REFERENCES
1. Zheng, Z.; Rampal, N.; Inizan, T. J.; Borgs, C.; Chayes, J. T.; Yaghi, O. M. Large language models for reticular chemistry. Nat. Rev. Mater. 2025, 10, 369-81.
2. Yuan, W.; Chen, G.; Wang, Z.; You, F. Empowering generalist material intelligence with large language models. Adv. Mater. 2025, 37, e2502771.
3. Chen, C. AI in materials science: charting the course to Nobel-worthy breakthroughs. Matter 2024, 7, 4123-5.
4. Van, M. H.; Verma, P.; Zhao, C.; Wu, X. A survey of AI for materials science: foundation models, LLM agents, datasets, and tools. arXiv 2025, arXiv:2506.20743. Available online: https://doi.org/10.48550/arXiv.2506.20743 (accessed 22 January 2026).
5. Zhou, B.; Jin, S.; Shao, J.; Chen, N.; Shanghai Institute of Metallurgy, Academia Sinica, Shanghai, China. IMEC - an expert system for retrieval and prediction of binary intermetallic compounds. Acta. Metall. Sin. 1989, 2, 428-33. Available online: https://www.amse.org.cn/EN/Y1989/V2/I12/428 (accessed 22 January 2026).
6. Lindsay, R. K.; Buchanan, B. G.; Feigenbaum, E. A.; Lederberg, J. DENDRAL: a case study of the first expert system for scientific hypothesis formation. Artif. Intell. 1993, 61, 209-61.
7. Wei, J.; Yang, Y.; Zhang, X.; et al. From AI for science to agentic science: a survey on autonomous scientific discovery. arXiv 2025, arXiv:2508.14111. Available online: https://doi.org/10.48550/arXiv.2508.14111 (accessed 22 January 2026).
8. 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.
9. Gridach, M.; Nanavati, J.; Abidine, K. Z. E.; Mendes, L.; Mack, C. Agentic AI for scientific discovery: a survey of progress, challenges, and future directions. arXiv 2025, arXiv:2503.08979. Available online: https://doi.org/10.48550/arXiv.2503.08979 (accessed 22 January 2026).
10. Feng, R.; Liang, Y.; Yin, T.; Gao, P.; Wang, W. Agentic assistant for materials scientists. Electrochem. Soc. Interface. 2025, 34, 45.
11. Duan, C.; Nandy, A.; Pal, S. C.; et al. The rise of generative AI for metal-organic framework design and synthesis. arXiv 2025, arXiv:2508.13197. Available online: https://doi.org/10.48550/arXiv.2508.13197 (accessed 22 January 2026).
12. Bayley, O.; Savino, E.; Slattery, A.; Noël, T. Autonomous chemistry: navigating self-driving labs in chemical and material sciences. Matter 2024, 7, 2382-98.
13. Shir, O. Towards AI research agents in the chemical sciences. ChemRxiv 2024. Available online: https://doi.org/10.26434/chemrxiv-2024-lf2xx (accessed 22 January 2026).
14. Bae, S.; Jeon, M.; Moon, H. R. Text mining in MOF research: from manual curation to large language model-based automation. Chem. Commun. 2025, 61, 11083-94.
15. Abramson, J.; Adler, J.; Dunger, J.; et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 2024, 630, 493-500.
16. Huang, L.; Koutra, D.; Kulkarni, A.; et al. Towards agentic AI for science: hypothesis generation, comprehension, quantification, and validation. In Proceedings of the Companion Proceedings of the ACM on Web Conference 2025, Sydney, Australia, April 28-May 2, 2025; Association for Computing Machinery: New York, NY, USA; pp 1639-42.
17. Abolhasani, M.; Kumacheva, E. The rise of self-driving labs in chemical and materials sciences. Nat. Synth. 2023, 2, 483-92.
19. Agarwal, S.; Sahu, G.; Puri, A.; et al. LitLLM: a toolkit for scientific literature review. arXiv 2024, arXiv:2402.01788. Available online: https://doi.org/10.48550/arXiv.2402.01788 (accessed 22 January 2026).
20. D’Arcy, M.; Hope, T.; Birnbaum, L.; Downey, D. MARG: multi-agent review generation for scientific papers. arXiv 2024, arXiv:2401.04259. Available online: https://doi.org/10.48550/arXiv.2401.04259 (accessed 22 January 2026).
