Efficient hyperparameter-tuned machine learning approach for estimation of supercapacitor performance attributes

Abstract

Recent years have witnessed the rise of supercapacitor as effective energy storage device. Specifically, carbon-based electrodes have been experimentally well studied and used in the fabrication of supercapacitors due to their excellent electrochemical properties. Recent publications have reported the use of Machine Learning (ML) techniques to study the correlation between the structural features of electrodes and supercapacitor performance metrics. However, the poor R-squared values (i.e., large deviations from the ideal value of unity) and large RMSE values reported in these works reflect the lack of accurate models’ development. This work reports the development and utilization of highly tuned and efficient ML models using hyperparameter tuning, that give insights into correlation between the structural features of electrodes and supercapacitor performance metrics namely specific capacitance, power density and energy density. Artificial Neural Networks (ANN) and Random Forest (RF) models have been employed to predict the various in-operando performance metrics of carbon-based supercapacitors based on three input features such as mesopore surface area, micropore surface area and scan rate. Experimentally measured values of these parameters used for training and testing these two models have been extracted from a set of research papers reported in literature. The optimization techniques and various tuning methodologies adopted for identifying model hyperparameters are discussed in this paper. The R2 values obtained for prediction of specific capacitance, power density and energy density using RF model are in the range from 0.8612 to 0.9353 respectively, while the RMSE values of the above parameters are 18.651, 0.2732 and 0.5764 for respective input parameters. Similarly, the R2 values obtained for prediction of specific capacitance, power density and energy density using ANN model are in the range from 0.9211 to 0.9644 respectively, while the RMSE values of the above parameters are 18.132, 0.1601 and 0.5764 for respective input parameters. Thus, the highly tuned ANN and RF models depict higher R-squared and lower RMSE values in comparison to those previously reported in literature, thereby demonstrating the importance of hyperparameter tuning and optimization in building accurate and reliable computational models.