AI-Driven Framework for Accelerating Polymer Nanocomposite Commercialization in Computational Materials Engineering

Notice

This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

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

    M. Sukanya,

  • Bantupalli Nagalakshmi,

  • D. Shobana,

  • V. Parimala,

  • S. Ahamed Ali,

  • K. Muthukannan,

  • A. Thilagavathy,

  1. Associate Professor, Department of Computer Science and Engineering, Kathir College of Engineering, Coimbatore, Tamil Nadu, India
  2. Assistant Professor, Department of Computer Science and Engineering, Ravindra College of Engineering for Women, Kurnool -518452, Andhra Pradesh, 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, Department of Electronics and Communication Engineering, Chennai Institute of Technology – 600133, Tamil Nadu, India
  5. Assistant Professor, Department of Computer Science and Engineering, Easwari Engineering College, Chennai, Tamil Nadu, India
  6. Associate Professor, Department of Computer Science and Engineering, Vel Tech MultiTech Dr Rangarajan Dr Sakunthala Engineering College, 42, Vel Tech Road, Vel Nagar, Avadi, Chennai, Tamil Nadu, India
  7. Associate Professor, Department of Computer Science and Engineering, R.M.K Engineering College, RSM Nagar, Kavaraipettai 601206, Tamil Nadu, India

Abstract

The remarkable mechanical strength increased functional qualities, lightweight structure, and thermal stability of polymer nanocomposites have prompted modern materials research to prioritize their rapid commercialization. Advanced materials can be created by adding nanoscale fillers such as carbon nanotubes, graphene, silica, and metal oxides to polymer matrices. These materials have applications in biomedical engineering, aerospace, electronics, packaging, and automobile manufacture. Research and development of polymer nanocomposites has traditionally relied on costly and time-consuming trial-and-error approaches, which are impractical for use in industrial settings. To get beyond these limitations, AI has revolutionized the material optimization, commercialization, and discovery processes. The mechanical, thermal, and functional properties of polymer nanocomposites are predicted by our AI system through the use of machine learning techniques, material composition, processing parameters, and performance statistics. Learn about intricate interactions between nanofiller and polymer by employing ANNs, SVMs, and RLNs. Experimental work, development time, and manufacturing expenses can all be saved with quick predictions of ideal material configurations. Artificial intelligence identifies cost-effective, high-performance, and trustworthy material compositions for scalable manufacturing. The results show that the development of polymer nanocomposite is more accurate, efficient, and innovative when using AI-based predictive modeling. Intelligent, data-driven materials engineering has shifted the focus from experimentation to the rapid commercialization and widespread application of next-generation polymer nanocomposites.

Keywords: Polymer Nanocomposites, Machine Learning, Materials Informatics, Materials Commercialization, Property Prediction, Artificial Neural Networks, Random Forest.

How to cite this article:
M. Sukanya, Bantupalli Nagalakshmi, D. Shobana, V. Parimala, S. Ahamed Ali, K. Muthukannan, A. Thilagavathy. AI-Driven Framework for Accelerating Polymer Nanocomposite Commercialization in Computational Materials Engineering. Journal of Polymer & Composites. 2026; 14(03):-.
How to cite this URL:
M. Sukanya, Bantupalli Nagalakshmi, D. Shobana, V. Parimala, S. Ahamed Ali, K. Muthukannan, A. Thilagavathy. AI-Driven Framework for Accelerating Polymer Nanocomposite Commercialization in Computational Materials Engineering. Journal of Polymer & Composites. 2026; 14(03):-. Available from: https://journals.stmjournals.com/jopc/article=2026/view=242889


