-
大语言模型的出现极大地推动了科学研究的进步. 以ChatGPT为代表的语言模型和DeepSeek R1为代表的推理模型, 为科研范式带来了显著变革. 尽管这些模型均为通用型, 但它们在电池领域, 尤其是固态电池的研究中, 展现出强大的泛化能力. 本研究系统性地筛选了2024年及之前重点期刊中的5309268篇文章, 精准提取了124021篇电池相关文献. 同时, 我们全面检索了欧洲专利局与美国专利局2024年及以前的申请与授权专利, 共计17559750篇, 从中筛选出125716篇电池相关专利. 利用这些文献与专利, 对语言模型的知识储备、实时学习、指令遵从和结构化输出能力进行了大量实验. 通过多维度的模型评估与分析发现: 当前的大语言模型在信息分类和数据提取等的精度基本达到了研究生水平, 语言模型在内容总结和趋势分析方面也展现出强大的能力. 同时, 我们也发现模型在极少数情况下可能出现数值幻觉问题. 而在处理电池领域海量数据时, 模型在工程应用方面仍存在优化空间. 我们根据模型的特点和以上测试结果, 利用模型提取了无机固态电解质材料数据, 包括离子电导率数据5970条、扩散系数数据387条、迁移势垒数据3094条, 此外还包括1000多条化学、电化学、力学等数据, 涵盖了无机固态电解质所涉及的几乎所有物理、化学、电化学性质, 这也意味着大语言模型对科研的应用已经从辅助科研转向主动促进科研发展阶段. 本文数据集可在中国科学院凝聚态物质科学数据中心查看, 网址
https://cmpdc.iphy.ac.cn/literature/SSE.html (DOI:https://doi.org/10.57760/sciencedb.j00213.00172 ).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 on the latest publications also show high accuracy. Fourth, by utilizing the iterative strategy of the model, we enable DeepSeek to engage in self-reflection thinking, thereby providing more comprehensive responses. The research results indicate that language models possess strong capabilities in content summarization and trend analysis. However, we also observe that the model may occasionally experience issues with numerical hallucinations. Additionally, while processing a large number of battery-related data, there is still room for optimization in engineering applications. According to the characteristics of the model and the above test results, we utilize the DeepSeek V3-0324 model to extract data on inorganic solid electrolyte materials, including 5970 ionic conductivity entries, 387 diffusion coefficient entries, and 3094 migration barrier entries. Additionally, it includes over 1000 data entries related to chemical, electrochemical, and mechanical properties, covering nearly all physical, chemical, and electrochemical properties related to inorganic solid electrolytes. This also means that the application of large language models in scientific research has shifted from auxiliary research to actively promoting its development. The datasets presented in this paper may be available at the website:https://cmpdc.iphy.ac.cn/literature/SSE.html (DOI:https://doi.org/10.57760/sciencedb.j00213.00172 ).-
Keywords:
- soild state battery /
- artificial intelligence /
- natural language process /
- materials database
[1] ChatGPT Website https://chatgpt.com/
[2] DeepSeek-AI 2025 arXiv: 2501.12948 [cs.CL]
[3] Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez A N, Kaiser L, Polosukhin I 2017 arXiv: 1706.03762 [cs.CL]
[4] Radford A, Narasimhan K, Salimans K, Sutskever I 2018 OpenAI Blog
[5] Devlin J, Chang M W, Lee K, Toutanova K 2018 arXiv: 1810.04805 [cs.CL]
[6] Radford A, Wu J, Child R, Luan D, Amodei D, Sutskever I 2019 OpenAI Blog
[7] Brown T B, Mann B, Ryder N, Subbiah M, Kaplan J, Dhariwal P, Neelakantan A, Shyam P, Sastry G, Askell A, Agarwal S, Herbert-Voss A, Krueger G, Henighan T, Child R, Ramesh A, Ziegler D M, Wu J, Winter C, Hesse C, Chen M, Sigler E, Litwin M, Gray S, Chess B, Clark J, Berner C, McCandlish S, Radford A, Sutskever I, Amodei D 2020 arXiv: 2005.14165 [cs.CL]
[8] Wei J, Wang X Z, Schuurmans D, Bosma M, Ichter B, Xia F, Chi E, Le Q, Zhou D 2022 arXiv: 2201.11903 [cs.CL]
[9] OpenAI o1 Hub https://openai.com/zh-Hans-CN/o1/
