Generative AI for Designing Sustainable Polymer Composites for Renewable Energy Applications

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

    Joshila Grace L K,

  • Kapil S. Banker,

  • Harshit P Bhavsar,

  • Rohini Goel,

  • Mit C. Patel,

  • Bharatkumar D Prajapati,

  1. Professor, Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India
  2. Assistant Professor, Department of Mechanical Engineering, Government Engineering College, Palanpur, Gujarat, India
  3. Associate Professor, Department of Mechanical Engineering, Swarrnim Institute of Technology, Swarrnim Startup & Innovation University, Gujarat, India
  4. Associate Professor, Department of Computer Science & Engineering, MM Engineering College, Maharishi Markandeshwar (Deemed to be University), Mullana, Ambala, Haryana, India
  5. Associate Professor, Department of Mechanical Engineering, Silver Oak University, Ahmedabad, Gujarat, India
  6. Assistant Professor, Department of Mechanical Engineering, Government Engineering College, Palanpur, Gujarat, India

Abstract

Sustainable polymer composites are increasingly required for renewable energy devices, yet conventional trial-and-error formulation cannot efficiently balance performance, processability, recyclability, and environmental constraints. This study proposes a generative artificial intelligence framework for designing polymer composites for photovoltaic encapsulation, dielectric energy storage, polymer electrolytes, and thermal-management systems. Public polymer-property and composite datasets were curated from open databases and published supplementary records. Chemical descriptors, molecular fingerprints, polymer embeddings, processing variables, and sustainability indicators were used to train a masked multitask property predictor. Conditional generative models produced new polymer candidates, which were screened through chemical validity, novelty, synthesizability proxy, uncertainty estimation, and physics-guided composite rules. The multitask model achieved reliable prediction across thermal, mechanical, dielectric, ionic, and sustainability-related targets, with test-set R² values ranging from 0.79 to 0.92. From 20,000 generated candidates, 92.10% were chemically valid and 81.64% were novel. Final ranking identified promising bio-based, recyclable, and energy-functional composite classes. The proposed framework provides a reproducible route for early-stage sustainable polymer composite discovery and supports future experimental validation under real renewable energy operating and aging conditions before device scale-up.

Keywords: Generative artificial intelligence, sustainable polymers, polymer composites, renewable energy materials, polymer informatics, recyclable polymers, dielectric materials, polymer electrolytes.

How to cite this article:
Joshila Grace L K, Kapil S. Banker, Harshit P Bhavsar, Rohini Goel, Mit C. Patel, Bharatkumar D Prajapati. Generative AI for Designing Sustainable Polymer Composites for Renewable Energy Applications. Journal of Polymer & Composites. 2026; 14(03):-.
How to cite this URL:
Joshila Grace L K, Kapil S. Banker, Harshit P Bhavsar, Rohini Goel, Mit C. Patel, Bharatkumar D Prajapati. Generative AI for Designing Sustainable Polymer Composites for Renewable Energy Applications. Journal of Polymer & Composites. 2026; 14(03):-. Available from: https://journals.stmjournals.com/jopc/article=2026/view=249818


References

[1].  T. Kopac, “Advancing Polymer Science and Energy Storage Solutions Through the Integration of Artificial Intelligence and Machine Learning: A Transformative Approach,” Polymers, vol. 17, no. 24, Art. no. 3267, 2025, doi: 10.3390/polym17243267.

[2]. H. Sikandar, N. Khan, M. Falahat, and M. I. Qureshi, “Generative AI for Sustainable Product Design: A Technology Convergence Framework Integrating Multi-Objective Optimisation and Smart Manufacturing,” IET Collaborative Intelligent Manufacturing, vol. 8, no. 1, Art. no. e70051, 2026, doi: 10.1049/cim2.70051.

[3].  A. N. Wilson, P. C. St. John, D. H. Marin, C. B. Hoyt, E. G. Rognerud, M. R. Nimlos, R. M. Cywar, N. A. Rorrer, K. M. Shebek, L. J. Broadbelt, G. T. Beckham, and M. F. Crowley, “PolyID: Artificial Intelligence for Discovering Performance-Advantaged and Sustainable Polymers,” Macromolecules, vol. 56, no. 21,8547–8557, 2023, doi: 10.1021/acs.macromol.3c00994.

[4].  U. Mamodiya, I. Kishor, D. Sinha, N. Guler, and N. Naik, “Cognitive Digital Twin Framework With Virtual Reality for Interactive Control of Solar PV Infrastructure,” IEEE Access, vol. 13, pp. 181874–181898, 2025, doi: 10.1109/ACCESS.2025.3623294.

 

[5].  A. Sharma, S. Sharma, M. Sharma, V. Sharma, S. Sharma, and I. Sivanesan, “Polymeric Frontiers in Next-Generation Energy Storage: Bridging Molecular Design, Multifunctionality, and Device Applications Across Batteries, Supercapacitors, Solid-State Systems, and Beyond,” Polymers, vol. 17, no. 20, Art. no. 2800, 2025, doi: 10.3390/polym17202800.

