Energy storage in supercapacitor researches: Interdisciplinary applications from molecular simulations to machine learning

Yawen Dong, Yutong Liu, Feifei Mao and Hua Wu

Applied Energy, 2025, vol. 393, issue C, No S0306261925008049

Abstract: Sustaining scientific attention is aimed at the supercapacitors (SCs), which are significant for environmental protection and energy storage. The properties of the SCs are built on capacity, cycling stability, power and energy density, etc., in which the performances of electrode materials, interaction between electrode and electrolyte and charge transfer on the surface or interlayer of electrode vastly affect the overall abilities of SCs. In SCs research field, computational simulation applications are crucial for their simulating calculation and prediction capabilities. This review provides a comprehensive overview of the latest advancements in using density functional theory (DFT) and machine learning (ML) techniques to design and optimize SCs. We summarize the applications of DFT in understanding the electronic structure, charge storage mechanisms, and electrochemical properties of electrode materials, as well as the interactions between electrodes and electrolytes. Additionally, the role of ML in predicting SC performance, optimizing material design, and monitoring the state of health (SOH) of SC devices have been highlighted. The combination of DFT and ML offers a powerful approach to accelerate the discovery of new materials and improve the overall performance of SCs. On this basis, the integration of additional computational techniques such as molecular dynamics (MD) and Monte Carlo (MC) simulations further complements and enhances the capabilities of analysis and prediction. By integrating DFT, MD, MC simulations and ML, researchers can not only gain comprehensive insights into the complex behaviors of electrode materials but also significantly accelerate material screening through this synergistic computational approach.

Keywords: Energy storage; Supercapacitor; Density functional theory; Molecular dynamics; Machine learning (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.apenergy.2025.126074

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