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.

翻译：人工智能与科学研究的融合已达到新高度，GPT-4V（一款通过ChatGPT或API提供增强视觉功能的大型语言模型）正是这一进展的体现。本研究展示了GPT-4V在获取金属有机框架复杂数据方面的卓越能力，尤其擅长从图形来源中提取信息。我们的方法涉及将346篇学术论文自动转换为6240张图像（该任务中的基准数据集），随后利用GPT-4V通过自然语言提示对这些图像进行分类与分析。该技术使GPT-4V能够准确识别并解读MOF表征中的关键图表，包括氮气等温线、PXRD图谱和TGA曲线等，其准确率与召回率均超过93%。模型从这些图表中提取关键信息的能力，不仅突显了其在数据挖掘中的潜力，更表明其能助力构建网状化学的综合性数字数据库。此外，从选定文献中提取的氮气等温线数据允许我们对200余种化合物的理论孔隙率与实验孔隙率进行对比，揭示出某些差异，并强调了整合计算数据与实验数据的重要性。本研究凸显了人工智能在加速科学发现与创新、弥合计算工具与实验研究之间差距、以及推动更高效、包容和全面科学探索方面的潜力。