Comparison of LLMs in extracting synthesis conditions and generating Q&A datasets for metal–organic frameworks
Spoiler alert: Claude and Gemini did better than GPT-4 in extracting information from chemistry literature.
Authors
- Yuang Shi (ORCID: https://orcid.org/0000-0001-5552-5582)
- Christian Borgs (ORCID: https://orcid.org/0000-0001-5653-0498)
- Nakul Rampal (ORCID: https://orcid.org/0000-0002-6187-5631)
- Chengbin Zhao (ORCID: https://orcid.org/0000-0001-5273-2594)
- Jennifer Chayes (ORCID: https://orcid.org/0000-0003-4020-8618)
- Omar M. Yaghi (ORCID: https://orcid.org/0000-0002-5611-3325)
- Dongrong Joe Fu (ORCID: https://orcid.org/0009-0001-9800-352X)
Institutions
- King Abdulaziz City for Science and Technology (SA)
- Kavli Energy NanoScience Institute (US)
- University of California, Berkeley (US)
Publication Details
- Journal
- Digital Discovery
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1039/d5dd00081e
- Citations
- 8
- Primary Topic
- Metal-Organic Frameworks: Synthesis and Applications
- Type
- article
- Field-Weighted Citation Impact
- 4.30
Funders
- King Abdulaziz City for Science and Technology