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AI4Research: A Survey of Artificial Intelligence for Scientific Research

Created by
  • Haebom

Author

Qiguang Chen, Mingda Yang, Libo Qin, Jinhao Liu, Zheng Yan, Jiannan Guan, Dengyun Peng, Yiyan Ji, Hanjing Li, Mengkang Hu, Yimeng Zhang, Yihao Liang, Yuhang Zhou, Jiaqi Wang, Zhi Chen, Wanxiang Che

Outline

This paper provides a comprehensive survey of the applications of artificial intelligence (AI), particularly large-scale language models (LLMs), to scientific research. While advances in LLMs like OpenAI-o1 and DeepSeek-R1 have led to a surge in research on the application of AI to the innovation process of scientific research, a comprehensive survey of this field has been lacking. This paper presents a systematic taxonomy that categorizes five key challenges in AI-powered research (AI4Research). It highlights key research gaps and promising future directions, focusing on the rigor and scalability of automated experiments and their societal impact. It also compiles a wealth of resources, including relevant multidisciplinary applications, data corpora, and tools.

Takeaways, Limitations

Takeaways:
We provide the first comprehensive survey of the AI4Research field, providing useful information and resources for researchers.
We present a systematic classification system that categorizes the five main tasks of AI4Research.
It presents the future direction of AI4Research, including the rigor and scalability of automated experiments and their social impact.
It provides a wealth of resources, including various applications, data corpora, and tools.
Limitations:
The classification system and future directions presented in this paper may require future revision and update as the field of AI4Research rapidly advances.
The list of resources presented may not be complete and requires ongoing updating.
There may be a lack of in-depth discussion of the ethical and social implications of AI4Research.
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