关键词:
Solid electrolytes
摘要:
The emergence of large language models has significantly advanced scientific research. Representative models such as ChatGPT and DeepSeek R1 have brought notable changes to the paradigm of scientific research. While these models are general-purpose, they have demonstrated strong generalization capabilities in the field of batteries, especially in solid-state battery research. In this study, we systematically screen 5309268 articles from key journals up to 2024, and accurately extract 124021 papers related to batteries. Additionally, we comprehensively search through 17559750 patent applications and granted patents from the European Patent Office and the United States Patent and Trademark Office up to 2024, identifying 125716 battery-related patents. Utilizing these extensive literature and patents, we conduct numerous experiments to evaluate the structured output capabilities of knowledge base, contextual learning, instruction adherence, and language models. Through multi-dimensional model evaluations and analyses, the following points are found. First, the model exhibits high accuracy in screening literature on inorganic solid-state electrolytes, equivalent to the level of a doctoral student in the relevant field. Based on 10604 data entries, the model demonstrates good recognition capabilities in identifying literature on in-situ polymerization/solidification technology. However, its understanding accuracy for this emerging technology is slightly lower than that for solid-state electrolytes, requiring further fine-tuning to improve accuracy. Second, through testing with 10604 data entries, the model achieves reliable accuracy in extracting inorganic ionic conductivity data. Third, based on solid-state lithium battery patents from four companies in South Korea and Japan over the past 20 years, this model proves effective in analyzing historical patent trends and conducting comparative analyses. Furthermore, the model-generated personalized literature reports based