• Dr. P.Senthil Pandian
• Dr. S. Dheepthi Gunavathana
• Dr. K. Radha Lakshmi
• R. Kubendran
• Dr. E. Paulsamy

The increasing demand for sustainable and high-efficiency energy storage systems has spurred significant interest in devices enhanced with nanomaterials, owing to their exceptional electrochemical properties. However, accurately predicting the performance of such systems is challenging due to the complex, non-linear, and multi-variable nature of nanostructures and their electrochemical behavior. In this study, we introduce a machine learning (ML)-driven framework to predict key performance metrics of energy storage devices incorporating Co-Fe N nanoparticles within N,S-doped carbon matrices. A range of ML models—Random Forest, Support Vector Regression (SVR), and Gradient Boosting Machines—were trained on a well-curated dataset containing information on material composition, synthesis parameters, and electrochemical performance indicators. The developed framework achieved prediction accuracies exceeding 92% for specific capacitance, energy density, and cycling stability. These findings highlight the capability of ML approaches to accelerate the design and optimization of advanced nanomaterial-based energy storage technologies

Super capacitors, Energy Storage Devices, Co-Fe N Nanoparticles, Carbon Matrix, Electrochemical Modeling, Machine Learning

"Predicting Electrochemical Performance of Nanostructured Energy Storage Devices Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 7, page no.d606-d615, July-2025, Available :http://www.jetir.org/papers/JETIR2507367.pdf

"Predicting Electrochemical Performance of Nanostructured Energy Storage Devices Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 7, page no. ppd606-d615, July-2025, Available at : http://www.jetir.org/papers/JETIR2507367.pdf