| dc.description.abstract | Hybrid electric vehicles and portable electronic systems use supercapacitors for energy storage owing
to their fast charging/discharging rates, long life cycle, and low maintenance. Specific capacitance
is regarded as one of the most important performance-related characteristics of a supercapacitor’s
electrode. In the current study, Machine Learning (ML) algorithms were used to determine the impact
of various physicochemical properties of carbon-based materials on the capacitive performance of
electric double-layer capacitors. Published experimental datasets from 147 references (4899 data
entries) were extracted and then used to train and test the ML models, to determine the relative
importance of electrode material features on specific capacitance. These features include current
density, pore volume, pore size, presence of defects, potential window, specific surface area, oxygen,
and nitrogen content of the carbon-based electrode material. Additionally, categorical variables
as the testing method, electrolyte, and carbon structure of the electrodes are considered as well.
Among five applied regression models, an extreme gradient boosting model was found to best
correlate those features with the capacitive performance, highlighting that the specific surface area,
the presence of nitrogen doping, and the potential window are the most significant descriptors for
the specific capacitance. These findings are summarized in a modular and open-source application
for estimating the capacitance of supercapacitors given, as only inputs, the features of their carbon-
based electrodes, the electrolyte and testing method. In perspective, this work introduces a new wide
dataset of carbon electrodes for supercapacitors extracted from the experimental literature, also
giving an instance of how electrochemical technology can benefit from ML models. | es |
