STM Journals Peer-reviewed research from Consortium e-Learning Network

Journal of Polymer & Composites Original Research

Generative AI for Designing Sustainable Polymer Composites for Renewable Energy Applications

  1. Joshila Grace L K Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai
  2. Kapil S. Banker Department of Mechanical Engineering, Government Engineering College, Palanpur
  3. Harshit P Bhavsar Department of Mechanical Engineering, Swarrnim Institute of Technology, Swarrnim Startup & Innovation University
  4. Rohini Goel Department of Computer Science & Engineering, MM Engineering College, Maharishi Markandeshwar (Deemed to be University), Mullana, Ambala
  5. Mit C. Patel Department of Mechanical Engineering, Silver Oak University, Ahmedabad
  6. Bharatkumar D Prajapati Department of Mechanical Engineering, Government Engineering College, Palanpur

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

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