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Cited by 134Machine Learning in Materials ScienceMetal-Organic Frameworks: Synthesis and ApplicationsArtificial Intelligence Applications

The DOI matches its Crossref record; 119 of the 120 references it cites were found in a registry or an index.

Large language models for reticular chemistry

Zheng, Zhiling, Rampal, Nakul, Inizan, Theo Jaffrelot, Borgs, Christian, Chayes, Jennifer T., Yaghi, Omar M.·2025·Nature Reviews Materials
DOI 10.1038/s41578-025-00772-8
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Crossref · 10.1038/s41578-025-00772-8

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119 of 119 references verified0 citation errors · 0 not checkable

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Citations

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Show all 120 references

Research works printed but not linked: 16

  1. Yaghi, O. M. & Zheng, Z. Reticular chemistry and new materials. In 26th Int. Solvay Conf. Chem. Chem. Chall. 21st Century (eds Wüthrich, K., Feringa, B. L., Rongy, L. & De Wit, A.) 155–160 (World Scientific, 2024).
  2. Gupta, P., Ding, B., Guan, C. & Ding, D. Generative AI: a systematic review using topic modelling techniques. Data Inf. Manag. 8, 100066 (2024).
  3. OpenAI et al. GPT-4 technical report. Preprint at https://arxiv.org/abs/2303.08774 (2023). · arXiv
  4. Gemini Team et al. Gemini: a family of highly capable multimodal models. Preprint at https://arxiv.org/abs/2312.11805 (2023). · arXiv
  5. Liu, H., Li, C., Wu, Q. & Lee, Y. J. Visual instruction tuning. In 37th Conf. Neural Inform. Process. Syst. (NeurIPS, 2023).
  6. Yang, Z. et al. The dawn of LMMs: preliminary explorations with GPT-4V(ision). Preprint at https://arxiv.org/abs/2309.17421 (2023). · arXiv
  7. Dong, Q. et al. A survey on in-context learning. Preprint at https://arxiv.org/abs/2301.00234 (2024). · arXiv
  8. Asai, A., Wu, Z., Wang, Y., Sil, A. & Hajishirzi, H. Self-RAG: learning to retrieve, generate, and critique through self-reflection. In Proc. 12th Int. Conf. Learn. Represent. (ICLR, 2023).
  9. Jiang, A. Q. et al. Mistral 7B. Preprint at https://arxiv.org/abs/2310.06825v1 (2023). · arXiv
  10. Hu, E. J. et al. Lora: low-rank adaptation of large language models. In Proc. 10th Int. Conf. Learn. Represent. (ICLR, 2021).
  11. Han, Z., Gao, C., Liu, J., Zhang, J. & Zhang, S. Q. Parameter-efficient fine-tuning for large models: a comprehensive survey. Trans. Mach. Learn. Res. https://openreview.net/forum?id=lIsCS8b6zj (2024).
  12. Lee, W., Kang, Y., Bae, T. & Kim, J. Harnessing large language model to collect and analyze metal-organic framework property dataset. Preprint at https://arxiv.org/abs/2404.13053. · arXiv
  13. Liu, S. et al. Conversational Drug Editing Using Retrieval and Domain Feedback. In Proc. 12th Int. Conf. Learn. Represent. (ICLR, 2024).
  14. An, Y. et al. Knowledge graph question answering for materials science (KGQA4MAT): developing natural language interface for metal-organic frameworks knowledge graph (MOF-KG) using LLM. In 17th Int. Conf. Metadata Semantics Res. (Springer, 2023).
  15. Rubungo, A. N., Li, K., Hattrick-Simpers, J. & Dieng, A. B. LLM4Mat-bench: benchmarking large language models for materials property prediction. Preprint at https://arxiv.org/abs/2411.00177 (2024). · arXiv
  16. Stojkovic, J., Choukse, E., Zhang, C., Goiri, I. & Torrellas, J. Towards greener LLMs: bringing energy-efficiency to the forefront of LLM inference. Preprint at https://arxiv.org/abs/2403.20306 (2024). · arXiv

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Metadata

Authors
Zheng, Z., Rampal, N., Inizan, T. J., Borgs, C., Chayes, J. T., & Yaghi, O. M.
Affiliations
  • University of California, Berkeley (US)
  • King Abdulaziz City for Science and Technology (SA)
Corresponding author
Yaghi, Omar M.
ORCID
Venue
Nature Reviews Materials
Publisher
Springer Science and Business Media LLC
Published
2025-02-01
Volume
10
Issue
5
Pages
369-381
ISSN
2058-8437 (ISSN-L)
DOI
10.1038/s41578-025-00772-8
OpenAlex
W4407024439
Semantic Scholar
004afc06f3ed2ae0aba710f52eab181b16164cc0
Cited by
134
References
120
Citations by year
2026: 71 · 2025: 63
FWCI
6.69
Type
journal article
Language
en
Open access
closed
Licence
Locations
Nature Reviews Materials (publishedVersion)
Topics
  • Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
  • Physical Sciences › Chemistry › Inorganic Chemistry › Metal-Organic Frameworks: Synthesis and Applications
  • Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
Keywords
large language models, reticular chemistry, metal-organic frameworks, covalent organic frameworks, prompt engineering, LLM fine-tuning, crystallization, materials discovery, synthesis-structure-property relationships, data-driven research

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