Lakshmi Sridevi,
K. Veena,
R. Ashok,
B. Anni Princy,
B. Kesava Rao,
S. Deepa,
G. Janani,
- Professor, Department of Computer Science and Engineering, Chennai Institute of Technology, Chennai, Tamil Nadu, India
- Associate Professor, Department of Computer Science and Engineering, School of Computing, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India
- Assistant Professor, Department of Electronics and Communication Engineering, Kamaraj College of Engineering & Technology, Tamil Nadu, India
- Professor, Department of Computer Science and Engineering, Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Chennai, Tamil Nadu, India
- Associate Professor, Department of Civil Engineering, R.V.R & J.C College of Engineering (A), Guntur, Andhra Pradesh, India
- Professor, Department of Electronics and Communication Engineering, Panimalar Engineering College, Tamil Nadu, India
- Assistant Professor, Department of Information Technology, R.M.D Engineering College, Kavaraipettai, Tamil Nadu, India
Abstract
Polymer nanocomposites are high performance engineered materials obtained by inclusion of nano-sized fillers into the polymer matrix to enhance mechanical, thermal, electrical, barrier and functional properties. However, the complex and non-linear interactions among polymer chemistry, nanofiller characteristics, filler concentration, dispersion, interfacial bonding and processing conditions make it challenging to anticipate and maximize their properties. Artificial intelligence (AI) and machine learning (ML) offer powerful data-driven solutions to these difficulties by establishing correlations between the material composition, processing parameters, microstructural attributes and the end performance. This work focuses on the application of AI and ML methods in the engineering design, prediction and optimization of polymer nanocomposites. The necessary material and processing data are thoroughly processed to build predictive models to anticipate mechanical, thermal, electrical and other functional properties. Machine learning techniques can identify influential features, simulate complex non-linear interactions and accelerate the selection of ideal polymer-nanofiller combinations. Furthermore, predictive models can be used in conjunction with optimization approaches to identify new formulations and processing conditions with reduced experimental time, material consumption and development costs. Design of intelligent materials can be achieved by integrating AI, ML, experimental validation and iterative optimization. Such technologies would enable sustainable, efficient and application-specific synthesis of next-generation polymer nanocomposites for advanced engineering applications.
Keywords: Engineering Design, Predictive Modeling, Materials Optimization, Nanofillers, Sustainable Materials
References
1. H. Jamil, M. Faizan, M. Adeel, T. Jesionowski, G. Boczkaj, and A. Balčiūnaitė, “Recent advances in polymer nanocomposites: Unveiling the frontier of shape memory and self-healing properties—A comprehensive review,” Molecules, vol. 29, no. 6, p. 1267, 2024.
2. M. Sharma, P. Chauhan, N. K. Jangid, and R. Sharma, “Nanofillers for polymer nanocomposites: Multidisciplinary applications,” in Handbook of Nanofillers, S. Mallakpour and C. M. Hussain, Eds. Singapore: Springer, 2025.
3. T. Gao, Y. Zhao, Y. Niu, and X. Cao, “Research progress on nanopolymer composites in civil engineering,” Nanomaterials, vol. 16, no. 4, p. 267, 2026.
4. G. G. Vogiatzis and D. N. Theodorou, “Multiscale molecular simulations of polymer-matrix nanocomposites: Or what molecular simulations have taught us about the fascinating nanoworld,” Arch. Comput. Methods Eng., vol. 25, no. 3, pp. 591–645, 2018.
5. K. K. Gupta, S. Barman, A. Sankar et al., “Artificial intelligence in materials by design: Critical review and perspectives on materials informatics to generative and agentic intelligence,” Arch. Comput. Methods Eng., vol. 33, pp. 6015–6045, 2026.
6. R. I. Mukhamediev, Y. Popova, Y. Kuchin, E. Zaitseva, A. Kalimoldayev, A. Symagulov, V. Levashenko, F. Abdoldina, V. Gopejenko, K. Yakunin, E. Muhamedijeva, and M. Yelis, “Review of artificial intelligence and machine learning technologies: Classification, restrictions, opportunities and challenges,” Mathematics, vol. 10, no. 15, p. 2552, 2022.
