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A special issue of Polymers (ISSN 2073-4360). This special issue belongs to the section "Artificial Intelligence in Polymer Science".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 16670

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Special Issue Editors

Dr. Ming-Xiao Zhu

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Guest Editor

College of New Energy, China University of Petroleum (East China), Qingdao, China
Interests: dielectric polymers; polymer nanocomposites; multi-scale simulation of polymer properties; artificial intelligence for polymer design
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Guochang Li

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Guest Editor

School of Materials Science and Engineering, Qingdao University of Science and Technology, Qingdao 266042, China
Interests: dielectric polymers; polymer composites; silicone rubber
Special Issues, Collections and Topics in MDPI journals

Dr. Jianyi Xue

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Guest Editor

School of Electrical Engineering and Automation, Hefei University of Technology, Hefei, China
Interests: gas–solid interface insulation; cable insulation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Polymers and polymer-based composites are used in a wide range of engineering applications. However, due to the vastness of the chemical and structural space, traditional intuition-driven and/or trial-and-error approaches used to discover polymers with target properties are expensive and time-consuming. With the advanced development of artificial intelligence technologies, the use of machine learning (ML) has shown great potential in data-driven design and the discovery of polymers, also known as polymer informatics. This Special Issue aims to delve into recent advancements in ML-assisted design of polymers and polymer-based composites with exceptional properties such as electrical insulation, thermal stability and mechanical strength. Topics of interest cover various aspects of polymer informatics including polymer dataset generation (experiments and high-throughput computations), machine-readable fingerprinting of polymer structures, ML algorithms describing structure–property relationships, and rational design protocols.

Dr. Ming-Xiao Zhu
Prof. Dr. Guochang Li
Dr. Jianyi Xue
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Polymers is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

polymer
composite
structure–property relationships
insulation property
thermal behavior
mechanical strength
artificial intelligence
machine learning
rational design

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Published Papers (10 papers)

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Research

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36 pages, 5169 KB

Open AccessArticle

A Statistically Grounded and Physics-Aware Vision Framework for Detecting Barely Visible Impact Damage (BVID) in Heterogeneous Polymer-Matrix Composites

by Gönenç Duran

Polymers 2026, 18(10), 1240; https://doi.org/10.3390/polym18101240 - 19 May 2026

Viewed by 429

Abstract

Barely Visible Impact Damage (BVID) in heterogeneous polymer-matrix composites remains difficult to detect because subtle damage signatures are often masked by complex architectures, hybrid textures, and overlapping failure morphologies. This study therefore presents an experimentally grounded, physics-aware, and statistically validated vision-based inspection framework rather than a purely detector-centered benchmarking exercise. Real post-impact images were obtained from controlled low-velocity impact experiments on 20 composite architectures and 60 physical specimens, yielding approximately 2000 images across laminated, hybrid, textile-reinforced, and sandwich structures. The dataset was organized using a specimen-disjoint splitting protocol to prevent leakage across training, validation, and test subsets. To improve robustness while preserving physical realism, a physically grounded Albumentations strategy was developed using only physically admissible transformations and explicit exclusion of non-physical operations that could distort damage morphology or surface continuity. Model development was further complemented by a hybrid hardware workflow in which cloud-based GPU training was combined with deployment-oriented inference profiling on resource-constrained edge-like hardware, thereby linking detection accuracy to practical industrial feasibility. In addition, model performance was evaluated under a standardized training budget and validated through repeated runs, Friedman significance testing, and Holm-corrected Wilcoxon signed-rank pairwise comparisons to ensure error-controlled interpretation of inter-model differences. Across the evaluated compact YOLO families, YOLO26s delivered the strongest overall performance, reaching 0.841 mAP@0.5, 0.586 ± 0.004 mAP@0.5:0.95, and an F1-score of 0.809, while YOLO11s achieved the highest precision and YOLO26n remained competitive in recall with nano-level compactness. Overall, the results show that experimentally generated heterogeneous composite data, morphology-preserving augmentation strategy development, leakage-aware dataset design, deployment-oriented computational profiling, and statistically grounded validation together provide a more robust and application-relevant basis for automated BVID detection in polymer-matrix composite structures. Full article

(This article belongs to the Special Issue Artificial Intelligence in Polymers)

