Enhance Thermal and Conductive Properties through Graph Neural Network-Based Machine Learning-Driven Advanced Polymer Material Design

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Year : 2026 | Volume : 14 | 03 | Page :
    By

    R. Kiruba buri,

  • L.C. Manikandan,

  • D. Shobana,

  • A. Jeyamurugan,

  • V. Parimala,

  • Veeraiyah Thangasamy,

  • J. Jeya Caleb,

  1. Assistant Professor (Sr.Grade), Department of Computer Science and Engineering, School of Computing, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai 62, Tamil Nadu, India
  2. Professor, Department of Computer Science and Engineering, Universal Engineering College, Thrissur, Kerala, India
  3. Assistant Professor, Department of Electronics and Communication Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai, Tamil Nadu, India
  4. Assistant Professor (Senior Grade), Dept of Artificial Intelligence and Data Science, Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Chennai, Tamil Nadu, India
  5. Assistant Professor, Department of Electronics and Communication Engineering, Chennai Institute of Technology, Chennai, Tamil Nadu, India
  6. Professor, Department of Electronics and Communication, V.S.B. Engineering College, Karur 639111, Tamil Nadu, India
  7. Assistant Professor, Department of Electronics and Communication Engineering, Easwari Engineering College, Chennai – 600089, Tamil Nadu, India

Abstract

Advanced polymer materials are widely used in modern engineering and manufacturing because of their lightweight nature, flexibility, durability, and adaptability to different applications. However, designing polymer materials with enhanced thermal and electrical properties remains a challenging task. The performance of polymers is influenced by a complex combination of molecular structures, filler materials, processing parameters, and nanoscale interactions. Conventional optimization methods often require extensive experimental trials and computational resources, making it difficult to efficiently explore large material design spaces and identify optimal polymer configurations. To address these challenges, this study presents an intelligent framework for smart polymer material design using Graph Neural Network (GNN)-based machine learning. In the proposed approach, polymer molecular structures and filler interactions are represented as graph-based networks, where atoms are modeled as nodes and molecular bonds are represented as edges. This graph representation enables the framework to effectively capture structural relationships and interaction patterns that influence material properties. By learning these hidden connections, the GNN model can accurately predict the relationship between molecular architecture, composite composition, and key functional characteristics. The framework further incorporates data preprocessing, feature engineering, and adaptive model training to improve prediction efficiency, reliability, and overall performance. Machine-learning-driven optimization is employed to identify polymer compositions that provide improved thermal stability, enhanced electrical conductivity, and stronger structural performance. Extensive simulations and comparative evaluations demonstrate that the proposed GNN-based approach achieves higher prediction accuracy, better scalability, and more effective material optimization than conventional machine-learning techniques and traditional experimental modeling methods. The results indicate that the optimized polymer composites exhibit improved heat dissipation, superior electrical transport properties, and greater resistance to material degradation.

Keywords: Graph Neural Networks, Polymer Materials, Machine Learning, Thermal Conductivity, Electrical Properties, Advanced Composites, Material Optimization, Smart Manufacturing.

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How to cite this article:
R. Kiruba buri, L.C. Manikandan, D. Shobana, A. Jeyamurugan, V. Parimala, Veeraiyah Thangasamy, J. Jeya Caleb. Enhance Thermal and Conductive Properties through Graph Neural Network-Based Machine Learning-Driven Advanced Polymer Material Design. Journal of Polymer & Composites. 2026; 14(03):-.
How to cite this URL:
R. Kiruba buri, L.C. Manikandan, D. Shobana, A. Jeyamurugan, V. Parimala, Veeraiyah Thangasamy, J. Jeya Caleb. Enhance Thermal and Conductive Properties through Graph Neural Network-Based Machine Learning-Driven Advanced Polymer Material Design. Journal of Polymer & Composites. 2026; 14(03):-. Available from: https://journals.stmjournals.com/jopc/article=2026/view=247262


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Ahead of Print Subscription Original Research
Volume 14
03
Received 15/05/2026
Accepted 08/06/2026
Published 22/06/2026
Publication Time 38 Days
1

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