21. Zhang, Q.; Hu, Y.; Yan, J.; et al. Large-language-model-based AI agent for organic semiconductor device research. Adv. Mater. 2024, 36, e2405163.
22. Chen, K.; Du, Y.; Li, J.; Cao, H.; Guo, M.; Dang, X.; Li, L.; Qiu, J.; Heng, P.A.; Chen, G. ChemMiner: a large language model agent system for chemical literature data mining. arXiv 2024, arXiv:2402.12993. Available online: https://doi.org/10.48550/arXiv.2402.12993 (accessed 22 January 2026).
23. Odobesku, R.; Romanova, K.; Mirzaeva, S.; et al. Agent-based multimodal information extraction for nanomaterials. npj. Comput. Mater. 2025, 11, 1674.
24. Bazgir, A.; Praneeth Madugula, R.c.; Zhang, Y. Multicrossmodal automated agent for integrating diverse materials science data. arXiv 2025, arXiv:2505.15132. Available online: https://doi.org/10.48550/arXiv.2505.15132 (accessed 22 January 2026).
25. Ansari, M.; Moosavi, S. M. Agent-based learning of materials datasets from the scientific literature. Digital. Discov. 2024, 3, 2607-17.
26. Buehler, M. J. MechGPT, a language-based strategy for mechanics and materials modeling that connects knowledge across scales, disciplines, and modalities. Appl. Mech. Rev. 2024, 76, 021001.
27. Zhang, D.; Jia, X.; Hung, T. B.; et al. “DIVE” into hydrogen storage materials discovery with AI agents. arXiv 2025, arXiv:2508.13251. Available online: https://doi.org/10.48550/arXiv.2508.13251 (accessed 22 January 2026).
28. Li, X.; Huang, Z.; Quan, S.; Peng, C.; Ma, X. SLM-MATRIX: a multi-agent trajectory reasoning and verification framework for enhancing language models in materials data extraction. npj. Comput. Mater. 2025, 11, 1719.
29. Callahan, T. J.; Park, N. H.; Capponi, S. Agentic mixture-of-workflows for multi-modal chemical search. arXiv 2025, arXiv:2502.19629. Available online: https://doi.org/10.48550/arXiv.2502.19629 (accessed 22 January 2026).
30. Lee, N.; De Brouwer, E.; Hajiramezanali, E.; Park, C.; Scalia, G. RAG-enhanced collaborative LLM agents for drug discovery. arXiv 2025, arXiv:2502.17506. Available online: https://doi.org/10.48550/arXiv.2502.17506 (accessed 22 January 2026).
31. Buehler, M. J. Generative retrieval-augmented ontologic graph and multiagent strategies for interpretive large language model-based materials design. ACS. Eng. Au. 2024, 4, 241-77.
32. McNaughton, A. D.; Sankar, Ramalaxmi. G. K.; Kruel, A.; Knutson, C. R.; Varikoti, R. A.; Kumar, N. CACTUS: chemistry agent connecting tool usage to science. ACS. Omega. 2024, 9, 46563-73.
33. Wu, M.; Wang, Y.; Ming, Y.; et al. CheMatAgent: enhancing LLMs for chemistry and materials science through tree-search based tool learning. arXiv 2025, arXiv:2506.07551. Available online: https://doi.org/10.48550/arXiv.2506.07551 (accessed 22 January 2026).
34. Ma, K. AI agents in chemical research: GVIM - an intelligent research assistant system. Digital. Discov. 2025, 4, 355-75.
35. Yu, B.; Baker, F. N.; Chen, Z.; et al. Tooling or not tooling? The impact of tools on language agents for chemistry problem solving. In Proceedings of the Findings of the Association for Computational Linguistics: NAACL 2025, Albuquerque, NM, USA, April 29-May 4, 2025; Chiruzzo, L., Ritter, A., Wang, L. Eds.; Association for Computational Linguistics: New York, NY, USA, 2025; pp. 7620-40.
36. Tang, X.; Hu, T.; Ye, M.; et al. ChemAgent: self-updating memories in large language models improves chemical reasoning. arXiv 2025, arXiv:2501.06590. Available online: https://doi.org/10.48550/arXiv.2501.06590 (accessed 22 January 2026).
