Data-Driven Material Design and Performance Improvement: Constructing Sustainable Polymer Nanocomposites Using Deep Learning

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Year : 2026 | Volume : 14 | 04 | Page :
By

A. Muthukrishnan,

M. Daniel Nareshkumar,

A. Sakthivel,

S. Sivasankaran,

Talluri Upender,

M. Bharathi,

S. Murugesan,

  1. Professor, Department of Electrical and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, India
  2. Assistant Professor (Selection Grade), Department of Computer Science and Engineering, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Vadapalani Campus, Chennai, Tamil Nadu, India
  3. Professor, Department of Computer Science and Engineering, KGiSL Institute of Technology, Saravanampatti, Coimbatore, Tamil Nadu, India
  4. Professor, Department of Information Technology, Raak College of Engineering and Technology, Puducherry, India
  5. Assistant Professor, Department of Computer Science and Engineering, CMR College of Engineering & Technology, Kandlakoya, Medchal, Hyderabad, Telangana, India
  6. Assistant Professor, Department of Electrical and Communication Engineering, Jeppiaar Institute of Technology, Tamil Nadu, India
  7. Professor, Department of Artificial Intelligence and Data Science, J.J. College of Engineering and Technology, Trichy, Tamil Nadu, India

Abstract

In the formation of sustainable polymer nanocomposites, the effective material techniques are required to balance the mechanical qualities, environmental compatibility and processing efficiency. The optimization of polymer matrix, nanofiller loading, processing conditions and material properties is typically time consuming, resource intensive and highly dependent on trial-error methodology using standard experimental techniques. The present work provides a data-driven approach that combines deep learning with sustainable polymer nanocomposite design for predicting and enhancing the material performance. The deep learning models are trained on experimental data sets comprising polymer composition, nanofiller characteristics, filler loading, processing parameters and the measured mechanical, thermal and functional properties. The suggested method can automatically find complex nonlinear relationship between the material composition and performance, and accurately forecast the tensile strength, elastic modulus, thermal stability and related features. Feature analysis and model optimization are used to identify the most influential material properties and optimal formulations. This approach allows for virtual screening of candidate nanocomposites, and therefore potentially reduce the experimental burden, material consumption and development time. Sustainability concerns are often incorporated into design processes to encourage environmentally responsible material choices. The proposed data-driven approach is a viable way for the accelerated development of high-performance polymer nanocomposites with decreased resource usage. The results highlight the potential of deep learning for enabling intelligent, scalable and sustainable material engineering.

Keywords: Sustainable Materials, Data-Driven Design, Material Optimization, Nanofillers, Predictive Modeling, Performance Improvement, Machine Learning.

How to cite this article: A. Muthukrishnan, M. Daniel Nareshkumar, A. Sakthivel, S. Sivasankaran, Talluri Upender, M. Bharathi, S. Murugesan. Data-Driven Material Design and Performance Improvement: Constructing Sustainable Polymer Nanocomposites Using Deep Learning. Journal of Polymer & Composites. 2026; 14(04):-.
How to cite this URL: A. Muthukrishnan, M. Daniel Nareshkumar, A. Sakthivel, S. Sivasankaran, Talluri Upender, M. Bharathi, S. Murugesan. Data-Driven Material Design and Performance Improvement: Constructing Sustainable Polymer Nanocomposites Using Deep Learning. Journal of Polymer & Composites. 2026; 14(04):-. Available from: https://journals.stmjournals.com/jopc/article=2026/view=253675

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Ahead of Print Subscription Original Research
Volume 14
04
Received 11/08/2026
Accepted 22/08/2026
Published 31/08/2026
Publication Time 20 Days

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