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19 pages, 2528 KB

Open AccessArticle

AI-Based Polymer Classification Using Ensemble Deep Learning and Heuristic Optimization: Implications for Recycling Applications

by Mohammad Anwar Parvez

Polymers 2026, 18(10), 1208; https://doi.org/10.3390/polym18101208 - 15 May 2026

Viewed by 401

Abstract

Polymer-based product use is rapidly increasing worldwide, resulting in critical social, environmental, ecological, economic, and health effects. Worldwide efforts have increasingly focused on solutions to the equilibrium consumption, production, and disposal of plastics to tackle these issues. The frontiers of biodegradable and bio-based polymers are continually advancing in pursuit of sustainability. Therefore, designing ecological bioplastics made of both biodegradable and bio-based polymers reveals chances to overcome plastic pollution and resource depletion. Polymeric materials are mainly used to manufacture different products at the beginning of their lifespans and which become waste after usage. Numerous sustainability strategies and polymer recycling methods are described and mostly classified into chemical, mechanical, and thermal recycling processes. This manuscript presents a New Polymers Frontier in Recycling and Sustainability Using an Ensemble of Deep Learning with a Heuristic Search Algorithm (NPFRS-EDLHSA). This work is devoted to computational polymer typology, which is based on machine learning algorithms applied to data on physicochemical properties. Although polymer classification can facilitate downstream materials research, the present study does not directly simulate recycling, environmental impacts, or sustainability. The main contributions made by this work include (i) an exploratory analysis of ensemble deep learning models to classify polymers by type on a small and unbalanced dataset; (ii) an evaluation of the effect of feature selection with a heuristic optimization methodology; and (iii) a comparison of the effects on classification performance under limited data conditions. This research sets out to provide a methodological explanation, not arguments for industrial-scale applicability. For the polymer-type classification process, the proposed NPFRS-EDLHSA model designs an ensemble of deep learning techniques, namely a bidirectional recurrent neural network (BiRNN) model, a bidirectional gated recurrent unit (BiGRU) method, and a graph autoencoder (GAE) technique. Finally, the grasshopper optimization algorithm (GOA) adjusts the hyperparameter values of the ensemble models optimally and results in an improved classification performance. A wide-ranging set of experiments was conducted to validate the performance of the NPFRS-EDLHSA method. The experimental results indicated that the NPFRS-EDLHSA technique achieved a better performance than an existing model. Full article

(This article belongs to the Special Issue Artificial Intelligence in Polymers)

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24 pages, 6273 KB

Open AccessArticle

Manufacturing-Induced Defect Taxonomy and Visual Detection in UD Tapes with Carbon and Glass Fiber Reinforcements

by Gönenç Duran

Polymers 2026, 18(7), 807; https://doi.org/10.3390/polym18070807 - 26 Mar 2026

Cited by 1 | Viewed by 676

Abstract

Continuous unidirectional (UD) thermoplastic composite tapes are increasingly used in aerospace, automotive, and energy applications because of their high specific strength, low weight, recyclability, and compatibility with automated manufacturing. Since final component performance strongly depends on tape quality, reliable defect characterization and detection are essential. In this study, manufacturing-induced defects in polypropylene-based UD tapes reinforced with carbon and glass fibers were investigated using real images acquired directly from laboratory-scale production without synthetic data. Defects related to interfacial integrity, matrix distribution, fiber architecture, and surface irregularities were systematically analyzed, and a practical four-class defect taxonomy was established. To enable automated inspection under limited-data conditions, lightweight YOLOv8, YOLOv11, and the new YOLO26 models were comparatively evaluated using a UD tape-specific augmentation strategy combining physically constrained Albumentations and on-the-fly augmentation. Among the tested models, YOLO26-s achieved the best overall performance, reaching a mean mAP@0.5 of 0.87 ± 0.03, outperforming YOLOv11 (0.83) and YOLOv8 (0.78), with 0.90 precision and 0.85 recall. Interfacial (0.92 mAP) and matrix-related (0.90 mAP) defects were detected most reliably, whereas fiber-related (0.89 mAP) and surface defects (0.79 mAP) remained more challenging, particularly in glass-fiber-reinforced tapes due to transparency-masking effects. The results demonstrate the potential of compact deep learning models for computationally efficient and manufacturing-relevant in-line quality monitoring of UD tape production. Full article

(This article belongs to the Special Issue Artificial Intelligence in Polymers)

