Empowering Polymeric Materials Discovery by Artificial Intelligence

Chenyao Ma; Linda Zhang; Yuheng Chen; Wei Du; Shangwen Fang; Zihao Jiang; Chuanyu Liu; Xinyu Ma

physics.chem-phJun 18, 2026

Empowering Polymeric Materials Discovery by Artificial Intelligence

Authors: Chenyao Ma, Linda Zhang, Yuheng Chen, Wei Du, Shangwen Fang, Zihao Jiang, Chuanyu Liu, Xinyu Ma, +24 more

Organizations: Suzhou MatSource Technology Co., Ltd., Suzhou 215000, Jiangsu, China. · Advanced Institute for Materials Research (WPI-AIMR), Tohoku University, Sendai 980-8577, Japan. · Frontier Research Institute for Interdisciplinary Sciences (FRIS), Tohoku University, Sendai, 980-8577, Japan. · Jiangsu 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. · Gusu Laboratory of Materials, Suzhou 215000, Jiangsu, China. · Department of Chemistry, National University of Singapore, Singapore, Singapore. · Thrust of Sustainable Energy and Environment, The Hong Kong University of Science and Technology (Guangzhou), Guangdong, Guangzhou, 511453, China. · Department of Materials Design and Innovation, University at Buffalo, Buffalo, NY 14260, USA. · College of Smart Materials and Future Energy, State Key Laboratory of Molecular Engineering of Polymers, Fudan University, Shanghai 200433, China. · School of Physical Science and Technology, Shanghai tech University, Shanghai 201210, P.R. China. · The State Key Laboratory of Molecular Engineering of Polymers and Department of Macromolecular Science, Fudan University, Shanghai 200438, People’s Republic of China. · Department of Chemistry and Materials Science, Xi'an J Liverpool University, Suzhou 215123, Jiangsu, P. R. China. · Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, 130022, China. · State Key Laboratory of Advanced Environmental Technology, Department of Environmental Science and Engineering, University of Science and Technology of China, 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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