REFERENCES
1. Zhang, X.; Yan, M.; Chen, P.; et al. Emerging MOFs, COFs, and their derivatives for energy and environmental applications. Innovation 2025, 6, 100778.
2. Liu, Y.; Chen, L.; Yang, L.; et al. Porous framework materials for energy & environment relevant applications: a systematic review. Green. Energy. Environ. 2024, 9, 217-310.
3. Li, J. R.; Sculley, J.; Zhou, H. C. Metal-organic frameworks for separations. Chem. Rev. 2012, 112, 869-932.
4. Li, P.; Zhao, H.; Ji, R.; et al. A diacetylene covalent organic framework through a one-step two-electron O2 reduction pathway for efficient photosynthesis of H2O2. Catal. Sci. Technol. 2024, 14, 2470-8.
5. Kreno, L. E.; Leong, K.; Farha, O. K.; Allendorf, M.; Van Duyne, R. P.; Hupp, J. T. Metal-organic framework materials as chemical sensors. Chem. Rev. 2011, 112, 1105-25.
6. Ge, S.; Wei, K.; Peng, W.; et al. A comprehensive review of covalent organic frameworks (COFs) and their derivatives in environmental pollution control. Chem. Soc. Rev. 2024, 53, 11259-302.
7. Wang, K.; Li, Y.; Xie, L. H.; Li, X.; Li, J. R. Construction and application of base-stable MOFs: a critical review. Chem. Soc. Rev. 2022, 51, 6417-41.
8. He, D.; Jiang, Y.; Guillén-Soler, M.; et al. Algorithm-driven robotic discovery of polyoxometalate-scaffolding metal-organic frameworks. J. Am. Chem. Soc. 2024, 146, 28952-60.
9. Wang, Y.; Liu, H.; Hashimoto, Y.; Iwase, K.; Li, H.; Tomai, T. Materials process informatics-assisted precise particle size control of metal-organic frameworks. Chem. Sci. 2026, 17, 15034-43.
10. Oliveira, O. N. Jr.; Christino, L.; Oliveira, M. C. F.; Paulovich, F. V. Artificial intelligence agents for materials sciences. J. Chem. Inf. Model. 2023, 63, 7605-9.
11. Zhang, D.; Jia, X.; Wang, Y.; et al. Digital materials ecosystem: from databases to AI agents for autonomous discovery. Chem. Sci. 2026, 17, 5782-804.
12. Wang, Q.; Sato, R.; García-Méndez, R.; et al. AI agents for solid electrolytes: opportunities, challenges, and future directions. AI. Agent. 2025, 1, 10.
13. Zheng, Z.; Rong, Z.; Rampal, N.; Borgs, C.; Chayes, J. T.; Yaghi, O. M. A GPT‐4 reticular chemist for guiding MOF discovery. Angew. Chem. Int. Ed. Engl. 2023, 62, e202311983.
14. Zheng, Z.; Zhang, O.; Nguyen, H. L.; et al. ChatGPT research group for optimizing the crystallinity of MOFs and COFs. ACS. Cent. Sci. 2023, 9, 2161-70.
15. Pruyn, T. M.; Aswad, A.; Khan, S. T.; Huang, J.; Black, R.; Moosavi, S. M. MOF-ChemUnity: literature-informed large language models for metal-organic framework research. J. Am. Chem. Soc. 2025, 147, 43474-86.
16. Kang, Y.; Lee, W.; Bae, T.; Han, S.; Jang, H.; Kim, J. Harnessing large language models to collect and analyze metal-organic framework property data set. J. Am. Chem. Soc. 2025, 147, 3943-58.
17. Ansari, M.; Moosavi, S. M. Agent-based learning of materials datasets from the scientific literature. Digit. Discov. 2024, 3, 2607-17.
18. Lin, J.; Zhao, D.; Lu, S.; et al. Conversational large-language-model artificial intelligence agent for accelerated synthesis of metal-organic frameworks catalysts in olefin hydrogenation. ACS. Nano. 2025, 19, 23840-58.
19. 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.
20. Wang, K.; Ahn, D. D.; Rampal, N.; et al. Synthesis of highly crystalline covalent organic frameworks using large language models. J. Am. Chem. Soc. 2026, 148, 10094-104.
21. Zhang, D.; Chen, Y.; Liu, C.; et al. Accelerating catalyst materials discovery with large artificial intelligence models. Angew. Chem. Int. Ed. Engl. 2026, 65, e26150.