37. Zhang, B.; Li, X.; Xu, H.; Jin, Z.; Wu, Q.; Li, C. TopoMAS: large language model driven topological materials multiagent system. arXiv 2025, arXiv:2507.04053. Available online: https://doi.org/10.48550/arXiv.2507.04053 (accessed 22 January 2026).
38. Kumbhar, S.; Mishra, V.; Coutinho, K.; Handa, D.; Iquebal, A.; Baral, C. Hypothesis generation for materials discovery and design using goal-driven and constraint-guided LLM agents. arXiv 2025, arXiv:2501.13299. Available online: https://doi.org/10.48550/arXiv.2501.13299 (accessed 22 January 2026).
39. Robson, M. J.; Xu, S.; Wang, Z.; Chen, Q.; Ciucci, F. Multi-agent-network-based idea generator for zinc-ion battery electrolyte discovery: a case study on zinc tetrafluoroborate hydrate-based deep eutectic electrolytes. Adv. Mater. 2025, 37, e2502649.
40. Ansari, M.; Watchorn, J.; Brown, C. E.; Brown, J. S. dZiner: rational inverse design of materials with AI agents. arXiv 2024, arXiv:2410.03963. Available online: https://doi.org/10.48550/arXiv.2410.03963 (accessed 22 January 2026).
41. Ghafarollahi, A.; Buehler, M. J. SciAgents: automating scientific discovery through bioinspired multi-agent intelligent graph reasoning. Adv. Mater. 2025, 37, e2413523.
42. Baek, J.; Jauhar, S. K.; Cucerzan, S.; Hwang, S. J. ResearchAgent: iterative research idea generation over scientific literature with large language models. arXiv 2024, arXiv:2404.07738. Available online: https://doi.org/10.48550/arXiv.2404.07738 (accessed 22 January 2026).
43. Ma, Y.; Gou, Z.; Hao, J.; et al. SciAgent: tool-augmented language models for scientific reasoning. arXiv 2024, arXiv:2402.11451. Available online: https://doi.org/10.48550/arXiv.2402.11451 (accessed 22 January 2026).
44. Yuan, J.; Yan, X.; Feng, S.; et al. Dolphin: moving towards closed-loop auto-research through thinking, practice, and feedback. arXiv 2025, arXiv:2501.03916. Available online: https://doi.org/10.48550/arXiv.2501.03916 (accessed 22 January 2026).
45. Luu, R. K.; Deng, J.; Ibrahim, M. S.; et al. Generative artificial intelligence extracts structure-function relationships from plants for new materials. arXiv 2025, arXiv:2508.06591. Available online: https://doi.org/10.48550/arXiv.2508.06591 (accessed 22 January 2026).
46. Chen, J.; Saha, S.; Bansal, M. ReConcile: round-table conference improves reasoning via consensus among diverse LLMs. arXiv 2023, arXiv:2309.13007. Available online: https://doi.org/10.48550/arXiv.2309.13007 (accessed 22 January 2026).
47. Yang, Z.; Liu, W.; Gao, B.; et al. MOOSE-Chem: large language models for rediscovering unseen chemistry scientific hypotheses. arXiv 2024, arXiv:2410.07076. Available online: https://doi.org/10.48550/arXiv.2410.07076 (accessed 22 January 2026).
48. Yang, Z.; Liu, W.; Gao, B.; et al. MOOSE-Chem2: exploring LLM limits in fine-grained scientific hypothesis discovery via hierarchical search. arXiv 2025, arXiv:2505.19209. Available online: https://doi.org/10.48550/arXiv.2505.19209 (accessed 22 January 2026).
49. Liu, W.; Yang, Z.; Wang, J.; et al. MOOSE-Chem3: toward experiment-guided hypothesis ranking via simulated experimental feedback. arXiv 2025, arXiv:2505.17873. Available online: https://doi.org/10.48550/arXiv.2505.17873 (accessed 22 January 2026).
50. Schmidgall, S.; Moor, M. AgentRxiv: towards collaborative autonomous research. arXiv 2025, arXiv:2503.18102. Available online: https://doi.org/10.48550/arXiv.2503.18102 (accessed 22 January 2026).
51. Pu, Y.; Lin, T.; Chen, H. PiFlow: principle-aware scientific discovery with multi-agent collaboration. arXiv 2025, arXiv:2505.15047. Available online: https://doi.org/10.48550/arXiv.2505.15047 (accessed 22 January 2026).
