Volume 11 Issue 2
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Yaxi Yu, Zhenlei Wang, Xiaochun Zhang, Kun Dong. Insight into properties and structures of ionic liquids by machine learning molecular dynamics simulation. Green Energy&Environment, 2026, 11(2): 500-510. doi: 10.1016/j.gee.2025.06.001
Citation: Yaxi Yu, Zhenlei Wang, Xiaochun Zhang, Kun Dong. Insight into properties and structures of ionic liquids by machine learning molecular dynamics simulation. Green Energy&Environment, 2026, 11(2): 500-510. doi: 10.1016/j.gee.2025.06.001
shu

Insight into properties and structures of ionic liquids by machine learning molecular dynamics simulation

doi: 10.1016/j.gee.2025.06.001

  • a. Beijing Key Laboratory of Solid State Battery and Energy Storage Process, CAS Key Laboratory of Green Process and Engineering, State Key Laboratory of Mesoscience and Engineering, Institute of Process Engineering, Chinese Academy of Sciences, Beijing, 100190, China;

  • b. Sino-Danish College, University of Chinese Academy of Sciences, Beijing, 100049, China;

  • c. Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, 200237, China

Funds:

This work was financially supported by the National Natural Science Foundation of China (Nos. 22278397), and the Fundamental Research Funds for the Central Universities (2024SMECP01). The authors sincerely appreciate Prof. Suojiang Zhang (IPE, CAS) for his careful academic guidance, discussion and support.

  • Received date 27 March 2025, Accepted date 05 June 2025, RevRecd date 21 May 2025, Publish date 10 June 2025,
    Abstract
  • Ionic liquids (ILs) have exhibited great application potential in many fields due to their unique properties. Molecular dynamics (MD) simulation has been widely employed to investigate their microscopic structure. However, classical molecular dynamics simulations struggle to accurately describe the complex interactions in ILs using the existing parameterized force fields. Recently, the MD simulations based on machine learning force fields (MLFFs) trained by first-principles calculations have attracted considerable attentions due to their abilities to balance computational accuracy and efficiency. Herein, we report the Bayesian-based MLFFs which can be successfully applied in IL systems and accelerate MD simulation. The calculated atomic forces, structures, and vibrational behaviors were validated to match the accuracy of first-principles calculations. Properties of the imidazolium-based ILs, including density, self-diffusion coefficients, viscosity, and radial distribution functions were predicted at the extended scales. Z-bonds that describe the unique structures in ILs were analyzed and the influences of C-positions, temperature, and solvent H2O on Z-bonding configurations were systematically investigated. Our results confirmed that MLFFs presented the strong feasibility to investigate the large and complex systems, especially to predict structures and properties of the ILs. And the procedure described for MLFFs provides valuable guidance for researchers who are studying ILs.

     

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