22. Jang, S. H.; Yao, Y.; Liu, C.; et al. Building a physics-aware AI ecosystem for solid-state hydrogen storage materials. arXiv 2026, arXiv:2605.03081. Available online: https://doi.org/10.48550/arXiv.2605.03081 (accessed 27 July 2026).
23. Zhuang, Y.; Yang, X.; Zhang, C.; et al. Materials databases: foundations of modern digital materials. Precis. Chem. 2026.
24. Wang, Y.; Wang, Q.; Jang, S. H.; Cheng, E. J.; Li, H. Discovering new materials knowledge from “old data”. Chem. Commun. 2026, 62, 9536-49.
25. Wang, Y.; Wang, Y.; Li, H. Finding the hidden catalytic knowledge from literature data. EES. Catal. 2026, 4, 837-50.
26. 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.
27. Statt, M. J.; Rohr, B. A.; Brown, K.; et al. ESAMP: event-sourced architecture for materials provenance management and application to accelerated materials discovery. Digit. Discov. 2023, 2, 1078-88.
28. Zhang, D.; Jia, X.; Tran, H. B.; et al. “DIVE” into hydrogen storage materials discovery with AI agents. Chem. Sci. 2026, 17, 3031-42.
29. Xin, H.; Kitchin, J. R.; López, N.; et al. Transparent reporting for agentic catalysis enabled by artificial intelligence (TRACE-AI): community guidelines and a publication checklist. ChemRxiv 2026, Epub ahead of print.
30. Park, J. S.; O’Brien, J.; Cai, C. J.; Morris, M. R.; Liang, P.; Bernstein, M. S. Generative agents: interactive simulacra of human behavior. In UIST’23: The 36th Annual ACM Symposium on User Interface Software and Technology, San Francisco CA USA; ACM: New York, NY, USA, 2023; pp 1-22.
31. Statt, M. J.; Rohr, B. A.; Guevarra, D.; Breeden, J.; Suram, S. K.; Gregoire, J. M. The materials experiment knowledge graph. Digit. Discov. 2023, 2, 909-14.
32. Szymanski, N. J.; Rendy, B.; Fei, Y.; et al. An autonomous laboratory for the accelerated synthesis of inorganic materials. Nature 2023, 624, 86-91.
33. Zheng, Z.; Zhang, O.; Borgs, C.; Chayes, J. T.; Yaghi, O. M. ChatGPT chemistry assistant for text mining and the prediction of MOF synthesis. J. Am. Chem. Soc. 2023, 145, 18048-62.
34. Lin, Z.; Ren, D.; Ran, K.; et al. Reshaping MOFs synthesis conditions mining with a dynamic multi-agents framework of large language model. Trans. Mater. Res. 2026, 2, 100176.
35. Kim, H.; Kim, D.; Kim, J. LitMOF: an LLM multi-agent for literature-validated metal-organic frameworks database correction and expansion. arXiv 2025, arXiv:2512.01693. Available online: https://doi.org/10.48550/arXiv.2512.01693 (accessed 27 July 2026).
36. Peivaste, I.; Boscher, N. D.; Makradi, A.; Belouettar, S. Escaping the hydrolysis trap: a react agent for inverse design of durable photocatalytic covalent organic frameworks. npj Comput. Mater. 2026, Epub ahead of print.
37. Bai, X.; Wang, H. T.; Xie, L. H.; et al. An integrated AI system for multi-objective screening of MOF materials. Sep. Purif. Technol. 2025, 376, 133939.
38. Li, Y.; Zhao, J.; Cao, X. E.; Li, S. A large language model-based multi-agent methodology for intelligent materials screening: a case study on MOFs for CO2 capture. Sep. Purif. Technol. 2026, 394, 137462.
39. Lee, J.; Bae, T.; Kim, J. SimMOF: AI agent for Automated MOF Simulations. arXiv 2026, arXiv:2603.29152. Available online: https://doi.org/10.48550/arXiv.2603.29152 (accessed 27 July 2026).
40. Inizan, T. J.; Yang, S.; Kaplan, A.; et al. System of agentic AI for the discovery of metal-organic frameworks. arXiv 2025, arXiv:2504.14110. Available online: https://doi.org/10.48550/arXiv.2504.14110 (accessed 27 July 2026).
41. Shi, T.; Li, Y.; Wang, Z.; et al. Knowledge-driven autonomous materials research via collaborative multi-agent and robotic system. Matter 2026, 9, 102577.
