Image and Data Mining in Reticular Chemistry Using GPT-4V
Zhiling Zheng Zhiguo He Omar Khattab Nakul Rampal Matei A. Zaharia Christian Borgs Jennifer T. Chayes Omar M. Yaghi
The integration of artificial intelligence into scientific research has
reached a new pinnacle with GPT-4V, a large language model featuring enhanced
vision capabilities, accessible through ChatGPT or an API. This study
demonstrates the remarkable ability of GPT-4V to navigate and obtain complex
data for metal-organic frameworks, especially from graphical sources. Our
approach involved an automated process of converting 346 scholarly articles
into 6240 images, which represents a benchmark dataset in this task, followed
by deploying GPT-4V to categorize and analyze these images using natural
language prompts. This methodology enabled GPT-4V to accurately identify and
interpret key plots integral to MOF characterization, such as nitrogen
isotherms, PXRD patterns, and TGA curves, among others, with accuracy and
recall above 93%. The model's proficiency in extracting critical information
from these plots not only underscores its capability in data mining but also
highlights its potential in aiding the creation of comprehensive digital
databases for reticular chemistry. In addition, the extracted nitrogen isotherm
data from the selected literature allowed for a comparison between theoretical
and experimental porosity values for over 200 compounds, highlighting certain
discrepancies and underscoring the importance of integrating computational and
experimental data. This work highlights the potential of AI in accelerating
scientific discovery and innovation, bridging the gap between computational
tools and experimental research, and paving the way for more efficient,
inclusive, and comprehensive scientific inquiry.