References

  1. Musa, A. A., Bello, A., Adams, S. M., Onwualu, A. P., Anye, V. C., Bello, K. A., & Obianyo, I. I. (2025). Nano-enhanced polymer composite materials: A review of current advancements and challenges. Polymers, 17(7), 893.
  2. Rashid, A. B., Haque, M., Islam, S. M. M., & Uddin Labib, K. M. R. (2024). Nanotechnology-enhanced fiber-reinforced polymer composites: Recent advancements on processing techniques and applications. Heliyon, 10(2), e24692.
  3. Kamath, S., Chandrappa, R., Nagaraja, S., et al. (2025). Review of natural and synthetic nanofiller-enhanced natural fibre reinforced polymer composite (NFRPC): Materials, properties, and biomedical applications. Micro and Nano Systems Letters, 13, 22.
  4. Mametja, S., Mabuza, M., Sadiku, E. R., et al. (2025). Development of energy-efficient polymers by using conductive nanohybrid fillers: Recent progress and future prospects. Advanced Composites and Hybrid Materials, 8, 258.
  5. Shchegolkov, A. V., Shchegolkov, A. V., & Kaminskii, V. V. (2026). Carbon nanotubes and graphene in polymer composites for strain sensors: Synthesis, functionalization, and application. Journal of Composites Science, 10(1), 43.
  6. Yim, Y. J., Yoon, Y. H., Kim, S. H., Lee, J. H., Chung, D. C., & Kim, B. J. (2025). Carbon nanotube/polymer composites for functional applications. Polymers, 17(1), 119.
  7. Liu, B., Sun, J., Zhao, J., et al. (2025). Hybrid graphene and carbon nanotube–reinforced composites: Polymer, metal, and ceramic matrices. Advanced Composites and Hybrid Materials, 8, 1.
  8. Sharma, S. K., Sharma, L. K., Pradhan, R., Sharma, Y., Sharma, M., Gajević, S., Ivanović, L., & Stojanović, B. (2026). Interphase-centric and mechanism-driven advances in polymer composites reinforced with nano-, synthetic, and inorganic fillers. Polymers, 18(3), 323.
  9. Antunes, M., & Arencón, D. (2026). Recent developments in the mechanical behavior of polymer-based composites. Polymers, 18(5), 598.
  10. Rahman, M. M., Islam, S., Mubasshira, Islam, M. S., Ahammad, R., Islam, M. A., Hasib, M. A., Rahman, M. S., Moshwan, R., Ehsan, M. M., Rabbi, M. S., Moniruzzaman, M., Nazir, M. A., & Liu, W.-D. (2026). Polymer composites in additive manufacturing: Current technologies, applications, and emerging trends. Polymers, 18(2), 192.
  11. Kibrete, F., Trzepieciński, T., Gebremedhen, H. S., & Woldemichael, D. E. (2023). Artificial intelligence in predicting mechanical properties of composite materials. Journal of Composites Science, 7(9), 364.
  12. Sarker, I. H. (2022). AI-based modeling: Techniques, applications and research issues towards automation, intelligent and smart systems. SN Computer Science, 3, 158.
  13. Ghasemi, A., & Barisik, M. (2025). Machine learning for thermal transport prediction in nanoporous materials: Progress, challenges, and opportunities. Nanomaterials, 15(21), 1660.
  14. Rosa, A. C., Elomari, Y., Calderón, A., Mateu, C., Haddad, A., & Boer, D. (2024). Methodology for the prediction of the thermal conductivity of concrete by using neural networks. Applied Sciences, 14(17), 7598.
  15. Long, T., Pang, Q., Deng, Y., Pang, X., Zhang, Y., Yang, R., & Zhou, C. (2025). Recent progress of artificial intelligence application in polymer materials. Polymers, 17(12), 1667.
  16. Salas, M., Singh, A., Pignataro, C., et al. (2026). AI-powered open-source infrastructure for accelerating materials discovery and advanced manufacturing. Communications Materials, 7, 65.
  17. Srivastava, N., Verma, S., Singh, A., Shukla, P., Singh, Y., Oza, A. D., Kaur, T., Chowdhury, S., Kapoor, M., & Yadav, A. N. (2024). Advances in artificial intelligence-based technologies for increasing the quality of medical products. Daru, 33(1), 1.
