Global Hydrogen Review 2024 www.iea.org (International Energy Agency, 2024).
Jiao, K. et al. Designing the next generation of proton-exchange membrane fuel cells. Nature 595, 361–369 (2021).
Wan, R. et al. Earth-abundant electrocatalysts for acidic oxygen evolution. Nat. Catal. 7, 1288–1304 (2024).
An, L. et al. Recent development of oxygen evolution electrocatalysts in acidic environment. Adv. Mater. 33, 2006328 (2021).
Abed, J. et al. Pourbaix machine learning framework identifies acidic water oxidation catalysts exhibiting suppressed ruthenium dissolution. J. Am. Chem. Soc. 146, 15740–15750 (2024).
Li, J. et al. A review on highly efficient Ru-based electrocatalysts for acidic oxygen evolution reaction. Energy Fuels 38, 11521–11540 (2024).
Park, H., Onwuli, A., Butler, K. T. & Walsh, A. Mapping inorganic crystal chemical space. Faraday Discuss. 256, 601–613 (2025).
Yao, Z. et al. Machine learning for a sustainable energy future. Nat. Rev. Mater. 8, 202–215 (2023).
Sasmal, S. et al. Materials descriptors for advanced water dissociation catalysts in bipolar membranes. Nat. Mater. 23, 1421–1427 (2024).
Liu, X., Cai, C., Zhao, W., Peng, H. J. & Wang, T. Machine learning-assisted screening of stepped alloy surfaces for C1 catalysis. ACS Catal. 12, 4252–4260 (2022).
Chen, Z. W., Chen, L. X., Gariepy, Z., Yao, X. & Singh, C. V. High-throughput and machine-learning accelerated design of high entropy alloy catalysts. Trends Chem. 4, 577–579 (2022).
Vasudevan, R. K. et al. Materials science in the artificial intelligence age: high-throughput library generation, machine learning, and a pathway from correlations to the underpinning physics. MRS Commun. 9, 821–838 (2019).
Chanussot, L. et al. Open Catalyst 2020 (OC20) dataset and community challenges. ACS Catal. 11, 6059–6072 (2021).
Tran, R. et al. The Open Catalyst 2022 (OC22) dataset and challenges for oxide electrocatalysts. ACS Catal. 13, 3066–3084 (2023).
Erucar, I. & Keskin, S. High-throughput molecular simulations of metal organic frameworks for CO2 separation: opportunities and challenges. Front Mater. 5, 1–6 (2018).
Szymanski, N. J. et al. An autonomous laboratory for the accelerated synthesis of novel materials. Nature 624, 86–91 (2023).
Cheetham, A. K. & Seshadri, R. Artificial intelligence driving materials discovery? Perspective on the article: scaling deep learning for materials discovery. Chem. Mater. 36, 3490–3495 (2024).
Moon, J. et al. Active learning guides discovery of a champion four-metal perovskite oxide for oxygen evolution electrocatalysis. Nat. Mater. 23, 108–115 (2024).
Abed, J. et al. AMPERE: automated modular platform for expedited and reproducible electrochemical testing. Digit. Discov. 3, 2265–2274 (2024).
Janani, R., Farmilo, N., Roberts, A. & Sammon, C. Sol-gel synthesis pathway and electrochemical performance of ionogels: A deeper look into the importance of alkoxysilane precursor. J. Non Cryst. Solids 569, 120971 (2021).
Zhang, B. et al. Homogeneously dispersed multimetal oxygen-evolving catalysts. Science 352, 333–337 (2016).
Ward, L. et al. Matminer: an open source toolkit for materials data mining. Comput Mater. Sci. 152, 60–69 (2018).
Onwuli, A., Hegde, A. V., Nguyen, K. V. T., Butler, K. T. & Walsh, A. Element similarity in high-dimensional materials representations. Digit. Discov. 2, 1558–1564 (2023).
Chen, C., Ye, W., Zuo, Y., Zheng, C. & Ong, S. P. Graph networks as a universal machine learning framework for molecules and crystals. Chem. Mater. 31, 3564–3572 (2019).
Lundberg, S. M. et al. From local explanations to global understanding with explainable AI for trees. Nat. Mach. Intell. 2, 56–67 (2020).
Kessler, V. G. & Seisenbaeva, G. A. Molecular mechanisms of the metal oxide sol-gel process and their application in approaches to thermodynamically challenging complex oxide materials. J. Solgel Sci. Technol. 107, 190–200 (2023).
