Modeling of capacitance for carbon-based supercapacitors using Super Learner algorithm

Abstract

Due to some specifications such as high capacitance and power density, electrostatic double-layer capacitors (EDLCs) are more noticeable than other supercapacitors. Some physical and chemical properties, surface functional groups, and testing conditions affect the efficiency and in particular the capacitance of EDLCs with carbon-based electrodes. In this study, four machine learning models, including Super Learner (SL), Extremely Randomized Trees (Extra trees), Extreme learning machine (ELM), and Multivariate adaptive regression splines (MARS) were implemented to predict the EDLCs' capacitance based on different impressive properties. A large dataset was assigned to the 121 different carbonaceous electrodes collected under various conditions, including 13 physical and chemical properties: voltage window (V), specific surface area (SSA) and SSA of micropore, pore volume (PV), and micropore volume, the ratio of D-band and G-band (Id/Ig) and doping elements (inputs parameters). The results indicated that the SL model with the R2 values of 0.9781, 0.9717, and 0.9768 for training, testing, and total dataset, respectively, and the RSME value of 18.099 was the most accurate model in comparison with the others. Indeed, the sensitivity analysis results exhibited that SSA with the relevance factor of 0.323 is the most important feature in the capacitance of carbon-based electrodes, while the presence of boron, sulfur, fluorine, phosphorus doping elements and pore size can be ignored.