Ying Shen1, Shichao Zhao1, Yanfei Lv1, Fei Chen1, Li Fu1,*, Hassan Karimi-Maleh2,*
CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 1921-1950, 2025, DOI:10.32604/cmc.2025.067427
- 03 July 2025
Abstract This review presents a comprehensive and forward-looking analysis of how Large Language Models (LLMs) are transforming knowledge discovery in the rational design of advanced micro/nano electrocatalyst materials. Electrocatalysis is central to sustainable energy and environmental technologies, but traditional catalyst discovery is often hindered by high complexity, fragmented knowledge, and inefficiencies. LLMs, particularly those based on Transformer architectures, offer unprecedented capabilities in extracting, synthesizing, and generating scientific knowledge from vast unstructured textual corpora. This work provides the first structured synthesis of how LLMs have been leveraged across various electrocatalysis tasks, including automated information extraction from literature,… More >
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