52. Lai, Z.; Pu, Y. PriM: principle-inspired material discovery through multi-agent collaboration. arXiv 2025, arXiv:2504.08810. Available online: https://doi.org/10.48550/arXiv.2504.08810 (accessed 22 January 2026).
53. Madaan, A.; Tandon, N.; Gupta, P.; et al. Self-refine: iterative refinement with self-feedback. In Advances in Neural Information Processing Systems 36 (NeurIPS 2023), New Orleans, LA, USA, December 10-16, 2023; Oh, A., Naumann, T., Globerson, A., Saenko, K., Hardt, M., Levine, S., Eds.; Neural Information Processing Systems Foundation, Inc.: Vancouver, Canada; pp 46534-94. Available online: https://proceedings.neurips.cc/paper_files/paper/2023/hash/91edff07232fb1b55a505a9e9f6c0ff3-Abstract-Conference.html(accessed 22 January 2026).
54. Su, H.; Chen, R.; Tang, S.; et al. Many heads are better than one: improved scientific idea generation by a LLM-based multi-agent system. arXiv 2024, arXiv:2410.09403. Available online: https://doi.org/10.48550/arXiv.2410.09403 (accessed 22 January 2026).
55. Ghafarollahi, A.; Buehler, M. J. Sparks: multi-agent artificial intelligence model discovers protein design principles. arXiv 2025, arXiv:2504.19017. Available online: https://doi.org/10.48550/arXiv.2504.19017 (accessed 22 January 2026).
56. Liu, S.; Lu, Y.; Chen, S.; et al. DrugAgent: automating AI-aided drug discovery programming through LLM multi-agent collaboration. arXiv 2024, arXiv:2411.15692. Available online: https://doi.org/10.48550/arXiv.2411.15692 (accessed 22 January 2026).
57. 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 22 January 2026).
58. Tian, J.; Sobczak, M. T.; Patil, D.; et al. A multi-agent framework integrating large language models and generative AI for accelerated metamaterial design. arXiv 2025, arXiv:2503.19889. Available online: https://doi.org/10.48550/arXiv.2503.19889 (accessed 22 January 2026).
59. Averly, R.; Baker, F. N.; Watson, I. A.; Ning, X. LIDDIA: language-based intelligent drug discovery agent. arXiv 2025, arXiv:2502.13959. Available online: https://doi.org/10.48550/arXiv.2502.13959 (accessed 22 January 2026).
60. Bou, A.; Thomas, M.; Dittert, S.; et al. ACEGEN: reinforcement learning of generative chemical agents for drug discovery. J. Chem. Inf. Model. 2024, 64, 5900-11.
61. Che, X.; Zhao, Y.; Liu, Q.; Yu, F.; Gao, H.; Zhang, L. CSstep: step-by-step exploration of the chemical space of drug molecules via multi-agent and multi-stage reinforcement learning. Chem. Eng. Sci. 2025, 317, 122048.
62. Rajak, P.; Wang, B.; Nomura, K.; et al. Autonomous reinforcement learning agent for stretchable kirigami design of 2D materials. npj. Comput. Mater. 2021, 7, 572.
63. 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.
64. Takahara, I.; Mizoguchi, T.; Liu, B. Accelerated inorganic materials design with generative AI agents. arXiv 2025, arXiv:2504.00741. Available online: https://doi.org/10.48550/arXiv.2504.00741 (accessed 22 January 2026).
65. Ghafarollahi, A.; Buehler, M. J. ProtAgents: protein discovery via large language model multi-agent collaborations combining physics and machine learning. Digit. Discov. 2024, 3, 1389-409.
66. Bagal, V.; Aggarwal, R.; Vinod, P. K.; Priyakumar, U. D. MolGPT: molecular generation using a transformer-decoder model. J. Chem. Inf. Model. 2022, 62, 2064-76.
67. Gan, J.; Zhong, P.; Du, Y.; et al. MatLLMSearch: crystal structure discovery with evolution-guided large language models. arXiv 2025, arXiv:2502.20933. Available online: https://doi.org/10.48550/arXiv.2502.20933 (accessed 22 January 2026).
68. Kim, H.; Jang, Y.; Ahn, S. MT-Mol: multi agent system with tool-based reasoning for molecular optimization. arXiv 2025, arXiv:2505.20820. Available online: https://doi.org/10.48550/arXiv.2505.20820 (accessed 22 January 2026).