OpenAI o1 Hub https://openai.com/zh-Hans-CN/o1/
[10] Yang A, Yang B S, Hui B Y, Zheng B, Yu B W, Zhou C, Li C P, Li C Y, Liu D Y H, Huang F, Dong G T, Wei H R, Lin H, Tang J L, Wang J L, Yang J, Tu J H, Zhang J W, Ma J X, Xu J, Zhou J R, Bai J Z, He J Z, Lin J Y, Dang K, Lu K M, Chen K Q, Yang K X, Li M, Xue M F, Ni N, Zhang P, Wang P, Peng R, Men R, Gao R Z, Lin R J, Wang S J, Bai S, Tan S N, Zhu T H, Li T H, Liu T Y, Ge W B, Deng X D, Zhou X H, Ren X Z, Zhang X Y, Wei X P, Ren X C, Fan Y, Yao Y, Zhang Y C, Wan Y, Chu Y F, Cui Z Y, Zhang Z Y, Fan Z H 2024 arXiv: 2407.10671 [cs.CL]
[11] Zeng A H, Xu B, Wang B W, Zhang C H, Yin D, Zhang D, Rojas D, Feng G Y, Zhao H L, Lai H Y, Yu H, Wang H N, Sun J D, Zhang J J, Cheng J L, Gui J Y, Tang J, Zhang J, Sun J Y, Li J Z, Zhao L, Wu L D, Zhong L C, Liu M D, Huang M L, Zhang P, Zheng Q K, Lu R, Duan S Q, Zhang S D, Cao S L, Yang S X, Tam W L, Zhao W Y, Liu X, Xia X, Zhang X H, Gu X T, Lü X, Liu X H, Liu X Y, Yang X Y, Song X X, Zhang X K, An Y F, Xu Y F, Niu Y L, Yang Y T, Li Y Y, Bai Y S, Dong Y X, Qi Z H, Wang Z Y, Yang Z, Du Z X, Hou Z Y, Wang Z H 2024 arXiv: 2406.12793 [cs.CL]
[12] Gemini 2.5 Pro Gemini官网 https://gemini.google.com/app
[13] Chen J L, Xiao S T, Zhang P T, Luo K, Lian D F, Liu Z 2024 arXiv: 2402.03216 [cs.CL]
[14] MatElab平台https://in.iphy.ac.cn/eln/#/recusertype
[15] Lewis P, Perez E, Piktus A, Petroni F, Karpukhin V, Goyal N, Küttler H, Lewis M, Yih W T, Rocktäschel T, Riedel S, Kiela D 2000 arXiv: 2005.11401 [cs.CL]
[16] 吴思远, 王宇琦, 肖睿娟, 陈立泉, 2020 69 226104
Google Scholar
Wu S Y, Wang Y Q, Xiao R J, Chen L Q 2020 Acta Phys. Sin. 69 226104
Google Scholar
[17] 离子输运数据库 http://e01.iphy.ac.cn/bmd
[18] Xiao R J, Li H, Chen L Q 2015 Sci. Rep. 5 14227
Google Scholar
[19] He B, Chi S T, Ye A J, Mi P H, Zhang L W, Pu B W, Zou Z Y, Ran Y B, Zhao Q, Wang D, Zhang W Q, Zhao J T, Adams S, Avdeev M, Shi S Q 2020 Sci. Data 7 151
Google Scholar
[20] Yang F L, dos Santos E C, Jia X, Sato R, Kisu K, Hashimoto Y, Orimo S, Li H 2024 Nano Mater. Sci. 6 256
Google Scholar
[21] Hargreaves C J, Gaultois M W, Daniels L M, Watts E J, Kurlin V A, Moran M, Dang Y, Morris R, Axandra M, Thompson K, Wright M A, Beluvalli-Eshwarappa P, Blanc F, Collins C M, Crawford C A, Duff B B, Evans J, Gamon J, Han G P, Leube B T, Niu H J, Perez A J, Robinson A, Rogan O, Sharp P A, Shoko E, Sonni M, Thomas W J, Vasylenko A, Wang L, Rosseinsky M J, Dyer M S 2023 npj Comput. Mater. 9 9
Google Scholar
[22] Wu S Y, Zhu T N, Tu S J, Xiao R J, Yuan J, Wu Q S, Li H, Weng H M 2024 Chin. Phys. B 33 050704
Google Scholar
[23] 无机固态电解质材料数据库https://cmpdc.iphy.ac.cn/literature/SSE.html
[24] Zhang Y, Xie M X, Zhang W, Yan J L, Shao G Q 2020 Mater. Lett. 266 127508
Google Scholar
[25] Li Y X, Daikuhara S, Hori S, Sun X Y, Suzuki K, Hirayama M, Kanno R 2020 Chem. Mater. 32 8860
Google Scholar
[26] Xu R C, Wu Z, Zhang S Z, Wang X L, Xia Y, Xia X H, Huang X H, Tu J P 2017 Chem. Eur. J. 23 13950
Google Scholar
[27] Li X L, Peng W X, Tian R Z, Song D W, Wang Z Y, Zhang H Z, Zhu L Y, Zhang L Q 2020 Electrochim. Acta 363 137185
Google Scholar
[28] Wu F, Fitzhugh W, Ye L H, Ning J X, Li X 2018 Nat. Commun. 9 4037
Google Scholar
-
图 1 本工作主要考核目标(左侧)与设计的实验(右侧)
Fig. 1. The main assessment objectives (left) and the designed experiment (right) of this work.
