Modeling specific capacitance of carbon nanotube-based supercapacitor electrodes by machine learning algorithms

Abstract

Carbon nanotubes (CNTs) have emerged as promising materials for supercapacitors (SCs) due to their unique properties and exceptional electrical conductivity. These cylindrical structures composed of carbon atoms offer several advantages for SC electrode applications. The electrochemical performance of CNT-based electrodes is strongly influenced by factors such as surface area, pore structure, and ID/IG ratio. However, the lack of a credible physical model capable of accurately predicting the performance of SCs based on these physicochemical properties of CNTs poses a challenge. In this study, we propose the utilization of a data-driven approach employing various models including a gradient boosting regression (GBR), Bayesian regression (BR), ridge regression (RR), and stochastic gradient descent (SGD) model to predict the performance of SCs with CNT electrodes based on the microstructural properties of the electrode material and electrochemical operational parameters. The developed GBR model demonstrates its feasibility by achieving a low root mean square error (RMSE) value of approximately 36.31 for the prediction of specific capacitance for test split. Additionally, a sensitivity analysis was conducted to investigate the influence of independent input parameters on a single output parameter, specifically the specific capacitance. This analysis provides insights into the relative importance and impact of various input parameters on the specific capacitance of CNT-based SCs.