Page 133 - Read Online
P. 133
Li et al. J. Mater. Inf. 2026, 6, 10 Page 25 of 31
Manuscript drafting: Li, C.; Ran, N.
Manuscript editing: Ran, N.; Liu, J.
Review and approval of the final manuscript: Li, C.; Ran, N.; Liu, J.
Availability of data and materials
Not applicable.
Financial support and sponsorship
This work was supported by the Advanced Materials-National Science and Technology Major Project
(2025ZD0619500, 2025ZD0613500), the National Natural Science Foundation of China (22133005,
22403103), the China Academy of Engineering Science Technology Strategic Consultation Project
(2025-XZ-3), the AI for Science Program, the Shanghai Municipal Commission of Economy and
Informatization (2025-GZL-RGZN-BTBX-01005), the Shanghai Sailing Program (23YF1454900), and the
Science and Technology Commission of Shanghai Municipality (25CL2902100).
Conflicts of interest
All authors declared that there are no conflicts of interest.
Ethical approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Copyright
© The Author(s) 2026.
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. DOI
2. Yuan, W.; Chen, G.; Wang, Z.; You, F. Empowering generalist material intelligence with large language models. Adv. Mater. 2025, 37,
e2502771. DOI PubMed
3. Chen, C. AI in materials science: charting the course to Nobel-worthy breakthroughs. Matter 2024, 7, 4123-5. DOI
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.c
n/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. DOI
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. DOI PubMed
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. DOI
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. DOI

