Machine Learning Framework for Optimizing Polymer–Metal Oxide Composites as Charge Selective Layers in Perovskite Solar Cells

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Year : 2025 | Volume : 13 | 06 | Page :
    By

    Himanshu Rai,

  • Prabakaran Maruthamuthu Paramasivam,

  • S. Sivakumar,

  • P. Senthilkumar,

  1. , SRM Institute of Science and Technology, Tiruchirappalli, Tamil Nadu, India
  2. , Department of Electronics and Communication Engimeeering, A.K.T Memorial College of Engineering and Technology, AKT Nagar, Neelamangalam, Kallakurichi, Tamil Nadu, India
  3. , Department of Mechanical Engineering, A.K.T. Memorial College of Engineering and Technology, AKT Nagar, Neelamangalam, Kallakurichi, Tamil Nadu, India
  4. , Department of Mechanical Engineering, Nehru Institute of Engineering and Technology, Thirumalayampalayam, Coimbatore, Tamil Nadu, India

Abstract

To achieve high-performance and stability of perovskite solar cells (PSCs), it was important to incorporate innovative interfacial materials to tune the balanced charge extraction, low recombination, and enhanced operational lifespan. On this note, polymer composites with metal oxides have been proposed as promising candidates as charge selective layers (CSLs), whereby they present a rare combination of tunable energy levels, improved film forming abilities, and better interface engineering capabilities. In this project, we will develop a machine learning (ML)-based framework to speed up the screening and optimization of these hybrid materials to be used in CSL applications in PSCs. The study consists of the systematic synthetic selection and fabrication of composites founded on well-investigated metal oxides and the conductive and insulating polymers. Such material properties as bandgap, work function, film morphology, and thermal stability were determined experimentally and assembled in a large dataset. These features were used to train supervised learning models, which include Random Forest, Support Vector Regression, and XGBoost to predict power conversion efficiency (PCE) with great fidelity, R 2 values exceeding 0.9. The importance of processing parameters on photovoltaic performance including the oxide-to-polymer ratio, annealing temperature, and film roughness was further studied by Shapley value-based feature importance analysis. This model was demonstrated by experimental fabrication of ML-predicted CSL composites to be both predictive in nature and therefore valid. The sustainability analysis involving toxicity, energy input, and environmental stability was also included to guarantee environmentally friendly material use. This combined experimental-ML methodology offers a scalable and interpretable platform towards the rational design of charge-selective interlayers in high-performance perovskite photovoltaics.

Keywords: Perovskite solar cells, Charge selective layers, Polymer composites, Metal oxides, Machine learning, Interface engineering.

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How to cite this article:
Himanshu Rai, Prabakaran Maruthamuthu Paramasivam, S. Sivakumar, P. Senthilkumar. Machine Learning Framework for Optimizing Polymer–Metal Oxide Composites as Charge Selective Layers in Perovskite Solar Cells. Journal of Polymer and Composites. 2025; 13(06):-.
How to cite this URL:
Himanshu Rai, Prabakaran Maruthamuthu Paramasivam, S. Sivakumar, P. Senthilkumar. Machine Learning Framework for Optimizing Polymer–Metal Oxide Composites as Charge Selective Layers in Perovskite Solar Cells. Journal of Polymer and Composites. 2025; 13(06):-. Available from: https://journals.stmjournals.com/jopc/article=2025/view=228966


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Ahead of Print Subscription Original Research
Volume 13
06
Received 11/08/2025
Accepted 04/09/2025
Published 09/10/2025
Publication Time 59 Days
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