  18. Antony Jose, S., Tonner, A., Feliciano, M., Roy, T., Shackleford, A., & Menezes, P. L. (2025). Smart manufacturing for high-performance materials: Advances, challenges, and future directions. Materials, 18(10), 2255.
  19. Xue, X., Dhumras, H., Thakur, G., & Shukla, V. (2025). Integrating artificial intelligence and sustainable materials for smart eco innovation in production. Scientific Reports, 15(1), 36942.
  20. Venkataraman, M., Rao, G. C., Madavareddi, J. K., & Maddi, S. R. (2025). Leveraging machine learning models in evaluating ADMET properties for drug discovery and development. ADMET and DMPK, 13(3), 2772.
  21. Xu, P., Ji, X., Li, M., et al. (2023). Small data machine learning in materials science. npj Computational Materials, 9, 42.
  22. Singh, M. K., Palaniappan, S. K., Arora, G., et al. (2026). Recent advances in applications of nanocomposites: A brief overview. Discover Materials, 6, 58.
  23. Chan, J. X., Wong, J. F., Petrů, M., Hassan, A., Nirmal, U., Othman, N., & Ilyas, R. A. (2021). Effect of nanofillers on tribological properties of polymer nanocomposites: A review on recent development. Polymers, 13(17), 2867.
  24. Gao, T., Zhao, Y., Niu, Y., & Cao, X. (2026). Research progress on nanopolymer composites in civil engineering. Nanomaterials, 16(4), 267.
  25. Shah, M., Ullah, A., Azher, K., Ur Rehman, A., Akturk, N., Juan, W., Tüfekci, C. S., & Salamci, M. U. (2023). The influence of nanoparticle dispersions on mechanical and thermal properties of polymer nanocomposites using SLA 3D printing. Crystals, 13(2), 285.
  26. Khater, D., et al. (2024). Nanoparticles and nanofillers: Types, methods of preparation and characterization, and safety. In S. Mallakpour & C. M. Hussain (Eds.), Handbook of Nanofillers. Singapore: Springer.
  27. Venachi, N., Guerrero, A. I., Bolaños, P. A., et al. (2026). Review on aging of biodegradable polymers: Experimental approaches and emerging role of machine and deep learning. Journal of Polymers and the Environment, 34, 58.
  28. Gyabaah, K. Y., Mahoney, B., Martey, A. K., Yan, C., Mensah, P., & Li, G. (2026). Machine learning-assisted polymer and polymer composite design for additive manufacturing. AI Materials, 1(1), 2.
  29. Malashin, I., Tynchenko, V., Gantimurov, A., Nelyub, V., & Borodulin, A. (2024). A multi-objective optimization of neural networks for predicting the physical properties of textile polymer composite materials. Polymers, 16(12), 1752.
  30. Bakal Gumus, F., & Yildirim, H. (2025). Artificial neural networks-based prediction of mechanical properties in h-BN reinforced composites. International Journal of Mechanics and Materials in Design, 21, 1053–1067.
  31. Shen, C., Zhang, M., Lu, M., Chang, E., Gao, Z., Ban, W., Liu, Q., Zuo, Z., & Jiang, C. (2026). Machine learning empowered formulation design, optimization and characterization of nanoparticulate drug delivery systems: Current applications, challenges, and future perspectives. Acta Pharmaceutica Sinica B, 16(2), 665–685.
  32. Abu-Shams, M. (2026). Artificial intelligence integration in materials science. Discover Applied Sciences, 8, 232.
  33. Nazari, S., & Abdelrasoul, A. (2025). Advancements and applications of artificial intelligence and machine learning in material science and membrane technology: A comprehensive review. Membranes, 15(12), 353.
  34. Wang, F., Jiang, S., & Li, J. (2025). The AI-driven transformation in new materials manufacturing and the development of intelligent sports. Applied Sciences, 15(10), 5667.
  35. Choudhary, K., DeCost, B., Chen, C., et al. (2022). Recent advances and applications of deep learning methods in materials science. npj Computational Materials, 8, 59.
Ahead of Print Subscription Original Research
Volume 14
03
Received 09/04/2026
Accepted 24/04/2026
Published 05/05/2026
Publication Time 26 Days
49

Login


My IP

PlumX Metrics