We use prompt engineering to guide ChatGPT in the automation of text mining
of metal-organic frameworks (MOFs) 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 tradeoffs between 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 dataset built by text mining, we constructed a
machine-learning model with over 86% 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 on 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 sub-disciplines.

采用指令工程来指导 ChatGPT 在多样化的科学文献格式和风格中自动化地进行金属有机骨架合成条件的文本挖掘，该方法有效缓解了 ChatGPT 在科学领域中使用大型语言模型困难的问题，从而实现了令人印象深刻的准确性和精度。