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Bulletin of Chinese Academy of Sciences (Chinese Version)

Keywords

artificial intelligence for science (AI4S), scientific research paradigm, human-machine collaboration, supporting framework

Abstract

Artificial intelligence is profoundly transforming the fundamental nature of scientific research, reshaping its modes of knowledge production and organizational operation, driving the emergence of a new artificial intelligence for science (AI4S) research paradigm, and accelerating full-chain innovation paradigm transformation. This study defines the basic connotations of AI4S across three dimensions, namely, enabling applications, tools and methods, and epistemic knowledge, and systematically identifies five core characteristics: human-machine symbiosis, autonomous evolution, interdisciplinary integration, resource intensity, and open ecosystems. It further constructs a supporting system and operational architecture encompassing layers of infrastructure, data resources, model tools, task execution, and application scenarios. Building on this foundation, and in light of China’s practical circumstances and strategic needs, the study proposes a set of policy recommendations: consolidating the intelligent scientific research foundation, building a high-quality scientific data supply system, developing trusted intelligent research tools and an open-source collaboration ecosystem, establishing human-machine collaborative execution mechanisms, strengthening agile and secure governance, and advancing the transformation of scientific research organizational models, to provide theoretical and policy reference for the practice of AI4S.

First page

1206

Last Page

1219

Language

Chinese

Publisher

Bulletin of Chinese Academy of Sciences

References

[1] 浙江大学创新范式研究组. 创新范式：日用而不觉的变革力量. 杭州: 浙江大学出版社, 2024. Innovation Paradigm Research Group, Zhejiang University. Innovation Paradigms: The Transformative Forces Hidden in Plain Sight. Hangzhou: Zhejiang University Press, 2024. (in Chinese)

[2] 李国杰. 智能化科研（AI4R）：第五科研范式. 中国科学院院刊, 2024, 39(1): 1-9. Li G J. AI4R: The fifth scientific research paradigm. Bulletin of Chinese Academy of Sciences, 2024, 39(1): 1-9. (in Chinese)

[3] 中国科学院学部人工智能咨询研究组. 人工智能赋能科学研究：人工智能学科体系. 北京: 科学出版社, 2026. Artificial Intelligence Consultation Research Group of the Academic Divisions of the Chinese Academy of Sciences. Artificial Intelligence for Science: The Disciplinary System of Artificial Intelligence. Beijing: Science Press, 2026. (in Chinese)

[4] 中国科学技术信息研究所. AI for Science创新图谱. 北京: 中国科学技术信息研究所, 2025. Institute of Scientific and Technical Information of China. Innovation Map of AI for Science. Beijing: Institute of Scientific and Technical Information of China, 2025. (in Chinese)

[5] Wang H, Fu T, Du Y, et al. Scientific discovery in the age of artificial intelligence. Nature, 2023, 620: 47-60.

[6] Jumper J, Evans R, Pritzel A, et al. Highly accurate protein structure prediction with AlphaFold. Nature, 2021, 596: 583-589.

[7] Lam R, Sanchez-Gonzalez A, Willson M, et al. Learning skillful medium-range global weather forecasting. Science, 2023, 382: 1416-1421.

[8] 中国科学院创新范式研究组. 加快人工智能驱动的知识创新体系建设. 中国科学院院刊, 2025, 40(10): 1709-1719. Innovation Paradigm Research Group, Chinese Academy of Sciences. Accelerating the development of AI-driven knowledge innovation system. Bulletin of Chinese Academy of Sciences, 2025, 40(10): 1709-1719. (in Chinese)

[9] Chen K, Fang S, Li H, et al. More papers, fewer breakthroughs: Can AI reshape scientific discovery? The Innovation, 2026, 7(5): 101316.

[10] OECD. 科学中的人工智能：挑战、机遇和未来展望. 陈凯华, 译. 北京: 科学出版社, 2024. OECD. Artificial intelligence in Science: Challenges, Opportunities, and the Future of Research. Translated by Chen K H. Beijing: Science Press, 2024. (in Chinese)

[11] 鄂维南. AI助力打造科学研究新范式. 中国科学院院刊, 2024, 39(1): 10-16. E W N. AI helps to establish a new paradigm for scientific research. Bulletin of Chinese Academy of Sciences, 2024, 39(1): 10-16. (in Chinese)

[12] 杨小康, 许岩岩, 陈露, 等. AI for Science：智能化科学设施变革基础研究. 中国科学院院刊, 2024, 39(1): 59-69. Yang X K, Xu Y Y, Chen L, et al. AI for Science: Transforming fundamental research with intelligent scientific facilities. Bulletin of Chinese Academy of Sciences, 2024, 39(1): 59-69. (in Chinese)

[13] Butler K T, Davies D W, Cartwright H, et al. Machine learning for molecular and materials science. Nature, 2018, 559: 547-555.

