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Revisiting the structure, interaction, and dynamical property of ionic liquid from the deep learning force field

文献类型：期刊论文

作者Ling, Yulong2,3; Li, Kun3; Wang, Mi3; Lu, Junfeng3; Wang, Chenlu3; Wang, Yanlei3,4; He, Hongyan1,2,3
刊名JOURNAL OF POWER SOURCES
出版日期2023-01-30
卷号555页码:8
ISSN号0378-7753
关键词Ionic liquids Molecular dynamics simulations Force field Hydrogen bond Machine learning
DOI10.1016/j.jpowsour.2022.232350
英文摘要Rational understanding of interaction and structure of ionic liquids (ILs) is vital for their application in super -capacitors. The force field trained by machine learning has aroused considerable interest in the molecular design of ILs, which can effectively balance the competition between computational accuracy and efficiency. In this work, a new deep learning force field (DPFF) for 10 different ILs was obtained, where the dataset for atomic energy and force was prepared via the ab initio molecular dynamics (MD) simulation. Using the trained DPFF, the ns-long MD simulations for various ILs were performed successfully. Combining the error analysis on atomic energy, distribution of bonds and angles, and potential energy, one can prove that the MD simulation with DPFF can describe the force and energy of ILs with ab initio precision. Meanwhile, the analysis of the vibrational spectrum and hydrogen bond suggests that the DPFF can also predict the coupling nature between coulombic and hydrogen bonding interactions within ILs reasonably. Furthermore, the DPFF for ILs is trained to extend to the bulk system. Hence, DPFF, possessing high accuracy and low computational cost, can serve as an effective tool for the molecular design of new ILs-based electrolytes for high-performance energy storage devices.
WOS关键词MOLECULAR-DYNAMICS ; SIMULATION ; SEPARATION ; MECHANISM ; GAS
资助项目National Key R&D Program of China[2021YFB3802600] ; National Natural Science Foundation of China[21922813] ; National Natural Science Foundation of China[22078322] ; Youth Innovation Promotion Association of CAS[2021046] ; Youth Innovation Promotion Association of CAS[Y2021022] ; Innovation Academy for Green Manufacture, Chinese Academy of Sciences[IAGM2020C16]
WOS研究方向Chemistry ; Electrochemistry ; Energy & Fuels ; Materials Science
语种英语
出版者ELSEVIER
WOS记录号WOS:000891784100004
资助机构National Key R&D Program of China ; National Natural Science Foundation of China ; Youth Innovation Promotion Association of CAS ; Innovation Academy for Green Manufacture, Chinese Academy of Sciences
源URL[http://ir.ipe.ac.cn/handle/122111/56077]  
专题中国科学院过程工程研究所
通讯作者Wang, Yanlei; He, Hongyan
作者单位1.Chinese Acad Sci, Innovat Acad Green Manufacture, Beijing 100190, Peoples R China
2.Zhengzhou Univ, Henan Inst Adv Technol, Zhengzhou 450003, Peoples R China
3.Chinese Acad Sci, Beijing Key Lab Ion Liquids Clean Proc, State Key Lab Multiphase Complex Syst, CAS Key Lab Green Proc & Engn,Inst Proc Engn, Beijing 100190, Peoples R China
4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Ling, Yulong,Li, Kun,Wang, Mi,et al. Revisiting the structure, interaction, and dynamical property of ionic liquid from the deep learning force field[J]. JOURNAL OF POWER SOURCES,2023,555:8.
APA Ling, Yulong.,Li, Kun.,Wang, Mi.,Lu, Junfeng.,Wang, Chenlu.,...&He, Hongyan.(2023).Revisiting the structure, interaction, and dynamical property of ionic liquid from the deep learning force field.JOURNAL OF POWER SOURCES,555,8.
MLA Ling, Yulong,et al."Revisiting the structure, interaction, and dynamical property of ionic liquid from the deep learning force field".JOURNAL OF POWER SOURCES 555(2023):8.

入库方式： OAI收割

来源：过程工程研究所

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