AI4Research: A Survey of Artificial Intelligence for Scientific Research
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

TL;DR
This survey comprehensively reviews AI applications in scientific research, categorizing tasks, identifying research gaps, and providing resources to foster future innovations in AI-driven research methodologies.
Contribution
It introduces a systematic taxonomy of AI4Research tasks, highlights future research directions, and compiles valuable resources and applications for the scientific community.
Findings
Classified five main AI4Research tasks
Identified key research gaps and future directions
Compiled extensive resources and applications
Abstract
Recent advancements in artificial intelligence (AI), particularly in large language models (LLMs) such as OpenAI-o1 and DeepSeek-R1, have demonstrated remarkable capabilities in complex domains such as logical reasoning and experimental coding. Motivated by these advancements, numerous studies have explored the application of AI in the innovation process, particularly in the context of scientific research. These AI technologies primarily aim to develop systems that can autonomously conduct research processes across a wide range of scientific disciplines. Despite these significant strides, a comprehensive survey on AI for Research (AI4Research) remains absent, which hampers our understanding and impedes further development in this field. To address this gap, we present a comprehensive survey and offer a unified perspective on AI4Research. Specifically, the main contributions of our work…
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Taxonomy
TopicsMachine Learning in Materials Science · Topic Modeling · Scientific Computing and Data Management
