articleJournal of the American Chemical SocietyJan 21, 2025Closed access

Harnessing Large Language Models to Collect and Analyze Metal–Organic Framework Property Data Set

YKYeonghun KangWLWonseok LeeTBTaeun BaeSHSeunghee HanHJHuiwon Jang

Korea Advanced Institute of Science and Technology

PubMed
Indexed incrossrefpubmed

Abstract

This research focused on the efficient collection of experimental metal-organic framework (MOF) data from scientific literature to address the challenges of accessing hard-to-find data and improving the quality of information available for machine learning studies in materials science. Utilizing a chain of advanced large language models (LLMs), we developed a systematic approach to extract and organize MOF data into a structured format. Our methodology successfully compiled information from more than 40,000 research articles, creating a comprehensive and ready-to-use data set. Specifically, data regarding MOF synthesis conditions and properties were extracted from both tables and text and then analyzed.…

Citation impact

70
total citations
FWCI
29.03
Percentile
100%
References
54
Citations per year

Authors

6

Topics & keywords

Topics
Keywords
  • Chemistry
  • Property (philosophy)
  • Data set
  • Set (abstract data type)
  • Programming language
  • Artificial intelligence
  • Computer science
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