Design of electrocatalysts based on knowledge enhanced LLMs - WANG Ludi; CHEN Ming; CUI Wenjuan (大数据)
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.