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Can AI become a discoverer of chemical knowledge?
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Jun JIANG1, 2, Chengxing CUI2, 3, Wenguang HUANG4
Science & Technology Review | 2025, 43(21) : 16 - 22
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Science & Technology Review | 2025, 43(21): 16-22
• Commentary •
Can AI become a discoverer of chemical knowledge?
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Jun JIANG1, 2, Chengxing CUI2, 3, Wenguang HUANG4
Affiliations
  • 1Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei 230026, China
  • 2Institute of Intelligent Innovation, Henan Academy of Sciences, Zhengzhou 451162, China
  • 3School of Chemistry and Chemical Engineering (Institute of Computational Chemistry), Henan Institute of Science and Technology, Xinxiang 453003, China
  • 4Editorial Department of Science & Technology Review, Beijing 100081, China
Published: 2025-11-13 doi: 10.3981/j.issn.1000-7857.2025.07.00034
Outline
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Artificial intelligence (AI) is driving a paradigm shift in scientific research—from functioning primarily as an "accelerator" to emerging as a genuine "discoverer." Using the "Machine Chemist" platform at the University of Science and Technology of China as an illustrative example, this work provides a systematic analysis of the potential and challenges of AI in chemical knowledge discovery. Through machine learning, knowledge graphs, and automated experimental systems, AI can achieve a true transition from data to knowledge in areas such as molecular design, spectroscopic analysis, catalyst screening, and materials development. However, for AI to become an autonomous discoverer of chemical knowledge, three critical bottlenecks must be addressed: The scarcity of high−quality data, the limitations of human cognitive frameworks, and the low efficiency of experimental validation. This study further examines how chemical foundation models, multimodal data integration, and industrial−scale intelligent laboratories can drive systematic transformation of future scientific research paradigms by enabling data−driven decision optimization, accelerating interdisciplinary research, and restructuring automated experimental workflows.

artificial intelligence  /  machine chemist  /  chemical foundation model  /  interpretability  /  knowledge discovery
Jun JIANG, Chengxing CUI, Wenguang HUANG. Can AI become a discoverer of chemical knowledge?[J]. Science & Technology Review, 2025 , 43 (21) : 16 -22 . DOI: 10.3981/j.issn.1000-7857.2025.07.00034
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Year 2025 volume 43 Issue 21
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Article Info
doi: 10.3981/j.issn.1000-7857.2025.07.00034
  • Receive Date：2025-07-07
  • Online Date：2025-12-29
  • Published：2025-11-13
Article Data
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History
  • Received：2025-07-07
  • Revised：2025-10-19
Funding
Affiliations
    1Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei 230026, China
    2Institute of Intelligent Innovation, Henan Academy of Sciences, Zhengzhou 451162, China
    3School of Chemistry and Chemical Engineering (Institute of Computational Chemistry), Henan Institute of Science and Technology, Xinxiang 453003, China
    4Editorial Department of Science & Technology Review, Beijing 100081, China
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