图 2 DeepSeek R1, ChatGPT o3 min和Gemini 2.0对同样内容的总结比较
Fig. 2. Comparison of summaries generated by DeepSeek R1, ChatGPT o3 min, and Gemini 2.0 on the same content.
图 3 DeepSeek R1分析日韩4家企业过去20年固态电池专利
Fig. 3. Analysis of solid-state battery patents from four Japanese and South Korean companies over the past 20 years by DeepSeek R1
图 4 DeepSeek R1分析每日电池文献(左图)和电池新闻(右图)
Fig. 4. Analysis of solid-state battery literatures (left subfigure) and news (right subfigure) by DeepSeek R1.
图 5 DeepSeek R1 70B未挂载知识库(左图)和挂载知识库(右图)在回答同一问题的差异
Fig. 5. DeepSeek R1 70B without knowledge base mounted (left) and with knowledge base mounted (right) showing differences in responses to the same question.
图 6 DeepSeek R1 70B Deep Research流程(左图)和优化后输出(右图)
Fig. 6. Workflow of deep research of DeepSeek R1 70B (left) and answer by it (right).
图 7 无机固态电解质数据库构建流程
Fig. 7. Workflow of constructing inorganic solid electrolyte database.
表 1 无机固态电解质文献分类准确性
Table 1. Accuracy of literature classification for inorganic solid electrolytes.
MatElab模型 DeepSeek R1 QwQ 32B 精确性 0.859 0.883 0.781 召回率 0.850 0.823 0.827 F1 0.854 0.854 0.801 
下载: 导出CSV
表 2 原位固化技术文献分类准确性
Table 2. Accuracy of literature classification for in-situ solidification technology.
微调后的Qwen2-7B-Instruct DeepSeek R1 精确性 0.406 0.950 召回率 0.833 0.653 F1 0.576 0.774
下载: 导出CSV
-
[1] ChatGPT Website https://chatgpt.com/
[2] DeepSeek-AI 2025 arXiv: 2501.12948 [cs.CL]
[3] Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez A N, Kaiser L, Polosukhin I 2017 arXiv: 1706.03762 [cs.CL]
[4] Radford A, Narasimhan K, Salimans K, Sutskever I 2018 OpenAI Blog
[5] Devlin J, Chang M W, Lee K, Toutanova K 2018 arXiv: 1810.04805 [cs.CL]
[6] Radford A, Wu J, Child R, Luan D, Amodei D, Sutskever I 2019 OpenAI Blog
[7] Brown T B, Mann B, Ryder N, Subbiah M, Kaplan J, Dhariwal P, Neelakantan A, Shyam P, Sastry G, Askell A, Agarwal S, Herbert-Voss A, Krueger G, Henighan T, Child R, Ramesh A, Ziegler D M, Wu J, Winter C, Hesse C, Chen M, Sigler E, Litwin M, Gray S, Chess B, Clark J, Berner C, McCandlish S, Radford A, Sutskever I, Amodei D 2020 arXiv: 2005.14165 [cs.CL]
[8] Wei J, Wang X Z, Schuurmans D, Bosma M, Ichter B, Xia F, Chi E, Le Q, Zhou D 2022 arXiv: 2201.11903 [cs.CL]
[9] OpenAI o1 Hub https://openai.com/zh-Hans-CN/o1/
OpenAI o1 Hub https://openai.com/zh-Hans-CN/o1/
[10] Yang A, Yang B S, Hui B Y, Zheng B, Yu B W, Zhou C, Li C P, Li C Y, Liu D Y H, Huang F, Dong G T, Wei H R, Lin H, Tang J L, Wang J L, Yang J, Tu J H, Zhang J W, Ma J X, Xu J, Zhou J R, Bai J Z, He J Z, Lin J Y, Dang K, Lu K M, Chen K Q, Yang K X, Li M, Xue M F, Ni N, Zhang P, Wang P, Peng R, Men R, Gao R Z, Lin R J, Wang S J, Bai S, Tan S N, Zhu T H, Li T H, Liu T Y, Ge W B, Deng X D, Zhou X H, Ren X Z, Zhang X Y, Wei X P, Ren X C, Fan Y, Yao Y, Zhang Y C, Wan Y, Chu Y F, Cui Z Y, Zhang Z Y, Fan Z H 2024 arXiv: 2407.10671 [cs.CL]
