Bibliographic Details
| Title: |
Enhancing Molecular Dynamics Simulations of Electrical Double Layers: From Simplified to Realistic Models. |
| Authors: |
Zeng, Liang, Ji, Xiangyu, Zhang, Jinkai, Huang, Nan, Wang, Zhenxiang, Yu, Ding, Peng, Jiaxing, Feng, Guang |
| Source: |
WIREs: Computational Molecular Science; Mar/Apr2025, Vol. 15 Issue 2, p1-14, 14p |
| Subject Terms: |
ELECTRIC double layer, MOLECULAR dynamics, MULTISCALE modeling, SIMULATION methods & models, ELECTROCHEMICAL electrodes, THERMAL properties, ELECTROCHEMICALS industry, POLARIZATION (Electrochemistry) |
| Abstract: |
Molecular dynamics (MD) simulations have become a powerful tool for studying double‐layer systems, offering atomistic insights into their equilibrium properties and dynamic behaviors. These simulations have significantly advanced the understanding of key electrochemical mechanisms and the design of electrochemical devices. However, challenges remain in aligning simulations with the complexities of realistic applications. In this perspectiv, we highlight critical areas for enhancing the realism of MD simulations, including refining methods for representing electrode polarization, improving electrode and electrolyte models to incorporate structural and compositional complexities, and simulating charging and discharging processes under realistic conditions while considering associated thermal behaviors. We also stress the importance of scaling simulation results to experimental dimensions through multiscale modeling and dimensionless analysis. Overcoming these challenges will allow MD simulations to advance our understanding of electrical double‐layer behaviors and drive innovations in the development of future electrochemical technologies. [ABSTRACT FROM AUTHOR] |
|
Copyright of WIREs: Computational Molecular Science is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Database: |
Complementary Index |