硅酸盐学报

人工智能范式有机聚合物功能材料的研究与设计进展

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刘秀敏 ¹^,² , 叶财超 ¹

硅酸盐学报 | 综合评述 2026,54(7): 2380-2396

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硅酸盐学报 |综合评述 2026 , 54 (7) : 2380 -2396

人工智能范式有机聚合物功能材料的研究与设计进展

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刘秀敏¹^,², 叶财超¹

作者信息

^1．南方科技大学材料科学与工程系，广东深圳 518055

^2．南京理工大学化学与化工学院，南京 210094

通讯作者:

叶财超（1988—），男，博士，副教授。

作者简介:

刘秀敏（1996—），女，博士研究生。

Research and Design Progress on Organic Polymer Functional Materials under Artificial Intelligence Paradigm

Xiumin LIU¹^,², Caichao YE¹

Affiliations

^1.Department of Materials Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, Guangdong, China

^2.School of Chemistry and Chemical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China

Corresponding Author:

{YE} Caichao (1988-), male, Ph.D., Associate Professor. E-mail: yecc@sustech.edu.cn

LIU Xiumin (1996-), female, Doctoral candidate. E-mail: 1540353710@qq.com

出版时间: 2026-04-10 doi: 10.14062/j.issn.0454-5648.20250944

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摘要

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有机聚合物材料的高维度、非线性的化学空间制约了新材料的研发效率，而人工智能（AI）通过数据驱动模型揭示构效关系，正推动该领域进入高效设计的新阶段。本文系统综述了AI驱动聚合物功能材料研究的新范式。首先，针对数据瓶颈，阐述了高通量计算、机器学习与标准化数据库的协同策略；其次，针对结构表示难题，创新性引入“功能基元”概念，发展了聚合基元指纹（PUFp）与聚合物基元图（PU-Graph）等可解释特征编码方法；进而总结了从宏观工艺参数到微观结构编码的机器学习预测模型进展，并重点剖析了AI在有机半导体、光伏及电介质材料中的典型运用案例，展现了“数据-模型”协同驱动范式转型的现状。本文针对有机聚合物功能材料专用数据库的构建与结构编码方法的梳理，也为第四范式聚合物材料研发提供了数据及方法支撑。最后，展望了AI与材料大数据深度融合的前景，包括逆向设计、实验-模型闭环集成等关键方向。可以预见，随着这些问题的突破，AI将从辅助工具演变为聚合物材料创新的核心引擎。

关键词

人工智能范式 / 有机聚合物功能材料 / 功能基元 / 数据驱动 / 结构-性能关系

Abstract

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Polymer materials, as an important branch of materials science, are widely applied in some fields such as energy, electronics, and aerospace due to their adjustable structures, diverse properties, and low cost. Although polymer functional materials permeate into numerous technical fields, their multi-scale complex behaviors still pose significant challenges for precise design. In this context, the data-driven research paradigm advocated by the Materials Genome Initiative emerges, marking the entry of materials research into the "fourth paradigm" characterized by data-intensive approach. Machine learning as an important branch of artificial intelligence demonstrates outstanding performance prediction and design capabilities in various systems such as alloys, polymer dielectrics, and photovoltaic materials. Although machine learning makes progress in predicting and designing polymer properties, it still faces several challenges, i.e., the scale and quality of data are limited by the costs of synthesis and characterization; the representation of polymer molecules and feature engineering still require the development of more effective encoding methods; and the experimental-computational loop has not yet been fully connected.

This review first, in conjunction with the changes in the research paradigm, systematically explores the progress of AI in the field of organic polymer functional materials. The fourth paradigm centered on data significantly shortens the material research and development cycle and accelerates the screening and design of new materials. Although several review articles summarize the relevant progress of machine learning in the field of polymer material research and design from different perspectives, there is still insufficient discussion on key aspects such as the construction of polymer-specific databases, data-driven characterization of functional units, and model interpretability. Establishing a new research framework centered on artificial intelligence is expected to achieve groundbreaking progress in exploring the vast feature and property space of polymers.

This review represents the general process of applying artificial intelligence to polymer materials, mainly consisting of four stages, i.e., data generation, feature engineering, model selection, and model interpretation. Data quality and scale remain the weakest links in the existing data-driven approaches. The OPECM database, which contains a total of 3225 polymers, is constructed due to the scarcity of available datasets for organic polymer functional materials (such as semiconductors, photovoltaics, and dielectric materials), and the limited chemical diversity of these materials. To address the challenges faced by the polymer representation methods in quantitative structure-property relationship (QSPR) modeling, the "Polymer-Unit" concept is proposed and two novel structure-encoding schemes (i.e., the Polymer Unit Fingerprint (PUFp) and the Polymer-unit Graph) are developed.

Through the lens of database construction, feature representation, and model interpretability, this review highlights machine learning applications in functional polymer design, with some case studies covering semiconductor, photovoltaic, and dielectric materials. PUFp-based framework enables pre-screening and classification of materials via machine learning, offering an efficient alternative route to discovering high-mobility organic semiconductors. The Polymer-unit Graph representation, implemented with graph neural networks (GNNs), provides a visually interpretable and topology-aware approach to polymer modeling.

Summary and Prospects

AI achieves notable progress in polymer functional materials, particularly in inverse design, performance prediction, and process optimization, accelerating the shift from experience-driven to data-driven research. To fully unlock AI's potential, some challenges in data, algorithms, and integration must be systematically addressed. This review represents a dedicated polymer database, a functional unit-based encoding method, and an interpretable structure-performance framework to bridge the gap between "black-box" model outputs and human-understandable chemical insight. Four key directions for future work should be concerned, i.e., 1) building comprehensive, open, and interoperable polymer databases; 2) advancing generative and reinforcement learning for autonomous material discovery; 3) strengthening the dynamic coupling between experiments and models to form self-driving research loops; and 4) fostering interdisciplinary collaboration and integrated platform development. AI can evolve from a supportive tool into a core innovative engine for polymer functional materials, powered by the synergy of data, algorithms and experiments.

Key words

artificial intelligent paradigm / organic polymer functional materials / functional unit / data-driven / structure-property relationship

引用本文

刘秀敏, 叶财超. 人工智能范式有机聚合物功能材料的研究与设计进展. 硅酸盐学报, 2026 , 54 (7) : 2380 -2396 . DOI: 10.14062/j.issn.0454-5648.20250944

Xiumin LIU, Caichao YE. Research and Design Progress on Organic Polymer Functional Materials under Artificial Intelligence Paradigm[J]. Journal of the Chinese Ceramic Society, 2026 , 54 (7) : 2380 -2396 . DOI: 10.14062/j.issn.0454-5648.20250944

基金

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国家自然科学基金面上项目(52473235)
江苏省研究生科研与实践创新计划(KYCX25_0718)

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2026年第54卷第7期

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文章信息

doi: 10.14062/j.issn.0454-5648.20250944

接收时间：2025-12-30
首发时间：2026-10-22
出版时间：2026-04-10

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出版历史

收稿日期：2025-12-30
修回日期：2026-01-25

基金

国家自然科学基金面上项目(52473235)

江苏省研究生科研与实践创新计划(KYCX25_0718)

作者信息

^1．南方科技大学材料科学与工程系，广东深圳 518055

^2．南京理工大学化学与化工学院，南京 210094

通讯作者:

叶财超（1988—），男，博士，副教授。

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https://castjournals.cast.org.cn/joweb/gsyxb/CN/10.14062/j.issn.0454-5648.20250944

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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