ChatGPT Chemistry Assistant for Text Mining and the Prediction of MOF Synthesis
By Zhiling Zheng, Oufan Zhang, Christian Borgs, Jennifer Tour Chayes, Omar M. Yaghi. Journal of the American Chemical Society, 2023
429 citations recorded by the source corpus.
We use prompt engineering to guide ChatGPT in the automation of text mining of metal–organic framework (MOF) synthesis conditions from diverse formats and styles of the scientific literature. This effectively mitigates ChatGPT’s tendency to hallucinate information, an issue that previously made the use of large language models (LLMs) in scientific fields challenging. Our approach involves the development of a workflow implementing three different processes for text mining, programmed by ChatGPT itself. All of them enable parsing, searching, filtering, classification, summarization, and data unification with different trade-offs among labor, speed, and accuracy. We deploy this system to extract 26 257 distinct synthesis parameters pertaining to approximately 800 MOFs sourced from peer-reviewed research articles. This process incorporates our ChemPrompt Engineering strategy to instruct ChatGPT in text mining, resulting in impressive precision, recall, and F1 scores of 90–99%. Furthermore, with the data set built by text mining, we constructed a machine-learning model with over 87% accuracy in predicting MOF experimental crystallization outcomes and preliminarily identifying important factors in MOF crystallization. We also developed a reliable data-grounded MOF chatbot to answer questions about chemical reactions and synthesis procedures. Given that the process of using ChatGPT reliably mines and tabulates diverse MOF synthesis information in a unified format while using only narrative language requiring no coding expertise, we anticipate that our ChatGPT Chemistry Assistant will be very useful across various other chemistry subdisciplines.
Subfields: Computational Theory and Mathematics, Inorganic Chemistry, Materials Chemistry
View Paper: https://www.semanticscholar.org/paper/b33577b6624da43765d215d9954531e3c7e48e52
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In Inorganic Chemistry
In Materials Chemistry
Citations in the corpus
- Structured information extraction from scientific text with large language models (2024, methodology): “More recently, Zheng et al. 44 designed a prompt-engineering approach (ChemPrompt w/ ChatGPT 45 ) for extracting data from scienti fi c papers.”