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
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“Other reviews concentrate on natural-language processing in materials science, large language models for materials and chemistry, reticular chemistry, or generative crystal-structure modeling [5,11-14].”
From From Scientific Copilots to Tool-Grounded Autonomy: AI Agents in Simulation-Driven Materials Discovery (2026) · cites this paper as [13]
Citations
Cites
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Self-citation
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Pettinari, C. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-23709-6.00013-8
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.
- Venue
- Nature Reviews Materials
- Published
- 2025-02-01
- Volume
- 10
- Issue
- 5
- Pages
- 369-381
- ISSN
- 2058-8437 (ISSN-L)
- 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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