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We use prompt engineering to guide ChatGPT in the automation of text miningof metal-organic frameworks (MOFs) synthesis conditions from diverse formatsand styles of the scientific literature. This effectively mitigates ChatGPT'stendency to hallucinate information -- an issue that previously made the use ofLarge Language Models (LLMs) in scientific fields challenging. Our approachinvolves the development of a workflow implementing three different processesfor text mining, programmed by ChatGPT itself. All of them enable parsing,searching, filtering, classification, summarization, and data unification withdifferent tradeoffs between labor, speed, and accuracy. We deploy this systemto extract 26,257 distinct synthesis parameters pertaining to approximately 800MOFs sourced from peer-reviewed research articles. This process incorporatesour 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 amachine-learning model with over 86% accuracy in predicting MOF experimentalcrystallization outcomes and preliminarily identifying important factors in MOFcrystallization. We also developed a reliable data-grounded MOF chatbot toanswer questions on chemical reactions and synthesis procedures. Given that theprocess of using ChatGPT reliably mines and tabulates diverse MOF synthesisinformation in a unified format, while using only narrative language requiringno coding expertise, we anticipate that our ChatGPT Chemistry Assistant will bevery useful across various other chemistry sub-disciplines.

ChatGPT Chemistry Assistant for Text Mining and Prediction of MOF Synthesis