AI-Designed Functionally Graded Polymer Composites for Multifunctional Thin Films

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 | 02 | Page :
    By

    D Sai Ganesh,

  • S. N. Padhi,

  • Mamata Choudhury,

  • K. Vikas,

  • K. Anudeep,

  1. M. Tech Student, Department of Mechanical Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur District, Andhra Pradesh, India
  2. Professor, Department of Mechanical Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India
  3. Professor, Department of Computer Application, PSCMR college of Engineering and Technology, Vijayawada, Krishna District, Andhra Pradesh, India
  4. M. Tech Student, Department of Mechanical Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur District, Andhra Pradesh, India
  5. M. Tech Student, Department of Mechanical Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur District, Andhra Pradesh, India

Abstract

The design of multifunctional polymer composite thin films requires simultaneous optimization of mechanical, optical, barrier, and thermal properties—objectives often in conflict when using conventional homogeneous materials. This study presents an artificial intelligence-driven framework for designing functionally graded material (FGM) architectures in polymer nanocomposite thin films. We integrated machine learning with physics-based modeling to optimize compositional gradients across film thickness, achieving superior performance compared to homogeneous and discrete multilayer alternatives. A neural network trained on 150,000 finite element simulations and experimental data predicted material properties with R²>0.94 accuracy. Bayesian optimization identified optimal gradient profiles for poly(methyl methacrylate) (PMMA) matrices reinforced with titanium dioxide (TiO₂) nanoparticles and functional additives. Experimental validation via layer-by-layer spin coating demonstrated 82% improvement in tensile strength, 92% optical transmittance, 47% reduction in water vapor transmission rate, and enhanced thermal stability compared to homogeneous films. The optimized FGM architecture exhibited gradual composition transitions (40-80 wt% polymer, 15-45 wt% nanoparticles across 100 μm thickness), eliminating interfacial delamination while maintaining processing feasibility. Economic analysis reveals cost-competitiveness ($28/m²) with conventional multilayer films ($22/m²) while delivering 22% higher performance index. This AI-guided approach enables rapid exploration of vast design spaces, reducing development cycles from years to weeks, and establishes a generalizable methodology for multifunctional coating applications in optoelectronics, packaging, and protective systems.

Keywords: Functionally graded materials, polymer nanocomposites, machine learning, thin films, multi-objective optimization, layer-by-layer assembly.

How to cite this article:
D Sai Ganesh, S. N. Padhi, Mamata Choudhury, K. Vikas, K. Anudeep. AI-Designed Functionally Graded Polymer Composites for Multifunctional Thin Films. Journal of Polymer & Composites. 2026; 14(02):-.
How to cite this URL:
D Sai Ganesh, S. N. Padhi, Mamata Choudhury, K. Vikas, K. Anudeep. AI-Designed Functionally Graded Polymer Composites for Multifunctional Thin Films. Journal of Polymer & Composites. 2026; 14(02):-. Available from: https://journals.stmjournals.com/jopc/article=2026/view=240200


