REFERENCES
1. Wei, C.; Rao, R. R.; Peng, J.; et al. Recommended practices and benchmark activity for hydrogen and oxygen electrocatalysis in water splitting and fuel cells. Adv. Mater. 2019, 31, 1806296.
2. Wang, T.; Iriawan, H.; Peng, J.; et al. Confined water for catalysis: thermodynamic properties and reaction kinetics. Chem. Rev. 2025, 125, 1420-67.
3. Peng, J. Toward data-driven predictive modeling of electrocatalyst stability and surface reconstruction. J. Chem. Phys. 2025, 163, 040902.
4. Yuan, S.; Peng, J.; Cai, B.; et al. Tunable metal hydroxide-organic frameworks for catalysing oxygen evolution. Nat. Mater. 2022, 21, 673-80.
5. Peng, J.; Giner-sanz, J. J.; Giordano, L.; et al. Design principles for transition metal nitride stability and ammonia generation in acid. Joule. 2023, 7, 150-67.
6. Zheng, D. J.; Peng, J.; Mccormack, K.; et al. Uniting activity design principles of anode catalysts for direct liquid fuel cells. EES. Catal. 2024, 2, 1186-209.
7. Löffler, T.; Ludwig, A.; Rossmeisl, J.; Schuhmann, W. What makes high-entropy alloys exceptional electrocatalysts? Angew. Chem. Int. Ed. Engl. 2021, 60, 26894-903.
8. Hsu, W. L.; Tsai, C. W.; Yeh, A. C.; Yeh, J. W. Clarifying the four core effects of high-entropy materials. Nat. Rev. Chem. 2024, 8, 471-85.
9. Kar, N.; Skrabalak, S. E. Synthetic methods for high-entropy nanomaterials. Nat. Rev. Mater. 2025, 10, 638-53.
10. Peng, J.; Schwalbe-koda, D.; Akkiraju, K.; et al. Human- and machine-centred designs of molecules and materials for sustainability and decarbonization. Nat. Rev. Mater. 2022, 7, 991-1009.
11. Peng, J.; Damewood, J. K.; Karaguesian, J.; Gómez-bombarelli, R.; Shao-horn, Y. Navigating multimetallic catalyst space with Bayesian optimization. Joule. 2021, 5, 3069-71.
12. Rohr, B.; Stein, H. S.; Guevarra, D.; et al. Benchmarking the acceleration of materials discovery by sequential learning. Chem. Sci. 2020, 11, 2696-706.
13. Mints, V. A.; Pedersen, J. K.; Bagger, A.; et al. Exploring the composition space of high-entropy alloy nanoparticles for the electrocatalytic H2/CO oxidation with Bayesian optimization. ACS. Catal. 2022, 12, 11263-71.
14. Jenewein, K. J.; Torresi, L.; Haghmoradi, N.; Kormányos, A.; Friederich, P.; Cherevko, S. Navigating the unknown with AI: multiobjective Bayesian optimization of non-noble acidic OER catalysts. J. Mater. Chem. A. 2024, 12, 3072-83.
15. Esterhuizen, J. A.; Mathur, A.; Goldsmith, B. R.; Linic, S. High-performance iridium-molybdenum oxide electrocatalysts for water oxidation in acid: Bayesian optimization discovery and experimental testing. J. Am. Chem. Soc. 2024, 146, 5511-22.
16. Tadgell, C. A.; Kato, M.; Dieb, S.; et al. Machine learning-assisted development of platinum-free RuNiCo nanocages for electrocatalytic hydrogen oxidation reaction in acidic media. ACS. Appl. Energy. Mater. 2025, 8, 14052-7.
17. Niu, X.; Chen, Y.; Sun, M.; et al. Bayesian learning-assisted catalyst discovery for efficient iridium utilization in electrochemical water splitting. Sci. Adv. 2025, 11, eadw0894.
18. Xin, H.; Kitchin, J.; López, N.; et al. Roadmap for transforming heterogeneous catalysis with artificial intelligence. ChemRxiv 2025. Available online: https://doi.org/10.26434/chemrxiv-2025-chn4j (accessed 20 November 2025).
19. Zhang, Z.; Ren, Z.; Hsu, C. W.; et al. A multimodal robotic platform for multi-element electrocatalyst discovery. Nature. 2025, 647, 390-6.
20. Frazier, P. I.; Powell, W. B.; Dayanik, S. A knowledge-gradient policy for sequential information collection. SIAM. J. Control. Optim. 2008, 47, 2410-39.
21. Chen, E. Hong, Z.W.; Pajarinen, J.; Agrawal, P. Redeeming intrinsic rewards via constrained optimization. In Advances in Neural Information Processing Systems 35 (NeurIPS 2022), New Orleans, LA, USA, and online, November 28-December 9, 2022; Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K., Oh, A., Eds.; Curran Associates, Inc.: Red Hook, NY, USA, 2022; pp 4996-5008.
