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.
Development of multivariate metal−organic frameworks (MOFs) as derivatives of the state-of-art water-harvesting material MOF-303 {[Al(OH)(PZDC)], where PZDC 2− is 1Hpyrazole-3,5-dicarboxylate} was shown to be a powerful tool to generate efficient water sorbents tailored to a given environmental condition. Herein, a new multivariate MOF-303-based waterharvesting framework series from readily available reactants is developed. The resulting MOFs exhibit a larger degree of tunability in the operational relative humidity range (16%), regeneration temperature (14 °C), and desorption enthalpy (5 kJ mol −1 ) than reported previously. Additionally, a high-yielding (≥90%) and scalable (∼3.5 kg) synthesis is demonstrated in water and with excellent space-time yields, without compromising framework crystallinity, porosity, and water-harvesting performance.
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