REFERENCES

1. Li, H.; Kelly, S.; Guevarra, D.; et al. Analysis of the limitations in the oxygen reduction activity of transition metal oxide surfaces. Nat. Catal. 2021, 4, 463-8.

2. Zhu, Y.; Tang, Z.; Yuan, L.; Li, B.; Shao, Z.; Guo, W. Beyond conventional structures: emerging complex metal oxides for efficient oxygen and hydrogen electrocatalysis. Chem. Soc. Rev. 2025, 54, 1027-92.

3. Liu, H.; Sun, X.; Gao, F.; Zheng, Y.; Qiao, S. Cost-efficient and stable electrolysis of reverse osmosis water using a Co-RuO2-enabled PEM electrolyser. Nat. Catal. 2026, 9, 9-17.

4. Qin, Y.; Yu, T.; Deng, S.; et al. RuO2 electronic structure and lattice strain dual engineering for enhanced acidic oxygen evolution reaction performance. Nat. Commun. 2022, 13, 3784.

5. Song, F.; Bai, L.; Moysiadou, A.; et al. Transition metal oxides as electrocatalysts for the oxygen evolution reaction in alkaline solutions: an application-inspired renaissance. J. Am. Chem. Soc. 2018, 140, 7748-59.

6. Jain, A.; Ong, S. P.; Hautier, G.; et al. Commentary: The Materials Project: a materials genome approach to accelerating materials innovation. APL. Mater. 2013, 1, 011002.

7. Curtarolo, S.; Setyawan, W.; Hart, G. L.; et al. AFLOW: an automatic framework for high-throughput materials discovery. Comput. Mater. Sci. 2012, 58, 218-26.

8. Zagorac, D.; Müller, H.; Ruehl, S.; Zagorac, J.; Rehme, S. Recent developments in the Inorganic Crystal Structure Database: theoretical crystal structure data and related features. J. Appl. Crystallogr. 2019, 52, 918-25.

9. Zhang, D.; Li, H. Digital Catalysis Platform (DigCat): a gateway to big data and AI-powered innovations in catalysis. ChemRxiv 2024. Available online: https://doi.org/10.26434/chemrxiv-2024-9lpb9 (accessed 23 March 2026).

10. Ong, S. P.; Cholia, S.; Jain, A.; et al. The Materials Application Programming Interface (API): a simple, flexible and efficient API for materials data based on REpresentational State Transfer (REST) principles. Comput. Mater. Sci. 2015, 97, 209-15.

11. Ong, S. P.; Richards, W. D.; Jain, A.; et al. Python Materials Genomics (pymatgen): a robust, open-source python library for materials analysis. Comput. Mater. Sci. 2013, 68, 314-9.

12. Aykol, M.; Dwaraknath, S. S.; Sun, W.; Persson, K. A. Thermodynamic limit for synthesis of metastable inorganic materials. Sci. Adv. 2018, 4, eaaq0148.

13. Jain, A.; Hautier, G.; Ong, S. P.; et al. Formation enthalpies by mixing GGA and GGA + U calculations. Phys. Rev. B. 2011, 84, 045115.

14. Wang, A.; Kingsbury, R.; McDermott, M.; et al. A framework for quantifying uncertainty in DFT energy corrections. Sci. Rep. 2021, 11, 15496.

15. Persson, K. A.; Waldwick, B.; Lazic, P.; Ceder, G. Prediction of solid-aqueous equilibria: Scheme to combine first-principles calculations of solids with experimental aqueous states. Phys. Rev. B. 2012, 85, 235438.

16. Singh, A. K.; Zhou, L.; Shinde, A.; et al. Electrochemical stability of metastable materials. Chem. Mater. 2017, 29, 10159-67.

17. Patel, A. M.; Nørskov, J. K.; Persson, K. A.; Montoya, J. H. Efficient Pourbaix diagrams of many-element compounds. Phys. Chem. Chem. Phys. 2019, 21, 25323-7.

18. Jia, X.; Yu, Z.; Liu, F.; et al. Identifying stable electrocatalysts initialized by data mining: Sb2WO6 for oxygen reduction. Adv. Sci. 2024, 11, e2305630.

19. Jia, X.; Zhou, Z.; Liu, F.; et al. Closed-loop framework for discovering stable and low-cost bifunctional metal oxide catalysts for efficient electrocatalytic water splitting in acid. J. Am. Chem. Soc. 2025, 147, 22642-54.

20. Li, H. AI-driven multi-agent collaborations for accelerating catalyst design. Natl. Sci. Rev. 2026, 13, nwag067.

21. Zhang, D.; Jia, X.; Tran, H. B.; et al. “DIVE” into hydrogen storage materials discovery with AI agents. Chem. Sci. 2026, 17, 3031-42.

22. Kang, Y.; Kim, J. ChatMOF: an artificial intelligence system for predicting and generating metal-organic frameworks using large language models. Nat. Commun. 2024, 15, 4705.

23. Wang, Q.; Yang, F.; Wang, Y.; et al. Unraveling the complexity of divalent hydride electrolytes in solid-state batteries via a data-driven framework with large language model. Angew. Chem. Int. Ed. Engl. 2025, 64, e202506573.

24. Wang, Z.; Zheng, Y.; Chorkendorff, I.; Nørskov, J. K. Acid-stable oxides for oxygen electrocatalysis. ACS. Energy. Lett. 2020, 5, 2905-8.

25. Liu, H.; Jia, X.; Cao, A.; Wei, L.; D’agostino, C.; Li, H. The surface states of transition metal X-ides under electrocatalytic conditions. J. Chem. Phys. 2023, 158, 124705.

26. Liu, H.; Zhang, D.; Holmes, S. M.; D’Agostino, C.; Li, H. Origin of the superior oxygen reduction activity of zirconium nitride in alkaline media. Chem. Sci. 2023, 14, 9000-9.

27. Wang, T.; Guo, Z.; Oka, H.; Kumatani, A.; Liu, C.; Li, H. Origin of electrocatalytic nitrogen reduction activity over transition metal disulfides: critical role of in situ generation of S vacancy. J. Mater. Chem. A. 2024, 12, 8438-46.

28. Zhang, D.; Wang, Z.; Liu, F.; et al. Unraveling the pH-dependent oxygen reduction performance on single-atom catalysts: from single- to dual-Sabatier optima. J. Am. Chem. Soc. 2024, 146, 3210-9.