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25 pages, 1853 KB

Open AccessArticle

Deep Learning for Process Monitoring and Defect Detection of Laser-Based Powder Bed Fusion of Polymers

by Mohammadali Vaezi, Victor Klamert and Mugdim Bublin

Polymers 2026, 18(5), 629; https://doi.org/10.3390/polym18050629 - 3 Mar 2026

Viewed by 993

Abstract

Maintaining consistent part quality remains a critical challenge in industrial additive manufacturing, particularly in laser-based powder bed fusion of polymers (PBF-LB/P), where crystallization-driven thermal instabilities, governed by isothermal crystallization within a narrow sintering window, precipitate defects such as curling, warping, and delamination. In contrast to metal-based systems dominated by melt-pool hydrodynamics, polymer PBF-LB/P requires monitoring strategies capable of resolving subtle spatio-temporal thermal deviations under realistic industrial operating conditions. Although machine learning, particularly convolutional neural networks (CNNs), has demonstrated efficacy in defect detection, a structured evaluation of heterogeneous modeling paradigms and their deployment feasibility in polymer PBF-LB/P remains limited. This study presents a systematic cross-paradigm assessment of unsupervised anomaly detection (autoencoders and generative adversarial networks), supervised CNN classifiers (VGG-16, ResNet50, and Xception), hybrid CNN-LSTM architectures, and physics-informed neural networks (PINNs) using 76,450 synchronized thermal and RGB images acquired from a commercial industrial system operating under closed control constraints. CNN-based models enable frame- and sequence-level defect classification, whereas the PINN component complements detection by providing physically consistent thermal-field regression. The results reveal quantifiable trade-offs between detection performance, temporal robustness, physical consistency, and algorithmic complexity. Pre-trained CNNs achieve up to 99.09% frame-level accuracy but impose a substantial computational burden for edge deployment. The PINN model attains an RMSE of approximately 27 K under quasi-isothermal process conditions, supporting trend-level thermal monitoring. A lightweight hybrid CNN achieves 99.7% validation accuracy with 1860 parameters and a CPU-benchmarked forward-pass inference time of 1.6 ms (excluding sensor acquisition latency). Collectively, this study establishes a rigorously benchmarked, scalable, and resource-efficient deep-learning framework tailored to crystallization-dominated polymer PBF-LB/P, providing a technically grounded basis for real-time industrial quality monitoring. Full article

(This article belongs to the Special Issue Artificial Intelligence in Polymers)

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21 pages, 2265 KB

Open AccessArticle

An Ensemble Learning Model for Aging Assessment of Silicone Rubber Considering Multifunctional Group Comprehensive Analysis

by Kun Zhang, Chuyan Zhang, Zhenan Zhou, Zheyuan Liu, Yu Deng, Chen Gu, Songsong Zhou, Dongxu Sun, Hongli Liu and Xinzhe Yu

Polymers 2025, 17(22), 2988; https://doi.org/10.3390/polym17222988 - 10 Nov 2025

Cited by 2 | Viewed by 897

Abstract

With the widespread deployment of high-voltage and ultra-high-voltage transmission lines, composite insulators play a vital role in modern power systems. However, prolonged service leads to material aging, and the current lack of standardized, quantitative methods for evaluating silicone rubber degradation poses significant challenges for condition-based maintenance. To address this measurement gap, we propose a novel aging assessment framework that integrates Fourier Transform Infrared (FTIR) spectroscopy with a measurement-oriented ensemble learning model. FTIR is utilized to extract absorbance peak areas from multiple aging-sensitive functional groups, forming the basis for quantitative evaluation. This work establishes a measurement-driven framework for aging assessment, supported by information-theoretic feature selection to enhance spectral relevance. The dataset is augmented to 4847 samples using linear interpolation to improve generalization. The proposed model employs k-nearest neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), and Gradient-Boosting Decision Tree (GBDT) within a two-tier ensemble architecture featuring dynamic weight allocation and a class-balanced weighted cross-entropy loss. The model achieves 96.17% accuracy and demonstrates strong robustness under noise and anomaly disturbances. SHAP analysis confirms the resistance to overfitting. This work provides a scalable and reliable method for assessing silicone rubber aging, contributing to the development of intelligent, data-driven diagnostic tools for electrical insulation systems. Full article