[6].  Z. Yang, W. Ye, X. Lei, et al., “De novo Design of Polymer Electrolytes Using GPT-Based and Diffusion-Based Generative Models,” npj Computational Materials, vol. 10, Art. no. 296, 2024, doi: 10.1038/s41524-024-01470-9.

[7].  U. Mamodiya, I. Kishor, and R. Garine, “Artificial Intelligence Based Hybrid Solar Energy Systems With Smart Materials and Adaptive Photovoltaics for Sustainable Power Generation,” Scientific Reports, vol. 15, Art. no. 17370, 2025, doi: 10.1038/s41598-025-01788-4.

[8]. A. Sharma, T. Mukhopadhyay, S. M. Rangappa, et al., “Advances in Computational Intelligence of Polymer Composite Materials: Machine Learning Assisted Modeling, Analysis and Design,” Archives of Computational Methods in Engineering, vol. 29, pp. 3341–3385, 2022, doi: 10.1007/s11831-021-09700-9.

[9].  S. Jiang and M. A. Webb, “Generative Active Learning Across Polymer Architectures and Solvophobicities for Targeted Rheological Behavior,” npj Computational Materials, vol. 12, Art. no. 28, 2026, doi: 10.1038/s41524-025-01900-2.

[10]. Q. Cheng, Y. Han, L. Shanmugam, Y. Zhao, S. Dong, S. Du, and J. Yang, “A Deep Learning-Based Composite Design Strategy for Efficient Selection of Material and Layup Sequences from a Given Database,” Composites Science and Technology, vol. 230, Art. no. 109154, 2022, doi: 10.1016/j.compscitech.2021.109154.

[11]. Y. Zheng, P. Thakolkaran, A. K. Biswal, J. A. Smith, Z. Lu, S. Zheng, B. H. Nguyen, S. Kumar, and A. Vashisth, “AI-Guided Inverse Design and Discovery of Recyclable Vitrimeric Polymers,” Advanced Science, vol. 12, no. 6, Art. no. 2411385, 2025, doi: 10.1002/advs.202411385.

[12]. U. Mamodiya, I. Kishor, P. Vidyullatha, M. Almaayah, and A. Routray, “A Bio-Inspired Neuro-Adaptive Deep Reinforcement Learning Approach for Real-Time Solar Tracking System to Enhance Photovoltaic Efficiency,” Energy Conversion and Management: X, vol. 29, Art. no. 101486, 2026, doi: 10.1016/j.ecmx.2025.101486.

[13]. Z. Liu, J. Han, S. Wang, J. Li, H. Yi, and T. Long, “Research Progress and Future Perspectives of 3D Printing Polymer-Based Materials Whole Life Cycle Frameworks: Material Genetic Design–Intelligent Manufacturing–Recycling,” Advanced Engineering Materials, 2025, doi: 10.1002/adem.202501538.

[14]. H. Xue, G. Cheng, and W.-J. Yin, “Computational Design of Energy-Related Materials: From First-Principles Calculations to Machine Learning,” WIREs Computational Molecular Science, 2024, doi: 10.1002/wcms.1732.

[15]. K. M. Qureshi et al., “Are Polymer-Based Smart Materials Unlocking the Path to Sustainable Manufacturing for a Net-Zero Economy? Current Trends and Potential Applications,” IEEE Access, vol. 13, pp. 284–296, 2025, doi: 10.1109/ACCESS.2024.3521944.

[16]. S. M. Mousavi, S. A. Hashemi, M. Y. Kalashgrani, A. Gholami, Y. Mazaheri, M. Riazi, D. Kurniawan, M. Arjmand, O. Madkhali, M. D. Aljabri, M. M. Rahman, and W.-H. Chiang, “Artificial Intelligence in Advanced Materials and Energy Applications,” Chemical Record, vol. 24, Art. no. e202200266, 2024, doi: 10.1002/tcr.202200266.

[17]. M. Liu, Y. Zhou, X. Mei, Z. Yu, B. Guan, Y. Xiao, S. Liu, H. Wang, and Y. Qin, “AI-Driven Biomaterial Design: An Intelligent Closed Loop from Reverse Design to Biological Response,” Frontiers in Cell and Developmental Biology, vol. 13, Art. no. 1755565, 2026, doi: 10.3389/fcell.2025.1755565.

[18]. A. Bharadwaj, A. Sudhir, H. Shekhar, N. Khandelwal, and I. Kishor, “Raspberry Pi Based Weather Monitoring System,” International Journal of Research in Engineering, Science and Management, vol. 4, no. 8, pp. 115–118, 2021.

[19]. K. Choudhary, “AtomGPT: Atomistic Generative Pretrained Transformer for Forward and Inverse Materials Design,” The Journal of Physical Chemistry Letters, vol. 15, no. 27, pp. 6909–6917, 2024, doi: 10.1021/acs.jpclett.4c01126.

