Insights into the specific capacitance of CNT-based supercapacitor electrodes using artificial intelligence

Abstract

In this study, the specific capacitance characteristics of a carbon nanotube (CNT) supercapacitor was predicted using different machine learning algorithms, such as artificial neural network (ANN), random forest regression (RFR), k-nearest neighbors regression (KNN), and decision tree regression (DTR), based on experimental studies. The results of the simulation verified the accuracy of the ANN algorithm with respect to the data derived from the specific capacitance of the supercapacitor module. It was observed that there was a strong correlation between the experimental results and the predictions made by the ANN algorithm. Comparative analysis showed that the developed ANN algorithm was consistently superior over other algorithms in terms of different metrics, as indicated by the lowest root mean square error (RMSE) value of roughly 26.24 and the highest R² value of approximately 0.91. In contrast, the DTR model recorded the least reliable results in the accuracy analysis, as indicated by the highest RMSE value of about 53.46 and the lowest R² value of roughly 0.63. To further explore the impact of independent input parameters including pore structure, specific surface area, and I_D/I_G ratio on a single output parameter (particularly, the specific capacitance) the sensitivity analysis was also conducted using the SHapley Additive exPlanations (SHAP) framework. This investigation sheds light on the relative significance and effects of different input variables on the specific capacitance of supercapacitors based on CNTs. The results indicated that the ANN algorithm accurately predicted the capacitance of the CNT-based supercapacitor, demonstrating the feasibility and significance of neural network algorithms in the design of energy storage devices.