for bifunctional ORR/OER. Adv. Funct. Mater. 2022, 32, 2207110.

56. Zhao, X.; Liu, Y. Origin of selective production of hydrogen peroxide by electrochemical oxygen reduction. J. Am. Chem. Soc. 2021, 143, 9423-8.

57. Yu, S.; Levell, Z.; Jiang, Z.; Zhao, X.; Liu, Y. What is the rate-limiting step of oxygen reduction reaction on Fe-N-C catalysts? J. Am. Chem. Soc. 2023, 145, 25352-6.

58. Yue, J.; Li, Y.; Yang, C.; Luo, W. Hydroxyl‐binding induced hydrogen bond network connectivity on Ru‐based catalysts for efficient alkaline hydrogen oxidation electrocatalysis. Angew. Chem. Int. Ed. Engl. 2025, 64, e202415447.

59. Wu, J.; Zhang, J.; Chen, M.; Yan, J.; Mao, B.; Feng, G. Regulating the electrical double layer to prevent water electrolysis for wet ionic liquids with cheap salts. Nanoscale 2023, 15, 18603-12.

60. Chen, L.; Rosen, J.; Björk, J. Tuned interfacial hydrogen bonds for enhanced H2 electrocatalysis kinetics on Ti2C MXenes. Commun. Chem. 2026, 9, 246.

61. Qin, Y.; Zhao, W.; Xia, C.; et al. CO Intermediate‐assisted dynamic Cu sintering during electrocatalytic CO2 reduction on Cu-N-C catalysts. Angew. Chem. Int. Ed. Engl. 2024, 63, e202404763.

62. Xue, Y.; Chen, L.; Zhang, L.; Zheng, G.; Zhang, X.; Zhou, Z. Activity and stability origin of core-shell catalysts: unignorable atomic diffusion behavior. Chem. Sci. 2025, 16, 3323-8.

63. Hasa, B.; Zhao, Y.; Jiao, F. In situ/operando characterization techniques of electrochemical CO2 reduction. Annu. Rev. Chem. Biomol. Eng. 2023, 14, 165-85.

64. Shen, W.; Ye, Y.; Xia, Q.; Xi, P. Progress in in situ characterization of electrocatalysis. EES. Catal. 2025, 3, 10-31.

65. Olajide, G.; Maxson, T.; Szilvási, T. Temporal resolution of in situ TEM limits the characterization of catalyst site dynamics. ACS. Catal. 2025, 15, 18908-16.

66. Schwarz, K.; Sundararaman, R. The electrochemical interface in first-principles calculations. Surf. Sci. Rep. 2020, 75, 100492.

67. Wu, H.; Chen, M.; Cheng, H.; Yang, T.; Zeng, M.; Yang, M. Interpretable physics-informed machine learning approaches to accelerate electrocatalyst development. J. Mater. Inf. 2025, 5, 15.

68. Xu, L.; Zhou, J.; Bandarenka, A. S.; Chen, Z. Data-driven electrocatalyst discovery: recent trends in machine learning approaches and descriptor-based design principles. Acc. Mater. Res. 2026, 7, 294-308.

69. 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.

70. Xia, Z.; Xiao, H. Grand canonical ensemble modeling of electrochemical interfaces made simple. J. Chem. Theory. Comput. 2023, 19, 5168-75.

71. Groß, A. Challenges for ab initio molecular dynamics simulations of electrochemical interfaces. Curr. Opin. Electrochem. 2023, 40, 101345.

72. Yu, L.; Chen, X.; Yao, N.; Gao, Y.; Zhang, Q. Constant-potential molecular dynamics simulation and its application in rechargeable batteries. J. Mater. Chem. A. 2023, 11, 11078-88.

73. Nair, A. S.; Foppa, L.; Scheffler, M. Materials-discovery workflow guided by symbolic regression for identifying acid-stable oxides for electrocatalysis. npj. Comput. Mater. 2025, 11, 150.

74. Ouyang, R.; Curtarolo, S.; Ahmetcik, E.; Scheffler, M.; Ghiringhelli, L. M. SISSO: a compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates. Phys. Rev. Mater. 2018, 2, 083802.

75. Kocer, E.; Ko, T. W.; Behler, J. Neural network potentials: a concise overview of methods. Annu. Rev. Phys. Chem. 2022, 73, 163-86.

76. Batzner, S.; Musaelian, A.; Sun, L.; et al. E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials. Nat. Commun. 2022, 13, 2453.

77. Kulichenko, M.; Nebgen, B.; Lubbers, N.; et al. Data Generation for machine learning interatomic potentials and beyond. Chem. Rev. 2024, 124, 13681-714.

78. Choi, J.; Lee, B. Accelerating materials language processing with large language models. Commun. Mater. 2024, 5, 13.

79. Ramos, M. C.; Collison, C. J.; White, A. D. A review of large language models and autonomous agents in chemistry. Chem. Sci. 2025, 16, 2514-72.

