The impact of physicochemical features of carbon electrodes on the capacitive performance of supercapacitors: a machine learning approach

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.

Advancement in Supercapacitors for IoT Applications by Using Machine Learning: Current Trends and Future Technology

“…Consequently, there has been a shift in focus towards using ML models to forecast the effectiveness of SCs, considering the impact of doping materials as a percentage, as well as other structural and operational characteristics. Mishra et al [101] evaluated the impact of heteroatom doping composition and structural characteristics of carbon materials on the effectiveness of capacitance using ML models. A comprehensive dataset of 147 carbon-based supercapacitor sets was compiled from the existing literature.…”

Data-Driven Optimization of Carbon Electrodes for Aqueous Supercapacitors

“…ML methods can also be used to find the relative importance of the supercapacitor characteristics to their capacitance behavior by applying sensitivity analysis methods such as SHAP or Sobol indices. It is often observed that the specific surface area (SSA), pore volume (PV), and oxygen ratio are among the most important parameters representing the properties of carbon electrodes. ,, The ML predictions offer valuable insights into the synthesis of better carbon materials, help to identify critical features, optimize reaction conditions, and predict and optimize the cycle life, thereby facilitating advancements in carbon material synthesis. ,, Conversely, new experimental data can be leveraged to refine and enhance the predictive accuracy of ML models.…”

Atomistic to Mesoscopic Modelling of Thermophysical Properties of Graphene-Reinforced Epoxy Nanocomposites

“…Carbon nanofillers, with their distinctive physical characteristics, are preferred for creating high-performance epoxy nanocomposites compared to other nanofillers [ 4 , 5 , 6 ]. These nanocomposites have garnered significant attention from both academic and industrial sectors due to their broad range of potential uses [ 7 , 8 , 9 ]. Furthermore, graphene and graphene oxide have emerged as promising candidates for a wide range of applications, such as communication, photodetectors, and chemical sensors [ 10 , 11 ].…”