Empowering Polymeric Materials Discovery by Artificial Intelligence.
14 affiliations
- 1Suzhou MatSource Technology Co., Ltd., Suzhou 215000, Jiangsu, China.
- 2Advanced Institute for Materials Research (WPI-AIMR), Tohoku University, Sendai 980-8577, Japan.
- 3Frontier Research Institute for Interdisciplinary Sciences (FRIS), Tohoku University, Sendai 980-8577, Japan.
- 4Jiangsu Key Laboratory of New Power Batteries, Jiangsu Collaborative Innovation Centre of Biomedical Functional Materials, School of Chemistry and Materials Science, Nanjing Normal University, Nanjing 210023, P. R. China.
- 5Gusu Laboratory of Materials, Suzhou 215000, Jiangsu, China.
- 6Department of Chemistry, National University of Singapore, 3 Science Drive 3, Singapore 117543, Singapore.
- 7Thrust of Sustainable Energy and Environment, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou 511453, Guangdong, China.
- 8Department of Materials Design and Innovation, University at Buffalo, Buffalo, New York 14260, United States.
- 9College of Smart Materials and Future Energy, State Key Laboratory of Molecular Engineering of Polymers, Fudan University, Shanghai 200433, China.
- 10School of Physical Science and Technology, ShanghaiTech University, Shanghai 201210, P. R. China.
- 11The State Key Laboratory of Molecular Engineering of Polymers and Department of Macromolecular Science, Fudan University, Shanghai 200438, P. R. China.
- 12Department of Chemistry and Materials Science, Xi'an J Liverpool University, Suzhou 215123, Jiangsu, P. R. China.
- 13Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, China.
- 14State Key Laboratory of Advanced Environmental Technology, School of Environment, University of Science and Technology of China, Hefei 230026, China.
Abstract
Polymeric materials underpin modern technologies spanning energy storage, microelectronics, healthcare and sustainable manufacturing. Yet their rational design remains exceptionally challenging because material performance emerges from complex interactions among molecular composition, chain architecture, processing history and hierarchical structural evolution across multiple length and time scales. Consequently, polymer research has long relied on labor-intensive experimentation and fragmented modeling approaches, limiting both mechanistic understanding and innovation efficiency. Recent advances in data infrastructure, machine learning, large artificial intelligence (AI) models and laboratory automation are beginning to reshape this landscape. Rather than functioning as isolated tools, polymer databases, predictive models, AI agents and automated laboratories are increasingly converging into interconnected discovery ecosystems. As a result, the central challenge is shifting from improving predictive accuracy alone to enabling reliable decision-making, adaptive learning and seamless integration across computation, experimentation and scientific reasoning. We argue that polymer science is entering an era of autonomous discovery, in which data, simulation, reasoning and experimentation operate within self-improving feedback loops that continuously generate hypotheses, design materials, execute experiments and refine predictive models. By unifying molecular design, process optimization, experimental validation and industrial translation, such autonomous ecosystems establish a more predictive, reproducible and scalable paradigm for polymer innovation, fundamentally transforming how polymer research is conducted.
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BibTeX
@article{ma2026empowering,
title = {{Empowering Polymeric Materials Discovery by Artificial Intelligence.}},
author = {Chenyao Ma and Linda Zhang and Yuheng Chen and Wei Du and Shangwen Fang and Zihao Jiang and Chuanyu Liu and Xinyu Ma and Rui Su and Gang Wang and Muyao Yu and Dong Zhong and Jie Zhu and Weibo Gong and Huan Gu and Limin Li and Chen Shen and Rui Wu and Zhenghao Wu and Kan Xu and Min Zhou and Donglin He and Xiayun Huang and Shan Jiang and Pengfei Ou and Jiayu Peng and Yuwei Zhang and Jie Zhao and Di Zhang and Piao Ma and Zheng-Hao Li and Hao Li},
journal = {JACS Au},
year = {2026},
month = aug,
doi = {10.1021/jacsau.6c01014},
url = {https://europepmc.org/article/MED/42953159},
note = {CC BY},
}
RIS
TY - JOUR
TI - Empowering Polymeric Materials Discovery by Artificial Intelligence.
AU - Chenyao Ma
AU - Linda Zhang
AU - Yuheng Chen
AU - Wei Du
AU - Shangwen Fang
AU - Zihao Jiang
AU - Chuanyu Liu
AU - Xinyu Ma
AU - Rui Su
AU - Gang Wang
AU - Muyao Yu
AU - Dong Zhong
AU - Jie Zhu
AU - Weibo Gong
AU - Huan Gu
AU - Limin Li
AU - Chen Shen
AU - Rui Wu
AU - Zhenghao Wu
AU - Kan Xu
AU - Min Zhou
AU - Donglin He
AU - Xiayun Huang
AU - Shan Jiang
AU - Pengfei Ou
AU - Jiayu Peng
AU - Yuwei Zhang
AU - Jie Zhao
AU - Di Zhang
AU - Piao Ma
AU - Zheng-Hao Li
AU - Hao Li
PY - 2026
DA - 2026/08/01
JO - JACS Au
DO - 10.1021/jacsau.6c01014
UR - https://europepmc.org/article/MED/42953159
AB - Polymeric materials underpin modern technologies spanning energy storage, microelectronics, healthcare and sustainable manufacturing. Yet their rational design remains exceptionally challenging because material performance emerges from complex interactions among molecular composition, chain architecture, processing history and hierarchical structural evolution across multiple length and time scales. Consequently, polymer research has long relied on labor-intensive experimentation and fragmented modeling approaches, limiting both mechanistic understanding and innovation efficiency. Recent advances in data infrastructure, machine learning, large artificial intelligence (AI) models and laboratory automation are beginning to reshape this landscape. Rather than functioning as isolated tools, polymer databases, predictive models, AI agents and automated laboratories are increasingly converging into interconnected discovery ecosystems. As a result, the central challenge is shifting from improving predictive accuracy alone to enabling reliable decision-making, adaptive learning and seamless integration across computation, experimentation and scientific reasoning. We argue that polymer science is entering an era of autonomous discovery, in which data, simulation, reasoning and experimentation operate within self-improving feedback loops that continuously generate hypotheses, design materials, execute experiments and refine predictive models. By unifying molecular design, process optimization, experimental validation and industrial translation, such autonomous ecosystems establish a more predictive, reproducible and scalable paradigm for polymer innovation, fundamentally transforming how polymer research is conducted.
SN - 2691-3704
ER -