Baumann, T. F., Gash, A. E., Satcher, J. H., Leventis, N. & Steiner, S. A. in Springer Handbook of Aerogels (eds Aegerter, M. A., Leventis, N., Koebel, M. & Steiner, S. A., III) 419–435 https://doi.org/10.1007/978-3-030-27322-4_17 (Springer, 2023).
Rouwhorst, J., Ness, C., Stoyanov, S., Zaccone, A. & Schall, P. Nonequilibrium continuous phase transition in colloidal gelation with short-range attraction. Nat. Commun. 11, 3558 (2020).
Hu, C., Lu, W., Mata, A., Nishinari, K. & Fang, Y. Ions-induced gelation of alginate: mechanisms and applications. Int J. Biol. Macromol. 177, 578–588 (2021).
Edgington, J., Deberghes, A. & Seitz, L. C. Glassy carbon substrate oxidation effects on electrode stability for oxygen evolution reaction catalysis stability benchmarking. ACS Appl Energy Mater. 5, 12206–12218 (2022).
Du, K. et al. Interface engineering breaks both stability and activity limits of RuO2 for sustainable water oxidation. Nat. Commun. 13, 1–9 (2022).
Ikeda, H. et al. Microscopic high-speed video observation of oxygen bubble generation behavior and effects of anode electrode shape on OER performance in alkaline water electrolysis. Int J. Hydrog. Energy 47, 11116–11127 (2022).
Geiger, S. et al. The stability number as a metric for electrocatalyst stability benchmarking. Nat. Catal. 1, 508–515 (2018).
Oener, S. Z., Bergmann, A. & Cuenya, B. R. Designing active oxides for a durable oxygen evolution reaction. Nat. Synth. 2, 817–827 (2023).
Gao, G. et al. Recent advances in Ru/Ir-based electrocatalysts for acidic oxygen evolution reaction. Appl Catal. B 343, 123584 (2024).
Anantharaj, S., Kundu, S. & Noda, S. The Fe effect: a review unveiling the critical roles of Fe in enhancing OER activity of Ni and Co based catalysts. Nano Energy 80, 105514 (2021).
Wang, N. et al. Doping shortens the metal/metal distance and promotes OH coverage in non-noble acidic oxygen evolution reaction catalysts. J. Am. Chem. Soc. 145, 7829–7836 (2023).
Qin, Y. et al. RuO2 electronic structure and lattice strain dual engineering for enhanced acidic oxygen evolution reaction performance. Nat. Commun. 13, 3784 (2022).
Zhang, D. et al. Construction of Zn-doped RuO2 nanowires for efficient and stable water oxidation in acidic media. Nat. Commun. 14, 2517 (2023).
Zhu, W. et al. Stable and oxidative charged Ru enhance the acidic oxygen evolution reaction activity in two-dimensional ruthenium–iridium oxide. Nat. Commun. 14, 5365 (2023).
Liu, H. et al. Eliminating over-oxidation of ruthenium oxides by niobium for highly stable electrocatalytic oxygen evolution in acidic media. Joule 7, 558–573 (2023).
Sadeghi, M. A., Zhang, R. & Hattrick-Simpers, J. AutoEIS: automated equivalent circuit modeling from electrochemical impedance spectroscopy data using statistical machine learning. J. Open Source Softw. 10, 6256 (2025).
Jiang, X. et al. Interstitial-substitutional-mixed solid solution of RuO2 nurturing a new pathway beyond the activity-stability linear constraint in acidic water oxidation. Adv. Mater. 37, 2503354 (2025).
Patel, A. M., Nørskov, J. K., Persson, K. A. & Montoya, J. H. Efficient Pourbaix diagrams of many-element compounds. Phys. Chem. Chem. Phys. 21, 25323–25327 (2019).
Chen, T. & Guestrin, C. XGBoost: a scalable tree boosting system. In Proc. 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 785–794 https://doi.org/10.1145/2939672.2939785 (ACM, 2016).
Zhu, W. et al. Direct dioxygen radical coupling driven by octahedral ruthenium–oxygen–cobalt collaborative coordination for acidic oxygen evolution reaction. J. Am. Chem. Soc. 145, 17995–18006 (2023).
Li, L. et al. Spin-polarization strategy for enhanced acidic oxygen evolution activity. Adv. Mater. 35, 2302966 (2023).
Shen, Y. et al. Cr dopant mediates hydroxyl spillover on RuO2 for high-efficiency proton exchange membrane electrolysis. Nat. Commun. 15, 7861 (2024).
