Research on key technologies of artificial intelligence for research and development

doi:10.12267/j.issn.2096-5931.2025.08.006

Abstract

Abstract:

The integration of Artificial Intelligence (AI) with research and development is reshaping the paradigms of both scientific inquiry and industrial research and development, heralding a new era of research methodologies. This paper systematically examines five core technological areas within the "AI for research and development" framework, namely research data, research computing power, research models, research agents, and automated laboratories. It explores their definitions, connotations, current development status, and future trends, while discussing how AI drives scientific research toward autonomous intelligence. The findings indicate that the convergence of research data governance, heterogeneous computing resource management, specialized and multimodal research models, the autonomous research capabilities of intelligent agents, and the wide-area collaboration of automated laboratories collectively supports a novel intelligent research paradigm characterized by an "imagination-execution" closed loop. This paradigm provides robust momentum for accelerating scientific discoveries and facilitating their industrial translation.

Key words: AI, artificial intelligence for research and development, AI4R&D, artificial intelligence for science, AI4S

CLC Number:

TP18
G350.7

DONG Hao, ZHOU Jingcai. Research on key technologies of artificial intelligence for research and development[J]. Information and Communications Technology and Policy, 2025, 51(8): 42-49.

Add to citation manager EndNote|Ris|BibTeX

URL:

http://ictp.caict.ac.cn/EN/10.12267/j.issn.2096-5931.2025.08.006

http://ictp.caict.ac.cn/EN/Y2025/V51/I8/42

Figures/Tables 1

References 33

[1]	中国人工智能产业发展联盟. 2024科研智能(AI4R&D):人工智能驱动的研发新范式[R], 2024.
[2]	HEY T. The fourth paradigm: data-intensive scientific discovery[M]//KURBANOĞLU S, AL U, ERDOĞAN P L, et al. E-Science and Information Management: Communications in Computer and Information Science. Berlin: Springer, 2012.
[3]	李国杰. 智能化科研(AI4R):第五科研范式[J]. 中国科学院院刊, 2024, 39(1):1-9.
[4]	中国科学技术信息研究所. AI for Science创新图谱[R], 2025.
[5]	JUMPER J, EVANS R, PRITZEL A, et al. Highly accurate protein structure prediction with AlphaFold[J]. Nature, 2021, 596(7873):583-589.
[6]	张婧睿, 孙蒙鸽, 韩涛. 科研智能化趋势下科研数据研究[J]. 科学观察, 2023, 18(4):49-61. doi: 10.15978/j.cnki.1673-5668.202304007
[7]	NOACK M M, ZWART P H, USHIZIMA D M, et al. Gaussian processes for autonomous data acquisition at large-scale synchrotron and neutron facilities[J]. Nature Reviews Physics, 2021,3:685-697.
[8]	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[J]. 中国科学院院刊, 2024, 39(12):2048-2059.
[9]	SZYMANSKI N J, RENDY B, FEI Y X, et al. An autonomous laboratory for the accelerated synthesis of novel materials[J]. Nature, 2023, 624(7990):86-91.
[10]	National Center for Biotechnology Information. Current genbank release notes[EB/OL]. (2025-06-15)[2025-06-20]. https://www.ncbi.nlm.nih.gov/genbank/release/current/.
[11]	Semantic Scholar. Semantic scholar academic graph API[EB/OL]. 2025[2025-06-20]. https://www.semanticscholar.org/product/api.
[12]	OpenAlex. What content does OpenAlex include and how does it compare to other databases?[EB/OL]. 2025[2025-06-20]. https://help.openalex.org/hc/en-us/articles/27190301279127-What-content-does-OpenAlex-include-and-how-does-it-compare-to-other-databases.
[13]	AMiner. AMiner开放文档[EB/OL]. 2025[2025-06-20]. https://www.aminer.cn/open/docs.
[14]	TAYLOR R, KARDAS M, CUCURULL G, et al. Galactica: a large language model for science[J]. arXiv Preprint, arXiv:2211.09085, 2022.
[15]	ZHANG D, HU Z, ZHOUBIAN S, et al. SciGLM: training scientific language models with self-reflective instruction annotation and tuning[J]. arXiv Preprint, arXiv:2401.07950, 2024.
[16]	LUO R, SUN L, XIA Y, et al. BioGPT: generative pre-trained transformer for biomedical text generation and mining[J]. Briefings in Bioinformatics, 2022, 23(6):bbac409.
[17]	SINGHAL K, TU T, GOTTWEIS J, et al. Toward expert-level medical question answering with large language models[J]. Nature Medicine, 2025,31:943-950.
[18]	ZHAO Z, MA D, CHEN L, et al. ChemDFM: a large language foundation model for chemistry[J]. arXiv Preprint, arXiv:2401.14818, 2024.
[19]	ZHANG D, LIU W, TAN Q, et al. CheMLLM: a chemical large language model[J]. arXiv Preprint, arXiv:2402.06852, 2024.
[20]	BI K, XIE L, ZHANG H, et al. Accurate medium-range global weather forecasting with 3D neural networks[J]. Nature, 2023, 619(7970):533-538.
[21]	ZHANG K, ZHOU R, ADHIKARLA E, et al. A generalist vision-language foundation model for diverse biomedical tasks[J]. Nature Medicine, 2024,30:3129-3141.
[22]	ZHANG S, XU Y, USUYAMA N, et al. Biomedclip: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs[J]. arXiv Preprint, arXiv:2303.00915, 2023.
[23]	MORO V, LOH C, DANGOVSKI R, et al. Multimodal foundation models for material property prediction and discovery[J]. arXiv Preprint, arXiv:2312.00111, 2025.
[24]	GUO X, LAO J, DANG B, et al. SkySense: a multi-modal remote sensing foundation model towards universal interpretation for earth observation imagery[C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle: IEEE, 2024:27672-27683.
[25]	LU C, LU C, LANGE R T, et al. The AI scientist: towards fully automated open-ended scientific discovery[J]. arXiv Preprint, arXiv:2408.06292, 2024.
[26]	GOTTWEIS J, WENG W H, DARYIN A, et al. Towards an AI co-scientist[J]. arXiv Preprint, arXiv:2502.18864, 2025.
[27]	BRAN M A, COX S, SCHILTER O, et al. Augmenting large language models with chemistry tools[J]. Nature Machine Intelligence, 2024, 6(5):525-535. doi: 10.1038/s42256-024-00832-8 pmid: 38799228
[28]	QIU H, ZHAO J, JING E, et al. Introducing polySea: an LLM-based pymer smart evolution agent[J]. ChemRxiv, 2025.
[29]	LI N, GAO C, LI M, et al. EconAgent: large language model-empowered agents for simulating macroeconomic activities[J]. arXiv Preprint, arXiv:2310.10436, 2023.
[30]	PIAO J, YAN Y, ZHANG J, et al. AgentSociety: large-scale simulation of LLM-driven generative agents advances understanding of human behaviors and society[J]. arXiv Preprint, arXiv:2502.08691, 2025.
[31]	BURGER B, MAFFETTONE P M, GUSEV V V, et al. A mobile robotic chemist[J]. Nature, 2020, 583(7815):237-241.
[32]	ZHU Q, ZHANG F, HUANG Y, et al. An all-round AI-Chemist with a scientific mind[J]. National Science Review, 2022, 9(10):nwac190.
[33]	STRIETH-KALTHOFF F, HAO H, RATHORE V, et al. Delocalized, asynchronous, closed-loop discovery of organic laser emitters[J]. Science, 2024, 384(6697):eadk9227.