80. Mirza, A.; Alampara, N.; Kunchapu, S.; et al. A framework for evaluating the chemical knowledge and reasoning abilities of large language models against the expertise of chemists. Nat. Chem. 2025, 17, 1027-34.

81. 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.

82. Wang, S. H.; Pillai, H. S.; Wang, S.; Achenie, L. E. K.; Xin, H. Infusing theory into deep learning for interpretable reactivity prediction. Nat. Commun. 2021, 12, 5288.

83. Omidvar, N.; Wang, S. H.; Huang, Y.; et al. Explainable AI for optimizing oxygen reduction on Pt monolayer core-shell catalysts. Electrochem. Sci. Adv. 2024, 4, e202300028.

84. Lunger, J. R.; Karaguesian, J.; Chun, H.; et al. Towards atom-level understanding of metal oxide catalysts for the oxygen evolution reaction with machine learning. npj. Comput. Mater. 2024, 10, 80.

85. Wellawatte, G. P.; Schwaller, P. Human interpretable structure-property relationships in chemistry using explainable machine learning and large language models. Commun. Chem. 2025, 8, 11.

86. Li, Q.; Ouyang, Y.; Lu, S.; et al. Perspective on theoretical methods and modeling relating to electro-catalysis processes. Chem. Commun. 2020, 56, 9937-49.

87. Bonati, L.; Polino, D.; Pizzolitto, C.; et al. The role of dynamics in heterogeneous catalysis: Surface diffusivity and N2 decomposition on Fe(111). Proc. Natl. Acad. Sci. U. S. A. 2023, 120, e2313023120.

88. Chen, X.; El Khatib, M.; Lindgren, P.; Willard, A.; Medford, A. J.; Peterson, A. A. Atomistic learning in the electronically grand-canonical ensemble. npj. Comput. Mater. 2023, 9, 73.

89. Chen, L.; Tian, Y.; Hu, X.; et al. Constant-potential MD with neural network potentials reveals cation effects on CO2 reduction at Au-water interfaces. JACS. Au. 2025, 6, 868-80.

90. Dufils, T.; Knijff, L.; Shao, Y.; Zhang, C. PiNNwall: heterogeneous electrode models from integrating machine learning and atomistic simulation. J. Chem. Theory. Comput. 2023, 19, 5199-209.

91. Bergmann, N.; Bonnet, N.; Marzari, N.; Reuter, K.; Hörmann, N. G. Machine learning the energetics of electrified solid-liquid interfaces. Phys. Rev. Lett. 2025, 135, 146201.

92. Hu, X.; Chen, L.; Wang, H.; Zhang, X.; Zhou, Z. Accelerating discovery in computational electrocatalysis: From machine learning potentials to AI-driven workflows. Curr. Opin. Electrochem. 2026, 57, 101848.

93. Hu, X.; Chen, S.; Chen, L.; Wang, H.; Zhang, X.; Zhou, Z. Automating structure-activity analysis for electrochemical nitrogen reduction catalyst design through multi-agent collaborations. Natl. Sci. Rev. 2025, 12, nwaf372.

94. Wang, X.; Li, Z.; Zhang, D.; Li, H.; Xu, H.; Cheng, D. Catalysis AI agent guides discovering the universal design principle of Cu‐based single‐atom alloy catalysts for CO2 electroreduction. Angew. Chem. Int. Ed. Engl. 2026, 65, e24612.

95. Lin, H.; Cui, X.; Dai, B.; et al. Reasoning language model as rule finder: a case study on C-H bond activation using 2D metal-organic frameworks. ACS. Cent. Sci. 2025, 11, 1135-46.

96. Zhao, P.; Zhang, Y.; Chen, B.; et al. AI-agent-guided design of dual-scale modulated nickel-based catalyst with built-in electric field for enhanced biomass electrooxidation. ACS. Nano. 2026, 20, 10640-50.

97. Steinmann, S. N.; Hermawan, A.; Bin Jassar, M.; Seh, Z. W. Autonomous high-throughput computations in catalysis. Chem. Catalysis. 2022, 2, 940-56.

98. Fisker Bødker, N.; Moretti, E. R.; Chang, J. H.; Vegge, T. FastCat: autonomous discovery of multielement layered double hydroxide alloy catalysts for alkaline oxygen evolution reaction. Adv. Intell. Discov. 2025, 2, e202500138.

99. Yang, R.; Zhou, D.; Jia, Z.; et al. Toward practical design of high‐entropy catalysts for chlorine evolution reaction via pareto‐guided multi‐objective bayesian optimization enabled by a robotic AI‐chemist. Angew. Chem. Int. Ed. 2026, 65, e8794274.

100. Tan, Y.; Chen, L.; Shan, X.; et al. Development of AI-eChemist Laboratory. AI. Agent. 2026, 2, 17.