Machine Learning-Based Performance Prediction of Nanomaterial-Enhanced Energy Storage Devices

The growing demand for sustainable and efficient energy storage systems has driven interest in nanomaterial-enhanced devices due to their superior electrochemical properties. Predicting the performance of such devices is a complex task due to the non-linear and multi-parametric nature of nanostructures and their electrochemical behavior. In this study, we present a machine learning (ML)-based framework to predict the performance metrics of energy storage devices enhanced with Co-Fe N nanoparticles embedded in N,S-doped carbon matrices. Various ML models, including Random Forest, Support Vector Regression (SVR), and Gradient Boosting Machines, were trained on a curated dataset comprising material composition, synthesis conditions, and electrochemical output parameters. The proposed framework achieves over 92% accuracy in predicting specific capacitance, energy density, and cycling stability. Our results demonstrate the potential of ML for accelerating the design and development of next-generation nanomaterial-based energy storage systems.