Authors
Yongjun Xu, Zezhi Shao, Xin Liu, Yuquan Li, Zhulin An, Chenguang Fu, Changping Huang, Lin Zhang, Feng Gao, Jian Li, Yuyang Li, Fanglong Yao, Anquan Xia, Kaihua Chen, Xiaolong Zheng, Chuanguang Yang, Libo Huang, Fengliang Dong, Shanliang Yao, Zhao Zhang, Jue Wang, Qi Wang, Yuanzhuo Wang, Haisheng Li, Nachuan Xiao, Yuyang Wang, Guoshuang Feng, Puxiang Lai, Yuxuan Song, Tao Xu, Jie Yang, Yun Peng, Xiangbin Meng, Xin Peng, Kunjie Li, Yupeng Niu, Tiancheng Xu, Huiyu Xu, Tao Huang, Yong Han, Min Li, Jiayun Hou, Xiaoling Lu, Fangrong Yan, Yang Zhao, Yongyue Wei, Ming-Jie Yin, Zongguo Wang, Leilei Xiang, Yubao Qiu, Jiangyuan Zeng, Yang Liu, Yue Chang, Sabine Grunwald, Willie Peijnenburg, Thilo Hofmann, Ronald Roepman, Jun Li, Gerd Dercon, Lianru Gao, Mariana Vezzone, Marko Virta, Sabine Dietmann, Deshuai Yuan, Fei Chen, Wei Chen, Kai Wu, Tao Chen, Haibo Zhang, Zuoqiang Yuan, Jifeng Han, Xuetao Zhu, Bin Zhang, Wentao Su, Wei Meng, Ming Zhang, Dachuan Zhang, Qingyan Wu, Xiaojie Lin, Xiayao Peng, Tao Yang, Wei Wu, Lei Gan, Meng Wan, Ye Ding, Zhongbao Wei, Shaohua Yang, Yunfei Zhang, Lijun Yang, Kun Yu, Yuanyuan Duan, Xingying Chen, Peng Jia, Ali Luo, Jiaben Lin, Cunshi Wang, Zhiting Zhang, Xinhe Liu, Dongchen Zhu, Peng Hu, Tong Li, Jinran Wu, Ram Dalal, Yang Wu, Chengqing Yu, Zixuan Li, Weijie Zhu, Tianyou Lu, Lijun Yun, Thorsten Jelinek, Wenguan Wang, Tiange Liu, Fei Wang, Heyang Li, Shimin Di, Boyu Diao, Xiangfeng Wang, Xin Cao, Zhigang Yin, Fang Wang, Dake Zhang, Ming Luo, Sen Qian, Ping Yu, Xingchen Liu, Jun Shen, Zhengyang Li, Ji Dai, Yi Lin, You-Gan Wang, Geoffrey John McLachlan, Mohamed Missous, Matthias C Rillig, Ravi Naidu, Wulf Amelung, James M Tiedje, Chuang Shi, Yanfen Wang, Bing Zhang, Jiabao Zhang
Published in
Innovation (Cambridge (Mass.)). Volume 7. Issue 11. Pages 101530. Nov 02, 2026. Epub Aug 05, 2026.
Abstract
By 2026, artificial intelligence (AI) has completed a qualitative transition from specialized analytical tools to a foundational engine for scientific inquiry, fundamentally altering the paradigms of knowledge generation. In this review, we systematically evaluate the progress of AI for Science (AI4Science), characterized by the shift from text-centric generation to reasoning-oriented intelligence and the deep embedding of foundation models into the entire research life cycle. We propose a unified, closed-loop research paradigm comprising six core modules: literature research, data analysis, hypothesis formulation, experimental execution, report generation, and autonomous evolution. Furthermore, we synthesize breakthroughs across a broad spectrum of frontier domains-including information science, mathematics, energy, physics, chemistry, materials science, medicine, life sciences, astronomy, geosciences, and social science-highlighting AI's capacity for overcoming dimensionality constraints and enabling complex inverse design. We then identify critical challenges that persist, such as the scarcity of standardized high-quality data, the interpretability gap, and the physical consistency of generative outputs. Finally, we offer forward-looking perspectives on constructing cross-disciplinary scientific foundation models and fostering a future of human-AI collaborative autonomous research.
PMID:
43011738
Bibliographic data and abstract were imported from PubMed on 11 Nov 2026.
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