AI-Based Discovery of High-Performance Energy Storage Polymer Composites: A Comprehensive Review

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Year : 2026 | Volume : 14 | 03 | Page :
    By

    Manas Kumar Yogi,

  • Darapu Uma,

  • Yamuna Mundru,

  • Manam Siva Sai,

  1. Assistant Professor, Department of Computer Science and Engineering, Pragati Engineering College (Autonomous), Surampalem, Andhra Pradesh, India
  2. Associate Professor, Department of Computer Science and Engineering, Pragati Engineering College (Autonomous), Surampalem, Andhra Pradesh, India
  3. Assistant Professor, Department of Computer Science and Engineering, Pragati Engineering College (Autonomous), Surampalem, Andhra Pradesh, India
  4. Postgraduate Student, Department of Computer Science and Engineering, Pragati Engineering College (Autonomous), Surampalem, Andhra Pradesh, India

Abstract

The accelerating global demand for high-performance energy storage systems has stimulated significant research into advanced polymer composites as next-generation electrolytes, electrode binders, and functional membranes for batteries, supercapacitors, and photovoltaic devices. However, the vast compositional and structural design space of polymer materials presents formidable challenges for conventional trial-and-error discovery strategies, which remain slow, costly, and biased by prior expert knowledge. Machine learning (ML) and artificial intelligence (AI) have emerged as transformative tools for navigating this complexity, enabling rapid prediction of electrochemical properties, de novo design of polymer electrolytes, and precise optimization of nanostructures for supercapacitors and batteries. This review systematically examines the application of ML techniques, including graph neural networks, Bayesian optimization, variational autoencoders, and transformer-based language models, for the discovery of energy storage polymer composites. The discussion critically evaluates ML-driven advancements across lithium-ion batteries, flexible energy storage devices, and solar energy materials, drawing on quantitative performance benchmarks reported in the primary literature. Emerging strategies such as active learning, multi-fidelity data fusion, physics-informed neural networks, and polymer-specific foundation models are discussed alongside persistent challenges related to data scarcity, model interpretability, and the translation gap between computational prediction and experimental synthesis. The review further addresses the landscape of open polymer property databases, the role of autonomous closed-loop experimentation in accelerating materials discovery, and the importance of reproducible, well-documented machine learning pipelines for the field to mature beyond proof-of-concept demonstrations. By consolidating evidence from verified primary sources and presenting original comparative analyses across methods and application domains, this review provides researchers, materials scientists, and computational chemists with an actionable, evidence-based perspective on the current state and future trajectory of AI-accelerated, sustainable energy storage polymer composite discovery.

Keywords: Machine Learning; Polymer Composites; Energy Storage; Lithium-Ion Batteries; Graph Neural Networks; Electrochemical Property Prediction; Polymer Electrolytes; Supercapacitors; Physics-Informed Neural Networks; Active Learning

How to cite this article:
Manas Kumar Yogi, Darapu Uma, Yamuna Mundru, Manam Siva Sai. AI-Based Discovery of High-Performance Energy Storage Polymer Composites: A Comprehensive Review. Journal of Polymer & Composites. 2026; 14(03):-.
How to cite this URL:
Manas Kumar Yogi, Darapu Uma, Yamuna Mundru, Manam Siva Sai. AI-Based Discovery of High-Performance Energy Storage Polymer Composites: A Comprehensive Review. Journal of Polymer & Composites. 2026; 14(03):-. Available from: https://journals.stmjournals.com/jopc/article=2026/view=248809


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Ahead of Print Subscription Review Article
Volume 14
03
Received 18/06/2026
Accepted 30/06/2026
Published 03/07/2026
Publication Time 15 Days
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