(This article belongs to the Special Issue Artificial Intelligence in Polymers)

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26 pages, 6153 KB

Open AccessArticle

Industrial Vegetable Oils: A Green Alternative for Enhancing Rubber Properties

by Julijana Žeravica, Olga Govedarica, Mirjana Jovičić, Sonja Stojanov and Dragan Govedarica

Polymers 2025, 17(21), 2898; https://doi.org/10.3390/polym17212898 - 30 Oct 2025

Cited by 2 | Viewed by 1263

Abstract

This study investigates the viability of industrial hempseed oil as a sustainable extender oil in rubber compounding, addressing the urgent demand for alternatives to petroleum-based oils due to regulatory pressures on polycyclic aromatic hydrocarbons (PAH). We employed automated neural networks to analyze the physical and mechanical properties of rubber composites containing industrial hempseed oil, comparing them with six vegetable oils and three petroleum-based oils at extender oil concentrations from 0 to 30 phr. The results revealed that compounds with 20 phr of industrial hempseed oil and raw soybean oil exhibited the highest cure rate index values of 64.32 1/min. Rubber samples with industrial hempseed oil showed a significant 18% reduction in hardness compared to conventional oils, with the softest rubber measuring 40.5 Shore A hardness at 30 phr. Additionally, energy consumption during mixing was decreased by up to 12% for vegetable oil samples compared to mineral oils, enhancing processing efficiency. The neural network approach yielded more accurate predictions of the cure rate index, Shore A hardness, and power consumption during rubber mixing, with a validation performance exceeding 99.2%. Sensitivity analysis identified key factors, including oil content and surface tension, influencing rubber hardness. Overall, this study underscores the potential of industrial hempseed oil as an effective, eco-friendly substitute for conventional mineral oils, contributing to more sustainable practices in the rubber industry. Full article

(This article belongs to the Special Issue Artificial Intelligence in Polymers)

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16 pages, 2638 KB

Open AccessArticle

Use of Artificial Neural Networks for Recycled Pellets Identification: Polypropylene-Based Composites

by Maya T. Gómez-Bacab, Aldo L. Quezada-Campos, Carlos D. Patiño-Arévalo, Zenen Zepeda-Rodríguez, Luis A. Romero-Cano and Marco A. Zárate-Navarro

Polymers 2025, 17(17), 2349; https://doi.org/10.3390/polym17172349 - 29 Aug 2025

Viewed by 1572

Abstract

Polymer recycling is challenging due to practical classification difficulties. Even when the polymer matrix is identified, the presence of various polymeric composites complicates their accurate classification. In this study, Fourier-transform infrared spectroscopy (ATR-FTIR) was used in combination with artificial neural networks (ANNs) to quantitatively predict the mineral filler content in polypropylene (PP) composites. Calibration curves were developed to correlate ATR-FTIR spectral features (600–1700 cm⁻¹) with the concentration (wt.%) of three mineral fillers: talc (PP-Talc), calcium carbonate (PP-CaCO₃), and glass fiber (PP-GF). ANN models developed in MATLAB 2024a achieved prediction errors below 7.5% and regression coefficients (R²) above 0.98 for all filler types. The method was successfully applied to analyze a commercial recycled pellet, and its predictions were validated by X-ray fluorescence (XRF) and energy-dispersive X-ray spectroscopy (EDX). This approach provides a simple, rapid, and non-destructive tool for non-expert users to identify both the type and amount of mineral filler in recycled polymer materials, thereby reducing misclassification in their commercialization or quality control in industrial formulations. Full article

(This article belongs to the Special Issue Artificial Intelligence in Polymers)

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19 pages, 4563 KB

Open AccessArticle

Designing Imidazolium-Mediated Polymer Electrolytes for Lithium-Ion Batteries Using Machine-Learning Approaches: An Insight into Ionene Materials

