Harnessing Large Language Model to collect and analyze Metal-organic framework property dataset
arxiv(2024)
摘要
This research was 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 dataset. The
findings highlight the significant advantage of incorporating experimental data
over relying solely on simulated data for enhancing the accuracy of machine
learning predictions in the field of MOF research.
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