Generative AI for Designing Sustainable Polymer Composites for Renewable Energy Applications

Year : 2026 | Volume : 14 | Special Issue 03 | Page : 1232 1257
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.

[This article belongs to Special Issue under section in Journal of Polymer & Composites (jopc)]

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):1232-1257.
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):1232-1257. Available from: https://journals.stmjournals.com/jopc/article=2026/view=249818

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Special Issue Subscription Original Research
Volume 14
Special Issue 03
Received 27/06/2026
Accepted 06/07/2026
Published 16/07/2026
Publication Time 19 Days

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