References

  1. Müller, E. et al. Functionally graded materials for sensor and energy applications. Sci. Eng. A 362, 17-39 (2003).
  2. Miyamoto, Y., Kaysser, W. A., Rabin, B. H., Kawasaki, A., Ford, R. G. Functionally Graded Materials: Design, Processing and Applications (Springer, 1999).
  3. Butler, K. T. et al. Machine learning for molecular and materials science. Nature 559, 547-555 (2018).
  4. Gómez-Bombarelli, R. et al. Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach. Mater. 15, 1120-1127 (2016).
  5. Zhang, Y. et al. Functionally graded polymer composites: A brief review of current fabrication methods and introduction of a novel solution blow spinning method. Sci. Eng. C 101, 67-78 (2019).
  6. Sanchez-Lengeling, B. & Aspuru-Guzik, A. Inverse molecular design using machine learning: Generative models for matter engineering. Science 361, 360-365 (2018).
  7. Kim, C. et al. Machine-learning-accelerated high-throughput materials screening. Rev. Mater. 2, 123801 (2018).
  8. Uma Mageswari, S. D., Suresh, M., Rajesh Kumar, U., Krishnamoorthy, N., Padhi, S. N., Bamane, K. D., Munjal, N., & Rajaram, A. Study on the phytoextraction of soil contaminated with selected heavy metals: Effects of biochar. Oxidation Communications 46(4), 986-993 (2023).
  9. Rafiee, M. A. et al. Enhanced mechanical properties of nanocomposites at low graphene content. ACS Nano 3, 3884-3890 (2009).
  10. Bharadwaj, R. K. Modeling the barrier properties of polymer-layered silicate nanocomposites. Macromolecules 34, 9189-9192 (2001).
  11. Padhi, S. N., Rout, T., Raghuram, K. S. Parametric instability and property variation analysis of a rotating cantilever FGO beam. International Journal of Recent Technology and Engineering 8(1), 2921-2925 (2019).
  12. Shahriari, B. et al. Taking the human out of the loop: A review of Bayesian optimization. IEEE 104, 148-175 (2016).
  13. Xia, H. et al. Preparation and characterization of poly(methyl methacrylate)/TiO₂ hybrid optical thin films. Thin Solid Films 515, 2863-2868 (2007).
  14. Saibabaa, O. S., Sivaji raja, G., Bhagat, V., Arun Kumar, M. P., Dessai, A. N., Reddy, R. K. K., Padhi, S. N. Free Vibration Response of Graphene Reinforced Polymer Composite Face Sheet Sandwich Panel Under Thermal Environment. Materials Today: Proceedings 57, 834-839 (2022).
  15. Roland, G., Padhi, S. N., Kayalvili, S., Cloudin, S., Kumar, A., & others. An Automated System for Arrhythmia Detection using ECG records from MITDB. In 2022 International Conference on Automation, Computing and Renewable Systems (ICACRS) (2022).
  16. Lookman, T., Balachandran, P. V., Xue, D., Yuan, R. Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design. npj Computational Materials 5, 21 (2019).
  17. Vinayaka, N., Christiyan, K. G., Shreepad, S., Padhi, S. N., Dambhare, S. G., Gayathri, K., Kolekar, A. B., Nagarajan, S., & others. Tribological Behavior on Stir-Casted Metal Matrix Composites of Al8011 and Nano Boron Carbide Particles. Journal of Nanomaterials 2023 (2023).
  18. Rhim, J. W., Park, H. M., Ha, C. S. Bio-nanocomposites for food packaging applications. Progress in Polymer Science 38, 1629-1652 (2013).
  19. Potts, J. R., Dreyer, D. R., Bielawski, C. W., Ruoff, R. S. Graphene-based polymer nanocomposites. Polymer 52, 5-25 (2011).
  20. Ash, B. J., Siegel, R. W., Schadler, L. S. Mechanical behavior of alumina/poly(methyl methacrylate) nanocomposites. Macromolecules 37, 1358-1369 (2004).
  21. Karuppiah, G., Kuttalam, K. C., Palaniappan, M., Santulli, C., & Palanisamy, S. Multiobjective optimization of fabrication parameters of jute fiber/polyester composites with egg shell powder and nanoclay filler. Molecules 25(23), 5579 (2020). https://doi.org/10.3390/molecules25235579
  22. Santulli, C., Palanisamy, S., & Kalimuthu, M. Pineapple fibers, their composites and applications. In Plant Fibers, their Composites, and Applications (pp. 323-346). Elsevier (2022). https://doi.org/10.1016/B978-0-12-824528-6.00007-2
  23. Goutham, E. R. S., Hussain, S. S., Muthukumar, C., Krishnasamy, S., Kumar, T. S. M., Santulli, C., & Siengchin, S. Drilling parameters and post-drilling residual tensile properties of natural-fiber-reinforced composites: A review. Journal of Composites Science 7(4), 136 (2023). https://doi.org/10.3390/jcs7040136
  24. Ayrilmis, N., Kanat, G., Avsar, E. Y., Palanisamy, S., & Ashori, A. Utilizing waste manhole covers and fibreboard as reinforcing fillers for thermoplastic composites. Journal of Reinforced Plastics and Composites 44(17-18), 1108-1118 (2025). https://doi.org/10.1177/07316844241238507
  25. Karthik, A., Karuppusamy, M., Krishnakumar, S., Palanisamy, S., Jayamani, M., Sureshkumar, K., Ali, S. K., & Al-Farraj, S. A. Enhancement of mechanical properties of hybrid polymer composites using palmyra palm and coconut sheath fibers: The role of tamarind shell powder. BioResources 20(1), 698-724 (2024). https://doi.org/10.15376/biores.20.1.698-724
  26. Palanisamy, S., Mayandi, K., Dharmalingam, S., Rajini, N., Santulli, C., Mohammad, F., & Al-Lohedan, H. A. Tensile properties and fracture morphology of acacia caesia bark fibers treated with different alkali concentrations. Journal of Natural Fibers 19(15), 11258-11269 (2022). https://doi.org/10.1080/15440478.2021.2022562
Ahead of Print Subscription Original Research
Volume 14
02
Received 09/01/2026
Accepted 20/01/2026
Published 16/04/2026
Publication Time 97 Days
2

Login


My IP

PlumX Metrics