LLMs-Powered Accurate Extraction, Querying and Intelligent Management of Literature derived 2D Materials Data
Lijun Shang, Yadong Yu, Wenqiang Kang, Jian Zhou, Dongyue Gao, Pan Xiang, Zhe Liu, Mengyan Dai, Zhonglu Guo, Zhimei Sun
TL;DR
This paper introduces a novel LLM-based system for precise extraction, querying, and intelligent management of 2D materials data from literature, enhancing data accessibility and analysis for researchers.
Contribution
It presents a new LLM-powered framework that automates literature data extraction and management specifically for 2D materials, addressing dispersion and complexity issues.
Findings
Achieved high accuracy in extracting material properties from papers
Enabled efficient querying of 2D materials data
Improved data organization for research applications
Abstract
Two-dimensional (2D) materials have showed widespread applications in energy storage and conversion owning to their unique physicochemical, and electronic properties. Most of the valuable information for the materials, such as their properties and preparation methods, is included in the published research papers. However, due to the dispersion of synthe
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Taxonomy
TopicsMachine Learning in Materials Science · 2D Materials and Applications · Supercapacitor Materials and Fabrication