7. I. Malashin, V. Tynchenko, A. Gantimurov, V. Nelyub, and A. Borodulin, “Boosting-based machine learning applications in polymer science: A review,” Polymers, vol. 17, no. 4, p. 499, 2025.
8. P. Koukaras and C. Tjortjis, “Data preprocessing and feature engineering for data mining: Techniques, tools, and best practices,” AI, vol. 6, no. 10, p. 257, 2025.
9. W. Li, A. Halimi, J. Vaidya, X. Jiang, and E. Ayday, “Privacy-preserving verification of ML preprocessing via model behavior indicators,” IEEE Trans. Privacy, vol. 2, pp. 144–158, 2025.
10. W. G. Mengesha, “AI-driven design of multifunctional nanomaterials in revolutionizing high-temperature, high-power solutions for space technology: Potentials, challenges and perspectives,” Discov. Nano, vol. 20, no. 1, p. 220, 2025.
11. V. V. Kumar, T. F. Santos, C. M. Santos et al., “Artificial intelligence and machine learning approaches for composite materials: Recent developments and applications,” Polym. Compos., vol. 47, no. 12, pp. 10636–10663, 2026.
12. B. Liu, P. Liu, W. Lu, and T. Olofsson, “Explainable artificial intelligence (XAI) for material design and engineering applications: A quantitative computational framework,” Int. J. Mech. Syst. Dyn., vol. 5, pp. 236–265, 2025.
13. A. Mishra, V. S. Jatti, E. M. Sefene, and S. Paliwal, “Explainable artificial intelligence (XAI) and supervised machine learning-based algorithms for prediction of surface roughness of additively manufactured polylactic acid (PLA) specimens,” Appl. Mech., vol. 4, no. 2, pp. 668–698, 2023.
14. G. Tzionis, P. Mouratidis, G. Kougka et al., “A review of explainable AI methods and their application in manufacturing systems,” Discov. Appl. Sci., vol. 8, p. 52, 2026.
15. T. Long, Q. Pang, Y. Deng, X. Pang, Y. Zhang, R. Yang, and C. Zhou, “Recent progress of artificial intelligence application in polymer materials,” Polymers, vol. 17, no. 12, p. 1667, 2025.
16. A. Majedi, “Artificial intelligence in nanotechnology: Current applications and future perspectives,” Health Nanotechnol., vol. 2, p. 16, 2026.
17. H. Kokash, M. Kokash, A. Bany-Ata, S. Baqain, and M. Shakoor, “Recent advances in artificial intelligence and machine learning for life cycle-wide additive manufacturing: A comprehensive review,” Machines, vol. 14, no. 5, p. 550, 2026.
18. N. I. Okafor, N. Igbokwe, H. Onohuean et al., “Artificial intelligence-driven development and characterization of nanomedicine,” BioNanoSci., vol. 16, p. 255, 2026.
19. R. Ma, Y. Hou, W. Zhang et al., “MOF-based nanocomposites in polymer matrix: Progress and prospects,” Adv. Compos. Hybrid Mater., vol. 8, p. 351, 2025.
20. P. K. Biswas, O. Omole, G. Peterson, E. Cumbo, M. Agarwal, and H. Dalir, “Carbon and cellulose based nanofillers reinforcement to strengthen carbon fiber-epoxy composites: Processing, characterizations, and applications,” Front. Mater., vol. 9, p. 1089996, 2023.
21. G. Spinelli, R. Guarini, R. Kotsilkova, E. Ivanov, and V. Romano, “Experimental, theoretical and simulation studies on the thermal behavior of PLA-based nanocomposites reinforced with different carbonaceous fillers,” Nanomaterials, vol. 11, no. 6, p. 1511, 2021.
22. Y. Liang, X. Wei, Y. Peng, X. Wang, and X. Niu, “A review on recent applications of machine learning in mechanical properties of composites,” Polym. Compos., vol. 46, no. 3, pp. 1939–1960, 2025.
23. A. Bahadori-Jahromi, S. Room, C. Paknahad, M. Altekreeti, Z. Tariq, and H. Tahayori, “The role of artificial intelligence and machine learning in advancing civil engineering: A comprehensive review,” Appl. Sci., vol. 15, no. 19, p. 10499, 2025.