[11] Zeng A H, Xu B, Wang B W, Zhang C H, Yin D, Zhang D, Rojas D, Feng G Y, Zhao H L, Lai H Y, Yu H, Wang H N, Sun J D, Zhang J J, Cheng J L, Gui J Y, Tang J, Zhang J, Sun J Y, Li J Z, Zhao L, Wu L D, Zhong L C, Liu M D, Huang M L, Zhang P, Zheng Q K, Lu R, Duan S Q, Zhang S D, Cao S L, Yang S X, Tam W L, Zhao W Y, Liu X, Xia X, Zhang X H, Gu X T, Lü X, Liu X H, Liu X Y, Yang X Y, Song X X, Zhang X K, An Y F, Xu Y F, Niu Y L, Yang Y T, Li Y Y, Bai Y S, Dong Y X, Qi Z H, Wang Z Y, Yang Z, Du Z X, Hou Z Y, Wang Z H 2024 arXiv: 2406.12793 [cs.CL]
[12] Gemini 2.5 Pro Gemini官网 https://gemini.google.com/app
[13] Chen J L, Xiao S T, Zhang P T, Luo K, Lian D F, Liu Z 2024 arXiv: 2402.03216 [cs.CL]
[14] MatElab平台https://in.iphy.ac.cn/eln/#/recusertype
[15] Lewis P, Perez E, Piktus A, Petroni F, Karpukhin V, Goyal N, Küttler H, Lewis M, Yih W T, Rocktäschel T, Riedel S, Kiela D 2000 arXiv: 2005.11401 [cs.CL]
[16] 吴思远, 王宇琦, 肖睿娟, 陈立泉, 2020 69 226104
Google Scholar
Wu S Y, Wang Y Q, Xiao R J, Chen L Q 2020 Acta Phys. Sin. 69 226104
Google Scholar
[17] 离子输运数据库 http://e01.iphy.ac.cn/bmd
[18] Xiao R J, Li H, Chen L Q 2015 Sci. Rep. 5 14227
Google Scholar
[19] He B, Chi S T, Ye A J, Mi P H, Zhang L W, Pu B W, Zou Z Y, Ran Y B, Zhao Q, Wang D, Zhang W Q, Zhao J T, Adams S, Avdeev M, Shi S Q 2020 Sci. Data 7 151
Google Scholar
[20] Yang F L, dos Santos E C, Jia X, Sato R, Kisu K, Hashimoto Y, Orimo S, Li H 2024 Nano Mater. Sci. 6 256
Google Scholar
[21] Hargreaves C J, Gaultois M W, Daniels L M, Watts E J, Kurlin V A, Moran M, Dang Y, Morris R, Axandra M, Thompson K, Wright M A, Beluvalli-Eshwarappa P, Blanc F, Collins C M, Crawford C A, Duff B B, Evans J, Gamon J, Han G P, Leube B T, Niu H J, Perez A J, Robinson A, Rogan O, Sharp P A, Shoko E, Sonni M, Thomas W J, Vasylenko A, Wang L, Rosseinsky M J, Dyer M S 2023 npj Comput. Mater. 9 9
Google Scholar
[22] Wu S Y, Zhu T N, Tu S J, Xiao R J, Yuan J, Wu Q S, Li H, Weng H M 2024 Chin. Phys. B 33 050704
Google Scholar
[23] 无机固态电解质材料数据库https://cmpdc.iphy.ac.cn/literature/SSE.html
[24] Zhang Y, Xie M X, Zhang W, Yan J L, Shao G Q 2020 Mater. Lett. 266 127508
Google Scholar
[25] Li Y X, Daikuhara S, Hori S, Sun X Y, Suzuki K, Hirayama M, Kanno R 2020 Chem. Mater. 32 8860
Google Scholar
[26] Xu R C, Wu Z, Zhang S Z, Wang X L, Xia Y, Xia X H, Huang X H, Tu J P 2017 Chem. Eur. J. 23 13950
Google Scholar
[27] Li X L, Peng W X, Tian R Z, Song D W, Wang Z Y, Zhang H Z, Zhu L Y, Zhang L Q 2020 Electrochim. Acta 363 137185
Google Scholar
[28] Wu F, Fitzhugh W, Ye L H, Ning J X, Li X 2018 Nat. Commun. 9 4037
Google Scholar
下载:
计量
- 文章访问数: 490
- PDF下载量: 30
- 被引次数: 0
