Construction of a knowledge graph for framework material enabled by large language models and its application

IF 9.4 1区 材料科学 Q1 CHEMISTRY, PHYSICAL
Xuefeng Bai, Song He, Yi Li, Yabo Xie, Xin Zhang, Wenli Du, Jian-Rong Li
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Abstract

Framework materials (FMs) have been extensively investigated with a plethora of literature documenting their unique properties and potential applications. Despite this, a comprehensive knowledge graph for this emerging field has not yet been constructed. In this study, by utilizing the natural language processing capabilities of large language models (LLMs), we have established a comprehensive knowledge graph (KG-FM). It covers synthesis, properties, applications, and other aspects of FMs including metal-organic frameworks (MOFs), covalent-organic frameworks (COFs), and hydrogen-bonded organic frameworks (HOFs). The knowledge graph was constructed through the analysis of over 100,000 articles, resulting in 2.53 million nodes and 4.01 million relationships. Subsequently, its application has been explored for enhancing data retrieval, mining, and the development of sophisticated question-answering systems. Especially when integrating the KGs with LLMs, resulted Qwen2-KG not only achieves a higher accuracy rate of 91.67% in question-answering than existing models but also provides precise information sources.

Abstract Image

基于大型语言模型的框架材料知识图谱的构建及其应用
框架材料(FMs)已被广泛研究,大量文献记录了其独特的性质和潜在的应用。尽管如此,这一新兴领域的全面知识图谱尚未构建。在本研究中,我们利用大型语言模型(llm)的自然语言处理能力,建立了一个综合知识图(KG-FM)。它涵盖了金属有机框架(MOFs)、共价有机框架(COFs)和氢键有机框架(HOFs)等金属有机框架的合成、性质、应用和其他方面。通过对超过10万篇文章的分析,构建了知识图谱,得到253万个节点和401万个关系。随后,将其应用于增强数据检索、挖掘和复杂问答系统的开发。特别是当将KGs与llm相结合时,Qwen2-KG不仅在问答中获得了比现有模型更高的91.67%的准确率,而且提供了精确的信息源。
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来源期刊
npj Computational Materials
npj Computational Materials Mathematics-Modeling and Simulation
CiteScore
15.30
自引率
5.20%
发文量
229
审稿时长
6 weeks
期刊介绍: npj Computational Materials is a high-quality open access journal from Nature Research that publishes research papers applying computational approaches for the design of new materials and enhancing our understanding of existing ones. The journal also welcomes papers on new computational techniques and the refinement of current approaches that support these aims, as well as experimental papers that complement computational findings. Some key features of npj Computational Materials include a 2-year impact factor of 12.241 (2021), article downloads of 1,138,590 (2021), and a fast turnaround time of 11 days from submission to the first editorial decision. The journal is indexed in various databases and services, including Chemical Abstracts Service (ACS), Astrophysics Data System (ADS), Current Contents/Physical, Chemical and Earth Sciences, Journal Citation Reports/Science Edition, SCOPUS, EI Compendex, INSPEC, Google Scholar, SCImago, DOAJ, CNKI, and Science Citation Index Expanded (SCIE), among others.
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