Knowledge discovery from porous organic cage literature using a large language model
A GPT-4-based text mining workflow was developed for rapid information extraction from porous organic cages (POCs) literature. A chatbot was also created to answer POCs-related questions using the knowledge database generated from the workflow.
Authors
- Siyuan Yang (ORCID: https://orcid.org/0009-0003-2666-9335)
- Aiting Kai
- Linjiang Chen (ORCID: https://orcid.org/0000-0002-0382-5863)
- Ming Liu (ORCID: https://orcid.org/0000-0003-0008-1480)
- Ming Liu (ORCID: https://orcid.org/0000-0003-4256-6531)
- Yong‐Liang Su (ORCID: https://orcid.org/0000-0002-4045-5195)
Institutions
- University of Science and Technology of China (CN)
- Zhejiang University of Science and Technology (CN)
- Zhejiang University (CN)
- University of Birmingham (GB)
Publication Details
- Journal
- Digital Discovery
- Published
- 2024-12-19
- DOI
- https://doi.org/10.1039/d4dd00337c
- Citations
- 1
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.12
Funders
- National Natural Science Foundation of China