69. Hu, Z.; Zhou, Y.; Wang, Z.; et al. Osda agent: leveraging large language models for de novo design of organic structure directing agents. In Proceedings of the The Thirteenth International Conference on Learning Representations, Singapore, Singapore, April 24-28, 2025; OpenReview.net: Amherst, MA, USA, 2025. Available online: https://openreview.net/forum?id=9YNyiCJE3k (accessed 22 January 2026).
70. Zhou, L.; Ling, H.; Yan, K.; et al. Toward greater autonomy in materials discovery agents: unifying planning, physics, and scientists. arXiv 2025, arXiv:2506.05616. Available online: https://doi.org/10.48550/arXiv.2506.05616 (accessed 22 January 2026).
71. Chaudhari, A.; Ock, J.; Farimani, A. B. Modular large language model agents for multi-task computational materials science. ChemRxiv 2025. Available online: https://doi.org/10.26434/chemrxiv-2025-zkn81-v2 (accessed 22 January 2026).
72. Hu, J.; Nawaz, H.; Rui, Y.; Chi, L.; Ullah, A.; Dral, P. O. Aitomia: your intelligent assistant for AI-driven atomistic and quantum chemical simulations. arXiv 2025, arXiv:2505.08195. Available online: https://doi.org/10.48550/arXiv.2505.08195 (accessed 22 January 2026).
73. Pham, T. D.; Tanikanti, A.; Keçeli, M. ChemGraph: an agentic framework for computational chemistry workflows. arXiv 2025, arXiv:2506.06363. Available online: https://doi.org/10.48550/arXiv.2506.06363 (accessed 22 January 2026).
74. Zou, Y.; Cheng, A. H.; Aldossary, A.; et al. El Agente: an autonomous agent for quantum chemistry. Matter 2025, 8, 102263.
75. Lv, S.; Peng, L.; Wu, W.; Yao, Y.; Jiao, S.; Hu, W. Bridging language models and computational materials science: a prompt-driven framework for material property prediction. MGE. Advances. 2025, 3, e70013.
76. Gadde, R. S. K.; Devaguptam, S.; Ren, F.; et al. Chatbot-assisted quantum chemistry for explicitly solvated molecules. Chem. Sci. 2025, 16, 3852-64.
77. Ni, B.; Buehler, M. J. MechAgents: large language model multi-agent collaborations can solve mechanics problems, generate new data, and integrate knowledge. Extreme. Mech. Lett. 2024, 67, 102131.
78. Liu, H.; Li, L. On languaging a simulation engine: rapid modeling of nanoporous media sorption by hierarchical language model. Mater. Today. Commun. 2024, 40, 109809.
79. Shi, Z.; Xin, C.; Huo, T.; et al. A fine-tuned large language model based molecular dynamics agent for code generation to obtain material thermodynamic parameters. Sci. Rep. 2025, 15, 10295.
80. Montoya, J. H.; Winther, K. T.; Flores, R. A.; Bligaard, T.; Hummelshøj, J. S.; Aykol, M. Autonomous intelligent agents for accelerated materials discovery. Chem. Sci. 2020, 11, 8517-32.
81. Jia, S.; Zhang, C.; Fung, V. LLMatDesign: autonomous materials discovery with large language models. arXiv 2024, arXiv:2406.13163. Available online: https://doi.org/10.48550/arXiv.2406.13163 (accessed 22 January 2026).
82. Lourenço, M. P.; Herrera, L. B.; Hostaš, J.; et al. QMLMaterial - a quantum machine learning software for material design and discovery. J. Chem. Theory. Comput. 2023, 19, 5999-6010.
83. Ghafarollahi, A.; Buehler, M. J. Rapid and automated alloy design with graph neural network-powered LLM-driven multi-agent systems. arXiv 2024, arXiv:2410.13768. Available online: https://doi.org/10.48550/arXiv.2410.13768 (accessed 22 January 2026).
84. Ito, S.; Muraoka, K.; Nakayama, A. Knowledge-informed molecular design for zeolite synthesis using general-purpose pretrained large language models toward human-machine collaboration. Chem. Mater. 2025, 37, 2447-56.