22. Yao, Y.; Huang, Z.; Xie, P.; et al. Carbothermal shock synthesis of high-entropy-alloy nanoparticles. Science. 2018, 359, 1489-94.
23. Boiko, D. A.; Macknight, R.; Kline, B.; Gomes, G. Autonomous chemical research with large language models. Nature. 2023, 624, 570-8.
24. M. Bran, A.; 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.
25. Darvish, K.; Skreta, M.; Zhao, Y.; et al. ORGANA: a robotic assistant for automated chemistry experimentation and characterization. Matter. 2025, 8, 101897.
26. Song, T.; Luo, M.; Zhang, X.; et al. A multiagent-driven robotic AI chemist enabling autonomous chemical research on demand. J. Am. Chem. Soc. 2025, 147, 12534-45.
27. Jing, Z.; Guo, Y.; Wang, Q.; et al. Ambient hydrogenation of solid aromatics enabled by a high entropy alloy nanocatalyst. Nat. Commun. 2024, 15, 5806.
28. Luo, J.; Li, X.; Ye, Y.; et al. Progressive fabrication of a Pt-based high-entropy-alloy catalyst toward highly efficient propane dehydrogenation. Angew. Chem. Int. Ed. Engl. 2024, 64, e202419093.
29. 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 20 November 2025).
30. Stolberg, M. A.; Lopez, J.; Cawthern, S. D.; et al. A data-driven platform for automated characterization of polymer electrolytes. Matter. 2025, 8, 102129.
31. Szymanski, N. J.; Rendy, B.; Fei, Y.; et al. An autonomous laboratory for the accelerated synthesis of novel materials. Nature. 2023, 624, 86-91.
32. Delgado-licona, F.; Alsaiari, A.; Dickerson, H.; et al. Flow-driven data intensification to accelerate autonomous inorganic materials discovery. Nat. Chem. Eng. 2025, 2, 436-46.
33. Abolhasani, M.; Kumacheva, E. The rise of self-driving labs in chemical and materials sciences. Nat. Synth. 2023, 2, 483-92.
34. Tom, G.; Schmid, S. P.; Baird, S. G.; et al. Self-driving laboratories for chemistry and materials science. Chem. Rev. 2024, 124, 9633-732.
35. Anthropic. Introducing the model context protocol. November 24, 2024. https://www.anthropic.com/news/model-context-protocol (accessed 2025-11-20).
36. Roch, L. M.; Häse, F.; Kreisbeck, C.; et al. ChemOS: an orchestration software to democratize autonomous discovery. PLoS. ONE. 2020, 15, e0229862.
37. Sim, M.; Vakili, M. G.; Strieth-kalthoff, F.; et al. ChemOS2.0: an orchestration architecture for chemical self-driving laboratories. Matter. 2024, 7, 2959-77.
38. Fei, Y.; Rendy, B.; Kumar, R.; et al. AlabOS: a Python-based reconfigurable workflow management framework for autonomous laboratories. Digit. Discov. 2024, 3, 2275-88.
39. Li, K.; Decost, B.; Choudhary, K.; Greenwood, M.; Hattrick-simpers, J. A critical examination of robustness and generalizability of machine learning prediction of materials properties. npj. Comput. Mater. 2023, 9, 55.
40. Dai, J.; Adhikari, S.; Wen, M. Uncertainty quantification and propagation in atomistic machine learning. Rev. Chem. Eng. 2025, 41, 333-57.
41. Li, K.; Rubungo, A. N.; Lei, X.; et al. Probing out-of-distribution generalization in machine learning for materials. Commun. Mater. 2025, 6, 9.
42. Sabanza-gil, V.; Barbano, R.; Pacheco Gutiérrez, D.; et al. Best practices for multi-fidelity Bayesian optimization in materials and molecular research. Nat. Comput. Sci. 2025, 5, 572-81.
43. Balcells, D. Co-intelligent design of catalysis research with large language models: hype or reality? ACS. Catal. 2025, 15, 16412-20.
44. Xin, H.; Kitchin, J. R.; Kulik, H. J. Towards agentic science for advancing scientific discovery. Nat. Mach. Intell. 2025, 7, 1373-5.
45. Kalai, A. T.; Nachum, O.; Vempala, S. S.; Zhang, E. Why language models hallucinate. arXiv 2025, arXiv:2509.04664. Available online: https://doi.org/10.48550/arXiv.2509.04664 (accessed 20 November 2025).
46. 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. Nature. Chem. 2025, 17, 1027-34.
47. Miret, S.; Krishnan, N. M. A. Enabling large language models for real-world materials discovery. Nat. Mach. Intell. 2025, 7, 991-8.
48. Leong, S. X.; Griesbach, C. E.; Zhang, R.; et al. Steering towards safe self-driving laboratories. Nat. Rev. Chem. 2025, 9, 707-22.
49. Macknight, R.; Boiko, D. A.; Regio, J. E.; Gallegos, L. C.; Neukomm, T. A.; Gomes, G. Rethinking chemical research in the age of large language models. Nat. Comput. Sci. 2025, 5, 715-26.
