Green Energy & Environment2026，Vol.11Issue(2)：P.500-510,11.DOI:10.1016/j.gee.2025.06.001
Insight into properties and structures of ionic liquids by machine learning molecular dynamics simulation
Yaxi Yu 1Zhenlei Wang 2Xiaochun Zhang 3Kun Dong2
作者信息
- 1. 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 Sino-Danish College,University of Chinese Academy of Sciences,Beijing,100049,China
- 2. 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
- 3. 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 Key Laboratory of Smart Manufacturing in Energy Chemical Process,Ministry of Education,East China University of Science and Technology,Shanghai,200237,China
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摘要
关键词
Ionic liquids/Machine learning force field/Molecular dynamics分类
化学化工引用本文复制引用
Yaxi Yu,Zhenlei Wang,Xiaochun Zhang,Kun Dong..Insight into properties and structures of ionic liquids by machine learning molecular dynamics simulation[J].Green Energy & Environment,2026,11(2):P.500-510,11.基金项目
supported by the National Natural Science Foundation of China(Nos.22278397) （Nos.22278397）
the Fundamental Research Funds for the Central Universities(2024SMECP01). （2024SMECP01）
