Harnessing Large Language Model to collect and analyze Metal-organic framework property dataset

Understand

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.