Insight into properties and structures of ionic liquids by machine learning molecular dynamics simulationOA
Insight into properties and structures of ionic liquids by machine learning molecular dynamics simulation
Yaxi Yu;Zhenlei Wang;Xiaochun Zhang;Kun Dong
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,ChinaBeijing 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,ChinaBeijing 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,ChinaBeijing 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
Ionic liquidsMachine learning force fieldMolecular dynamics
Ionic liquidsMachine learning force fieldMolecular dynamics
《绿色能源与环境（英文）》 2026 (2)
500-510,11
This work was financially supported by the National Nat-ural 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.
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