From AI for Science to Agentic Science: A Survey on Autonomous
  Scientific Discovery
Artificial intelligence (AI) is reshaping scientific discovery, evolving from
specialized computational tools into autonomous research partners. We position
Agentic Science as a pivotal stage within the broader AI for Science paradigm,
where AI systems progress from partial assistance to full scientific agency.
Enabled by large language models (LLMs), multimodal systems, and integrated
research platforms, agentic AI shows capabilities in hypothesis generation,
experimental design, execution, analysis, and iterative refinement -- behaviors
once regarded as uniquely human. This survey provides a domain-oriented review
of autonomous scientific discovery across life sciences, chemistry, materials
science, and physics. We unify three previously fragmented perspectives --
process-oriented, autonomy-oriented, and mechanism-oriented -- through a
comprehensive framework that connects foundational capabilities, core
processes, and domain-specific realizations. Building on this framework, we (i)
trace the evolution of AI for Science, (ii) identify five core capabilities
underpinning scientific agency, (iii) model discovery as a dynamic four-stage
workflow, (iv) review applications across the above domains, and (v) synthesize
key challenges and future opportunities. This work establishes a
domain-oriented synthesis of autonomous scientific discovery and positions
Agentic Science as a structured paradigm for advancing AI-driven research.
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Jiaqi Wei
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2025-08-21T09:15:38.233Z
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Jiaqi Wei
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Yuejin Yang
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Xiang Zhang
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Yuhan Chen
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Xiang Zhuang
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Zhangyang Gao
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2025-08-21T10:32:34.526Z
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Zhangyang Gao
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ZhangyangGao
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Dongzhan Zhou
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2025-08-21T10:32:42.215Z
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Dongzhan Zhou
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Guangshuai Wang
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2025-08-21T10:32:48.394Z
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GuangshuaiWang
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Wanggsh
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Zhiqiang Gao
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Juntai Cao
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Zijie Qiu
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2025-08-21T10:33:11.907Z
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qiu
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zijieqiu
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Xuming He
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Qiang Zhang
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Chenyu You
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2025-08-21T10:33:46.203Z
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Chenyu You
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Charlesyooo
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Shuangjia Zheng
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2025-08-21T10:33:53.289Z
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Shuangjia Zheng
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AbHunter
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Ning Ding
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2025-09-13T14:57:14.891Z
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Ning Ding
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stingning
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Wanli Ouyang
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Nanqing Dong
admin_assigned
2025-08-21T10:34:08.444Z
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Nanqing Dong
user
eveningdong
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Yu Cheng
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Siqi Sun
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Lei Bai
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Bowen Zhou
From AI for Science to Agentic Science: A Survey on Autonomous
  Scientific Discovery
Artificial intelligence (AI) is reshaping scientific discovery, evolving from
specialized computational tools into autonomous research partners. We position
Agentic Science as a pivotal stage within the broader AI for Science paradigm,
where AI systems progress from partial assistance to full scientific agency.
Enabled by large language models (LLMs), multimodal systems, and integrated
research platforms, agentic AI shows capabilities in hypothesis generation,
experimental design, execution, analysis, and iterative refinement -- behaviors
once regarded as uniquely human. This survey provides a domain-oriented review
of autonomous scientific discovery across life sciences, chemistry, materials
science, and physics. We unify three previously fragmented perspectives --
process-oriented, autonomy-oriented, and mechanism-oriented -- through a
comprehensive framework that connects foundational capabilities, core
processes, and domain-specific realizations. Building on this framework, we (i)
trace the evolution of AI for Science, (ii) identify five core capabilities
underpinning scientific agency, (iii) model discovery as a dynamic four-stage
workflow, (iv) review applications across the above domains, and (v) synthesize
key challenges and future opportunities. This work establishes a
domain-oriented synthesis of autonomous scientific discovery and positions
Agentic Science as a structured paradigm for advancing AI-driven research.
Agentic Science leverages large language models, multimodal systems, and integrated platforms to enable autonomous scientific discovery across various domains, encompassing hypothesis generation, experimental design, execution, analysis, and iterative refinement.
large language models multimodal systems integrated research platforms hypothesis generation experimental design scientific agency process-oriented autonomy-oriented mechanism-oriented domain-specific realizations dynamic four-stage workflow