[20]. Q. Jiang, H. Liang, Y. Zhang, and G. Huang, “Nanocomposite Design for Energy-Related Applications,” Nanomaterials, vol. 15, no. 17, Art. no. 1334, 2025, doi: 10.3390/nano15171334.

[21]. K.-H. Lee, H. J. Lim, and G. J. Yun, “A Data-Driven Framework for Designing Microstructure of Multifunctional Composites with Deep-Learned Diffusion-Based Generative Models,” Engineering Applications of Artificial Intelligence, vol. 129, Art. no. 107590, 2024, doi: 10.1016/j.engappai.2023.107590.

 

[22]. T. Chen, Z. Pang, S. He, et al., “Machine Intelligence-Accelerated Discovery of All-Natural Plastic Substitutes,” Nature Nanotechnology, vol. 19, pp. 782–791, 2024, doi: 10.1038/s41565-024-01635-z.

[23]. C. Kuenneth, J. Lalonde, B. L. Marrone, et al., “Bioplastic Design Using Multitask Deep Neural Networks,” Communications Materials, vol. 3, Art. no. 96, 2022, doi: 10.1038/s43246-022-00319-2.

[24]. M. Sharma, I. Kishor, A. Dwivedi, and A. Bhattacherjee, “Smart Devices for Augmenting Sensory Perception Empowering Differently-Abled Individuals Through Advanced Assistive Technologies,” in Integrating AI With Haptic Systems for Smarter Healthcare Solutions, IGI Global, 2025, pp. 26, doi: 10.4018/979-8-3373-2307-7.ch020.

[25]. D. Park, J. Lee, K. Park, and S. Ryu, “Hierarchical Generative Network: A Hierarchical Multitask Learning Approach for Accelerated Composite Material Design and Discovery,” Advanced Engineering Materials, vol. 25, Art. no. 2300867, 2023, doi: 10.1002/adem.202300867.

[26]. G. Han, Y. Sun, Y. Feng, G. Lin, and N. Lu, “Artificial Intelligence Guided Thermoelectric Materials Design and Discovery,” Advanced Electronic Materials, vol. 9, Art. no. 2370036, 2023, doi: 10.1002/aelm.202370036.

[27]. W. Zhao, X. Xu, H. Lan, L. Wang, J. Lin, L. Du, C. Zhang, and X. Tian, “Designing Multicomponent Thermosetting Resins through Machine Learning and High-Throughput Screening,” Macromolecules, vol. 58, no. 1, pp. 744–753, 2025, doi: 10.1021/acs.macromol.4c01822.

[28]. Kishor, U. Mamodiya, and B. Keswani, “Energy Efficiency and Practical Implications of IoT-Based Static vs. Single-Axis Solar Tracking Systems: A Comparative Analysis,” Journal of Intelligent Systems and Internet of Things, vol. 15, no. 2, pp. 164–182, 2025, doi: 10.54216/JISIoT.150212.

[29]. Y. Li, C. Wu, Y. Wang, N. Li, T. Liu, and J. Lu, “Accelerating the Battery Revolution: AI-Driven Multiscale Innovation From Material Discovery to Smart Manufacturing,” Advanced Functional Materials, vol. 36, no. 11, Art. no. e14830, 2026, doi: 10.1002/adfm.202514830.

[30]. Z. Huo, X. Xie, and R. Tong, “Machine Learning for Developing Sustainable Polymers,” Chemistry – A European Journal, vol. 31, Art. no. e202500718, 2025, doi: 10.1002/chem.202500718.

[31]. 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, Art. no. 1667, 2025, doi: 10.3390/polym17121667.

[32]. H. R. Gantla, M. Namdev, U. Mamodiya, I. Kishor, G. Kumar, and P. Goyal, “A Smart Monitoring Framework for Sustainable Solar Energy Harvesting Using IoT and Machine Learning Techniques,” in 2025

[33]. S. Han and X. Sun, “Optimizing Product Design Using Genetic Algorithms and Artificial Intelligence Techniques,” IEEE Access, vol. 12, pp. 151460–151475, 2024, doi: 10.1109/ACCESS.2024.3456081.

[34]. Palaniappan, M., Palanisamy, S., Murugesan, T., Tadepalli, S., Khan, R., Ataya, S., and Santulli, C. (2024). “Influence of washing with sodium lauryl sulphate (SLS) surfactant on different properties of ramie fibres,” BioResources 19(2), 2609-2625.

[35]. Manickaraj, K., Karthik , A., Palanisamy, S., Jayamani, M., Ali, S. K., Lakshmi Sankar, S., and Al-Farraj, S. A. (2025). “Improving mechanical performance of hybrid polymer composites: Incorporating banana stem leaf and jute fibers with tamarind shell powder,” BioResources 20(1), 1998–2025.

Ahead of Print Subscription Original Research
Volume 14
03
Received 27/06/2026
Accepted 06/07/2026
Published 16/07/2026
Publication Time 19 Days
34

Login


My IP

PlumX Metrics