24. I. H. Sarker, “AI-based modeling: Techniques, applications and research issues towards automation, intelligent and smart systems,” SN Comput. Sci., vol. 3, p. 158, 2022.
25. A. E. A. Allen and A. Tkatchenko, “Machine learning of material properties: Predictive and interpretable multilinear models,” Sci. Adv., vol. 8, no. 18, p. eabm7185, 2022.
26. M. A. do Nascimento Moura, G. C. dos Santos Ferreira, and A. L. Moreno Júnior, “A systematic review of machine learning techniques for predicting compressive and flexural strength of mortars,” Arch. Comput. Methods Eng., vol. 33, pp. 4441–4462, 2026.
27. X. Jiang, H. Sun, K. Choudhary et al., “Interpretable ensemble learning for materials property prediction with classical interatomic potentials,” npj Comput. Mater., vol. 11, p. 319, 2025.
28. Y. Raza, H. Raza, A. Ahmed et al., “Integration of response surface methodology (RSM), machine learning (ML), and artificial intelligence (AI) for enhancing properties of polymeric nanocomposites—A review,” Polym. Compos., vol. 46, no. 15, pp. 13591–13627, 2025.
29. S. Harishbabu, J. Djuansjah, P. S. R. Sreekanth, A. P. Kumar, B. Louhichi, S. K. Sahu, I. E. Lee, and Q. Wali, “Data-driven AI approach for optimizing processes and predicting mechanical properties of boron nitride nanoplatelet-reinforced PLA nanocomposites,” Polymers, vol. 18, no. 2, p. 185, 2026.
30. B. L. Ortiz, V. Gupta, R. Kumar, A. Jalin, X. Cao, C. Ziegenbein, A. Singhal, M. Tewari, and S. W. Choi, “Data preprocessing techniques for AI and machine learning readiness: Scoping review of wearable sensor data in cancer care,” JMIR Mhealth Uhealth, vol. 12, p. e59587, 2024.
31. Y. M. Zhao, J. Y. Zhang, P. K. Liaw et al., “Machine learning-based computational design methods for high-entropy alloys,” High Entropy Alloys Mater., vol. 3, pp. 41–100, 2025.
32. M. Bustamante-Torres, D. Romero-Fierro, B. Arcentales-Vera, S. Pardo, and E. Bucio, “Interaction between filler and polymeric matrix in nanocomposites: Magnetic approach and applications,” Polymers, vol. 13, no. 17, p. 2998, 2021.
33. J. Pires, C. D. Paula, V. G. L. Souza, A. L. Fernando, and I. Coelhoso, “Understanding the barrier and mechanical behavior of different nanofillers in chitosan films for food packaging,” Polymers, vol. 13, no. 5, p. 721, 2021.
34. R. Saha, M. H. Munshi, M. Akter, A. A. R. Shikder, and T. Islam, “Synthesis techniques, fundamental properties, and emerging applications of nanocomposites: A comprehensive review,” SPE Polym., vol. 6, no. 2, p. e70004, 2025.
35. J.-P. Li, N. Polovina, and S. Konur, “A review of AI-driven engineering modelling and optimization: Methodologies, applications and future directions,” Algorithms, vol. 19, no. 2, p. 93, 2026.
36. X. Xue, H. Dhumras, G. Thakur, and V. Shukla, “Integrating artificial intelligence and sustainable materials for smart eco innovation in production,” Sci. Rep., vol. 15, no. 1, p. 36942, 2025.
37. H. I. Aysel, X. Cai, and A. Prugel-Bennett, “Explainable artificial intelligence: Advancements and limitations,” Appl. Sci., vol. 15, no. 13, p. 7261, 2025.
38. S. Kabir, M. S. Hossain, and K. Andersson, “A review of explainable artificial intelligence from the perspectives of challenges and opportunities,” Algorithms, vol. 18, no. 9, p. 556, 2025.
Journal of Polymer & Composites
| Volume | 14 | |
| 04 | ||
| Received | 02/09/2026 | |
| Accepted | 18/09/2026 | |
| Published | 22/09/2026 | |
| Publication Time | 20 Days |