by Ghazal Piroozi and Irshad Kammakakam

Polymers 2025, 17(15), 2148; https://doi.org/10.3390/polym17152148 - 6 Aug 2025

Cited by 5 | Viewed by 2392

Abstract

Over the past few decades, lithium-ion batteries (LIBs) have gained significant attention due to their inherent potential for environmental sustainability and unparalleled energy storage efficiency. Meanwhile, polymer electrolytes have gained popularity in several fields due to their ability to adapt to various battery geometries, enhanced safety features, greater thermal stability, and effectiveness in reducing dendrite growth on the anode. However, their relatively low ionic conductivity compared to liquid electrolytes has limited their application in high-performance devices. This limitation has led to recent studies revolving around the development of poly(ionic liquids) (PILs), particularly imidazolium-mediated polymer backbones as novel electrolyte materials, which can increase the conductivity with fine-tuning structural benefits, while maintaining the advantages of both solid and gel electrolytes. In this study, a curated dataset of 120 data points representing eight different polymers was used to predict ionic conductivity in imidazolium-based PILs as well as the emerging ionene substructures. For this purpose, four ML models: CatBoost, Random Forest, XGBoost, and LightGBM were employed by incorporating chemical structure and temperature as the models’ inputs. The best-performing model was further employed to estimate the conductivity of novel ionenes, offering insights into the potential of advanced polymer architectures for next-generation LIB electrolytes. This approach provides a cost-effective and intelligent pathway to accelerate the design of high-performance electrolyte materials. Full article

(This article belongs to the Special Issue Artificial Intelligence in Polymers)

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16 pages, 2088 KB

Open AccessArticle

Predictive Modelling and Optimisation of Rubber Blend Mixing Using a General Regression Neural Network

by Ivan Kopal, Ivan Labaj, Juliána Vršková, Marta Harničárová, Jan Valíček, Alžbeta Bakošová, Hakan Tozan and Ashish Khanna

Polymers 2025, 17(13), 1868; https://doi.org/10.3390/polym17131868 - 3 Jul 2025

Cited by 4 | Viewed by 2463

Abstract

This paper presents an intelligent predictive system designed to support real-time decision making in the control of rubber blend mixing processes. The core of the system is a General Regression Neural Network (GRNN), which accurately predicts key process parameters, such as viscosity (expressed as torque), temperature, and energy consumption across varying masses of the processed material. The model can evaluate the mixing progress based on the initial 10% of input data, allowing early intervention and process optimisation. Experimental validation was conducted using a Brabender Plastograph EC Plus with a natural rubber-based blend in the mass range of 60–75 g. The GRNN kernel width parameter (σ) was optimised through a 10-fold cross-validation. High predictive accuracy was confirmed by values of the coefficient of determination (R²) approaching 1, and consistently low values of the root mean square error (RMSE). This system offers a robust and scalable solution for intelligent process control, productivity enhancement, and quality assurance across diverse industrial applications, beyond rubber blending. Full article

(This article belongs to the Special Issue Artificial Intelligence in Polymers)

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Review

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35 pages, 3108 KB

Open AccessReview

Data-Driven Optimization of Discontinuous and Continuous Fiber Composite Processes Using Machine Learning: A Review

by Ivan Malashin, Dmitry Martysyuk, Vadim Tynchenko, Andrei Gantimurov, Vladimir Nelyub and Aleksei Borodulin

Polymers 2025, 17(18), 2557; https://doi.org/10.3390/polym17182557 - 22 Sep 2025

Cited by 19 | Viewed by 4132

Abstract

This paper surveys the application of machine learning in fiber composite manufacturing, highlighting its role in adaptive process control, defect detection, and real-time quality assurance. First, the need for ML in composite processing is highlighted, followed by a review of data-driven approaches—including predictive modeling, sensor fusion, and adaptive control—that address material heterogeneity and process variability. An in-depth analysis examines six case studies, among which are XPBD-based surrogates for RL-driven robotic draping, hyperspectral imaging (HSI) with U-Net segmentation for adhesion prediction, and CNN-driven surrogate optimization for variable-geometry forming. Building on these insights, a hybrid AI model architecture is proposed for natural-fiber composites, integrating a physics-informed GNN surrogate, a 3D Spectral-UNet for defect segmentation, and a cross-attention controller for closed-loop parameter adjustment. Validation on synthetic data—including visualizations of HSI segmentation, graph topologies, and controller action weights—demonstrates end-to-end operability. The discussion addresses interpretability, domain randomization, and sim-to-real transfer and highlights emerging trends such as physics-informed neural networks and digital twins. This paper concludes by outlining future challenges in small-data regimes and industrial scalability, thereby providing a comprehensive roadmap for ML-enabled composite manufacturing. Full article

(This article belongs to the Special Issue Artificial Intelligence in Polymers)

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