Knowledge discovery from porous organic cage literature using a large language model

Abstract

Porous organic cages (POCs) are an emerging subclass of porous materials, drawing increasing attention due to their structural tunability, modularity and processibility, with the research in this area rapidly expanding. Nevertheless, it is a time-consuming and labour-intensive process to obtain sufficient information from the extensive literature on organic molecular cages. This article presents a GPT-4-based literature reading method that incorporates multi-label text classification and a follow-up information extraction, in which the potential of GPT-4 can be fully exploited to rapidly extract valid information from the literature. In the process of multi-label text classification, the prompt-engineered GPT-4 demonstrated the ability to label text with proper recall rates according to the type of information contained in the text, including authors, affiliations, synthetic procedures, surface area, and the Cambridge Crystallographic Data Centre (CCDC) number of corresponding cages. Additionally, GPT-4 demonstrated proficiency in information extraction, effectively transforming labeled text into concise tabulated data. Furthermore, we built a chatbot based on this database, allowing for quick and comprehensive searching across the entire database and responding to cage-related questions.