85. Lu, D.; Malof, J. M.; Padilla, W. J. An agentic framework for autonomous metamaterial modeling and inverse design. arXiv 2025, arXiv:2506.06935. Available online: https://doi.org/10.48550/arXiv.2506.06935 (accessed 22 January 2026).
86. Sprueill, H. W.; Edwards, C.; Agarwal, K.; et al. ChemReasoner: heuristic search over a large language model’s knowledge space using quantum-chemical feedback. arXiv 2024, arXiv:2402.10980. Available online: https://doi.org/10.48550/arXiv.2402.10980 (accessed 22 January 2026).
87. Ding, K.; Yu, J.; Huang, J.; Yang, Y.; Zhang, Q.; Chen, H. SciToolAgent: a knowledge-graph-driven scientific agent for multitool integration. Nat. Comput. Sci. 2025, 5, 962-72.
88. Bai, X.; Wang, H.; Xie, L.; et al. An integrated AI system for multi-objective screening of MOF materials. Sep. Purif. Technol. 2025, 376, 133939.
89. Elmegreen, B.; Hamann, H. F.; Wunsch, B.; et al. MDLab: AI frameworks for carbon capture and battery materials. Front. Environ. Sci. 2023, 11, 1204690.
90. Zhang, D.; Jia, X.; Liu, H.; et al. Cloud synthesis: a global close-loop feedback powered by autonomous AI-driven catalyst design agent. AI Agent 2025, 1, 2.
91. Chiang, Y.; Hsieh, E.; Chou, C. H.; Riebesell, J. LLaMP: large language model made powerful for high-fidelity materials knowledge retrieval and distillation. arXiv 2024, arXiv:2401.17244. Available online: https://doi.org/10.48550/arXiv.2401.17244 (accessed 22 January 2026).
92. Qiu, H.; Zhao, J.; Jing, E.; et al. Introducing PolySea: an LLM-based polymer smart evolution agent. ChemRxiv 2025. Available online: https://doi.org/10.26434/chemrxiv-2025-zw65g (accessed 22 January 2026).
93. Ghafarollahi, A.; Buehler, M. J. Automating alloy design and discovery with physics-aware multimodal multiagent AI. PNAS 2025, 122, e2414074122.
94. Li, Z.; Zhang, B.; Xiao, J.; et al. ChemHAS: hierarchical agent stacking for enhancing chemistry tools. arXiv 2025, arXiv:2505.21569. Available online: https://doi.org/10.48550/arXiv.2505.21569 (accessed 22 January 2026).
95. Zhang, H.; Song, Y.; Hou, Z.; Miret, S.; Liu, B. HoneyComb: a flexible LLM-based agent system for materials science. arXiv 2024, arXiv:2409.00135. Available online: https://doi.org/10.48550/arXiv.2409.00135 (accessed 22 January 2026).
96. Polat, C.; Tuncel, M.; Kurban, M.; Serpedin, E.; Kurban, H. xChemAgents: agentic AI for explainable quantum chemistry. arXiv 2025, arXiv:2505.20574. Available online: https://doi.org/10.48550/arXiv.2505.20574 (accessed 22 January 2026).
97. Fu, Z.; Huang, P.; Wang, X.; et al. Artificial intelligence-assisted ultrafast high-throughput screening of high-entropy hydrogen evolution reaction catalysts. Adv. Energy. Mater. 2025, 15, 2500744.
98. Fan, H.; Huang, J.; Xu, J.; et al. AutoMEX: streamlining material extrusion with AI agents powered by large language models and knowledge graphs. Mater. Design. 2025, 251, 113644.
99. Li, Y.; Wang, S.; Lv, Z.; et al. Transforming the synthesis of carbon nanotubes with machine learning models and automation. Matter 2025, 8, 101913.
100. Ma, Q.; Zhou, Y.; Li, J. Automated retrosynthesis planning of macromolecules using large language models and knowledge graphs. Macromol. Rapid. Commun. 2025, e2500065.
101. Rajak, P.; Krishnamoorthy, A.; Mishra, A.; Kalia, R.; Nakano, A.; Vashishta, P. Autonomous reinforcement learning agent for chemical vapor deposition synthesis of quantum materials. npj. Comput. Mater. 2021, 7, 535.
102. Singh, N.; Lane, S.; Yu, T.; et al. A generalized platform for artificial intelligence-powered autonomous enzyme engineering. Nat. Commun. 2025, 16, 5648.