[14] Bergen K J, Johnson P A, de Hoop M V, et al. Machine learning for data-driven discovery in solid Earth geoscience. Science, 2019, 363: eaau0323.

[15] Karagiorgi G, Kasieczka G, Kravitz S, et al. Machine learning in the search for new fundamental physics. Nature Reviews Physics, 2022, 4: 399-412.

[16] Krenn M, Pollice R, Guo S Y, et al. On scientific understanding with artificial intelligence. Nature Reviews Physics, 2022, 4: 761-769.

[17] Raccuglia P, Elbert K C, Adler P D F, et al. Machine-learning-assisted materials discovery using failed experiments. Nature, 2016, 533: 73-76.

[18] Stokes J M, Yang K, Swanson K, et al. A deep learning approach to antibiotic discovery. Cell, 2020, 180(4): 688-702.

[19] Davies A, Veličković P, Buesing L, et al. Advancing mathematics by guiding human intuition with AI. Nature, 2021, 600: 70-74.

[20] Swanson K, Wu W, Bulaong N L, et al. The Virtual Lab of AI agents designs new SARS-CoV-2 nanobodies. Nature, 2025, 646: 716-723.

[21] 陈凯华, 方思清, 李赫扬. 人工智能嵌入知识生产的功能边界. 科学学研究, 2026, doi： 10.16192/j.cnki.1003-2053.20260601.002. Chen K H, Fang S Q, Li H Y. The functional boundaries of artificial Intelligence’s embedding in knowledge production. Studies in Science of Science, 2026, doi: 10.16192/j.cnki.1003-2053.20260601.002. (in Chinese)

[22] Kattel R, Lember V, Tõnurist P. Collaborative innovation and human-machine networks. Public Management Review, 2020, 22(11): 1652-1673.

[23] Reichstein M, Camps-Valls G, Stevens B, et al. Deep learning and process understanding for data-driven Earth system science. Nature, 2019, 566: 195-204.

[24] Chen M, Qian Z, Boers N, et al. Collaboration between artificial intelligence and Earth science communities for mutual benefit. Nature Geoscience, 2024, 17: 949-952.

[25] 陈凯华, 刘泓欣, 郭锐. 我国加强支持科研智能数据库建设的意义、挑战与建议. 中国科学院院刊, 2026, 41(1): 194-204. Chen K H, Liu H X, Guo R. Significance, challenges, and policy recommendations for strengthening database development to support AI for Science in China. Bulletin of Chinese Academy of Sciences, 2026, 41(1): 194-204. (in Chinese)

[26] 廖方宇, 汪洋, 曹荣强, 等. 第五科研范式下新型科研信息化基础平台架构与关键技术. 中国科学院院刊, 2024, 39(12): 2048-2059. Liao F Y, Wang Y, Cao R Q, et al. Architecture and key technologies of new research informatization infrastructure platform under the fifth research paradigm. Bulletin of Chinese Academy of Sciences, 2024, 39(12): 2048-2059. (in Chinese)

[27] 陈凯华, 杨一帆 ,徐海涛, 等. 创新管理科学与工程——复杂创新研究与管理的理论与方法.管理世界, 2025, 41(11): 1-31. Chen K H, Yang Y F, Xu H T, et al. Innovation management science and engineering: Theory and methodology of complex innovation research and management. Management World, 2025, 41(11): 1-31. (in Chinese)

[28] 陈凯华. 数字化背景下的创新发展与管理理论. 北京: 科学出版社, 2024. Chen K H. Innovation Development and Management Theory in the Digital Context. Beijing: Science Press, 2024. (in Chinese)

[29] 李赫扬, 方思清, 陈凯华. 人工智能驱动的科学学理论与方法重塑. 科学学研究, 2026, 44(1): 4-13. Li H Y, Fang S Q, Chen K H. Artificial Intelligence - driven theoretical and methodological transformation of the Science of Science. Studies in Science of Science, 2026, 44(1): 4-13. (in Chinese)

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