大数据2025，Vol.11Issue(2)：47-54,8.DOI:10.11959/j.issn.2096-0271.2025028
基于知识增强大模型的电催化剂设计
Design of electrocatalysts based on knowledge enhanced LLMs
摘要
Abstract
As an important means of achieving sustainable carbon cycling,developing high-performance electrocatalysts is the key to the sustainable development in future,and recommending innovative and valuable preparation solutions is one of the effective ways to improve the efficiency of electrocatalytic development.This paper is based on scientific and technological literature in the field of electrocatalysis,inviting domain experts to construct a knowledge system and extract knowledge,forming a domain knowledge base.In addition,this paper utilizes literature data to fine tune and enhance knowledge of universal big language models,jointly completing preparation scheme recommendations for target products,material categories,and regulation method categories,and assisting in the design of electrocatalysts.Experimental results have shown that the preparation solutions provided by knowledge enhanced large language models have certain improvements in both effectiveness and innovation.关键词
电催化剂/知识增强/大模型Key words
electrocatalysts/knowledge enhancement/large language model分类
计算机与自动化引用本文复制引用
王露笛,陈鸣,崔文娟..基于知识增强大模型的电催化剂设计[J].大数据,2025,11(2):47-54,8.基金项目
中国科学院重点部署项目(No.RCJJ-145-24-20) Key Research Program of the Chinese Academy of Sciences(No.RCJJ-145-24-20) （No.RCJJ-145-24-20）