103. Lew, A. J. Accelerating materials recipe acquisition via LLM-mediated reinforcement learning. MRS. Adv. 2025, 10, 1493-500.
104. Low, A. K. Y.; Mekki-Berrada, F.; Gupta, A.; et al. Evolution-guided Bayesian optimization for constrained multi-objective optimization in self-driving labs. npj. Comput. Mater. 2024, 10, 1274.
105. Yoshikawa, N.; Skreta, M.; Darvish, K.; et al. Large language models for chemistry robotics. Auton. Robot. 2023, 47, 1057-86.
106. Cao, S.; Zhang, Z.; Alghadeer, M.; et al. Agents for self-driving laboratories applied to quantum computing. arXiv 2024, arXiv:2412.07978. Available online: https://doi.org/10.48550/arXiv.2412.07978 (accessed 22 January 2026).
107. Li, R.; Hu, Z.; Qu, W.; et al. LabUtopia: high-fidelity simulation and hierarchical benchmark for scientific embodied agents. arXiv 2025, arXiv:2505.22634. Available online: https://doi.org/10.48550/arXiv.2505.22634 (accessed 22 January 2026).
108. Zhang, X.; Chen, Z.; Chen, F.; et al. Material intelligence by the convergence of artificial intelligence and robotic platforms. Nexus 2025, 2, 100083.
109. Zhou, J.; Luo, M.; Chen, L.; et al. A multi-robot–multi-task scheduling system for autonomous chemistry laboratories. Digital. Discov. 2025, 4, 636-52.
110. Zhou, R.; Liu, H.; Babichuk, I. S.; et al. A lightweight model and multi-agent system for layer identification in two-dimensional materials. Comput. Mater. Sci. 2025, 259, 114106.
111. Zhu, Z.; Yuan, S.; Yang, Q.; et al. Autonomous scanning tunneling microscopy imaging via deep learning. J. Am. Chem. Soc. 2024, 146, 29199-206.
112. Mandal, I.; Soni, J.; Zaki, M.; et al. Autonomous microscopy experiments through large language model agents. arXiv 2024, arXiv:2501.10385. Available online: https://doi.org/10.48550/arXiv.2501.10385 (accessed 22 January 2026).
113. Maffettone, P. M.; Banko, L.; Cui, P.; et al. Crystallography companion agent for high-throughput materials discovery. Nat. Comput. Sci. 2021, 1, 290-7.
114. Chang, M.; Ament, S.; Amsler, M.; et al. Probabilistic phase labeling and lattice refinement for autonomous materials research. npj. Comput. Mater. 2025, 11, 1627.
115. Luo, Y.; Wang, B.; Smeets, S.; Sun, J.; Yang, W.; Zou, X. High-throughput phase elucidation of polycrystalline materials using serial rotation electron diffraction. Nat. Chem. 2023, 15, 483-90.
116. Kalinin, S. V.; Mukherjee, D.; Roccapriore, K.; et al. Machine learning for automated experimentation in scanning transmission electron microscopy. npj. Comput. Mater. 2023, 9, 1142.
117. Dave, A.; Mitchell, J.; Burke, S.; Lin, H.; Whitacre, J.; Viswanathan, V. Autonomous optimization of non-aqueous Li-ion battery electrolytes via robotic experimentation and machine learning coupling. Nat. Commun. 2022, 13, 5454.
118. Siemenn, A. E.; Das, B.; Ji, K.; Sheng, F.; Buonassisi, T. A self-supervised robotic system for autonomous contact-based spatial mapping of semiconductor properties. Sci. Adv. 2025, 11, eadw7071.
119. Huang, K.; Kain, C.; Diaz-vallejo, N.; Sohn, Y.; Zhou, L. High throughput mechanical testing platform and application in metal additive manufacturing and process optimization. J. Manuf. Process. 2021, 66, 494-505.
120. Prince, M. H.; Chan, H.; Vriza, A.; et al. Opportunities for retrieval and tool augmented large language models in scientific facilities. npj. Comput. Mater. 2024, 10, 251.
121. Vriza, A.; Prince, M. H.; Zhou, T.; Chan, H.; Cherukara, M. J. Operating advanced scientific instruments with AI agents that learn on the job. arXiv 2025, arXiv:2509.00098. Available online: https://doi.org/10.48550/arXiv.2509.00098 (accessed 22 January 2026).
