Insight into properties and structures of ionic liquids by machine learning molecular dynamics simulationOA
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 firstprinciples 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 Cpositions,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.
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
化学化工
Ionic liquidsMachine learning force fieldMolecular dynamics
《Green Energy & Environment》 2026 (2)
P.500-510,11
supported by the National Natural Science Foundation of China(Nos.22278397)the Fundamental Research Funds for the Central Universities(2024SMECP01).
评论