抽象的

Material Property Prediction with Joint Reasoning based on Large Language Models and Knowledge Graphs for Lithium Batteries

Gang Lei1, Ziyu Yin1, Jianmao Xiao1*, Xinji Qiu2, Yuanlong Cao1, Qian Zhang3, Musheng Wu2

Lithium batteries, as a crucial part of modern energy storage, rely heavily on the properties of their materials, which affect energy density, cycle life, charge/dis-charge rates, and safety. Traditional experimental methods for predicting these properties are often costly and time-consuming. While data-driven machine learning approaches can predict performance by analysing the impact of various factors, inconsistencies in data measurement and reporting, along with a lack of semantic integration of material information, hinder research on lithium battery materials. This paper introduces a lithium battery material property prediction method based on Joint Reasoning with Large Language models and Knowledge Graphs (JRLKG). Knowledge relationships are extracted from extensive literature to construct a knowledge graph encompassing material structure, properties, and research methods. By assessing material similarity within the graph, related information is fed into the GPT-4 model. Prompt learning techniques guide GPT-4 to leverage explicit knowledge from the external knowledge graph and its implicit knowledge for joint reasoning predictions, providing results and rationales. Experiments show that JRLKG improves the accuracy and interpretability of material property predictions, offering new avenues and methods for research and shortening the Research and development cycle for lithium-ion battery materials.