122. 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.
123. Yao, L.; Samantray, S.; Ghosh, A.; et al. Operationalizing serendipity: multi-agent AI workflows for enhanced materials characterization with theory-in-the-loop. arXiv 2025, arXiv:2508.06569. Available online: https://doi.org/10.48550/arXiv.2508.06569 (accessed 22 January 2026).
124. Ding, N.; Qu, S.; Xie, L.; et al. Automating exploratory proteomics research via language models. arXiv 2024, arXiv:2411.03743. Available online: https://doi.org/10.48550/arXiv.2411.03743 (accessed 22 January 2026).
125. Alber, S.; Chen, B.; Sun, E.; Isakova, A.A.; Wilk, A.J.; Zou, J. CellVoyager: AI CompBio agent generates new insights by autonomously analyzing biological data. bioRxiv 2025. Available online: https://doi.org/10.1101/2025.06.03.657517 (accessed 22 January 2026).
126. Altayeb, M.; Wang, X.; Mahmoud, M. R.; Ali, Y. M.; Al-shami, H. A.; Jiang, K. AI agents for UHPC experimental design: high strength and low cost with fewer experimental trials. Constr. Build. Mater. 2024, 416, 135206.
127. 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.
128. Lu, J.; Song, Z.; Zhao, Q.; et al. Generative design of functional metal complexes utilizing the internal knowledge and reasoning capability of large language models. J. Am. Chem. Soc. 2025, 147, 32377-88.
129. Burger, B.; Maffettone, P. M.; Gusev, V. V.; et al. A mobile robotic chemist. Nature 2020, 583, 237-41.
130. Szymanski, N. J.; Rendy, B.; Fei, Y.; et al. An autonomous laboratory for the accelerated synthesis of novel materials. Nature 2023, 624, 86-91.
131. Slattery, A.; Wen, Z.; Tenblad, P.; et al. Automated self-optimization, intensification, and scale-up of photocatalysis in flow. Science 2024, 383, eadj1817.
132. Boiko, D. A.; MacKnight, R.; Kline, B.; Gomes, G. Autonomous chemical research with large language models. Nature 2023, 624, 570-8.
133. 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.
134. Chen, K.; Lu, J.; Li, J.; et al. Chemist-X: large language model-empowered agent for reaction condition recommendation in chemical synthesis. arXiv 2023, arXiv:2311.10776. Available online: https://doi.org/10.48550/arXiv.2311.10776 (accessed 22 January 2026).
135. Zhang, Z.; Ren, Z.; Hsu, C. W.; et al. A multimodal robotic platform for multi-element electrocatalyst discovery. Nature 2025, 647, 390-6.
136. Xu, J.; Moran, C. H. J.; Ghorai, A.; et al. Autonomous multi-robot synthesis and optimization of metal halide perovskite nanocrystals. Nat. Commun. 2025, 16, 7841.
137. Fehlis, Y.; Crain, C.; Jensen, A.; et al. Accelerating drug discovery through agentic AI: a multi-agent approach to laboratory automation in the DMTA cycle. arXiv 2025, arXiv:2507.09023. Available online: https://doi.org/10.48550/arXiv.2507.09023 (accessed 22 January 2026).
138. 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.
139. Ruan, Y.; Lu, C.; Xu, N.; et al. An automatic end-to-end chemical synthesis development platform powered by large language models. Nat. Commun. 2024, 15, 10160.
140. Ni, Z.; Li, Y.; Hu, K.; et al. MatPilot: an LLM-enabled AI materials scientist under the framework of human-machine collaboration. arXiv 2024, arXiv:2411.08063. Available online: https://doi.org/10.48550/arXiv.2411.08063 (accessed 22 January 2026).
141. Bazgir, A.; Zhang, Y. MatAgent: a human-in-the-loop multi-agent LLM framework for accelerating the material science discovery cycle. In Proceedings of the AI for Accelerated Materials Design-ICLR 2025, Singapore, Singapore, April 24-28; OpenReview.net: Amherst, MA, USA, 2025. Available online: https://openreview.net/forum?id=2Nm6Ef4tZD (accessed 22 January 2026).
142. Dai, T.; Vijayakrishnan, S.; Szczypiński, F. T.; et al. Autonomous mobile robots for exploratory synthetic chemistry. Nature 2024, 635, 890-7.





