Citation: Feng Wang, Jun Cheng. Understanding the solvation structures of glyme-based electrolytes by machine learning molecular dynamics[J]. Chinese Journal of Structural Chemistry, ;2023, 42(9): 100061. doi: 10.1016/j.cjsc.2023.100061 shu

Understanding the solvation structures of glyme-based electrolytes by machine learning molecular dynamics

Feng Wang ,
Jun Cheng

Glyme-based electrolytes are of great interest for rechargeable lithium metal batteries due to their high stability, low vapor pressure, and non-flammability. Understanding the solvation structures of these electrolytes at the atomic level will facilitate the design of new electrolytes with novel properties. Recently, classical molecular dynamics (CMD) and ab initio molecular dynamics (AIMD) have been applied to investigate electrolytes with complex solvation structures. On one hand, classical molecular dynamics (CMD) may not provide reliable results as it requires complex parameterization to ensure the accuracy of the classical force field. On the other hand, the time scale of AIMD is limited by the high cost of ab initio calculations, which causes that solvation structures from AIMD simulations depend on the initial configurations. In order to solve the dilemma, machine learning method is applied to accelerate AIMD, and the time scale of AIMD can be extended dramatically. In this work, we present a computational study on the solvation structures of triglyme (G3) based electrolytes by using machine learning molecular dynamics (MLMD). Firstly, we investigate the effects of density functionals on the accuracy of machine learning potential (MLP), and find that PBE-D3 shows better accuracy compared to BLYP-D3. Then, the densities of electrolytes with different concentration of LiTFSI are computed with MLMD, which shows good agreement with experiments. By analyzing the solvation structures of 1 ns MLMD trajectories, we found that Li+ prefers to coordinate with a G3 and a TFSI- in equimolar electrolytes. Our work demonstrates the significance of long-time scale MLMD simulations for clarifying the chemistry of non-ideal electrolytes.
- Glyme-based electrolytes,
- Machine learning potential,
- Molecular dynamics,
- Solvation structure
1. [1]
  Xi Tang , Chunlei Zhu , Yulu Yang , Shihan Qi , Mengqiu Cai , Abdullah N. Alodhayb , Jianmin Ma . Additive regulating Li⁺ solvation structure to construct dual LiF−rich electrode electrolyte interphases for sustaining 4.6 V Li||LiCoO₂ batteries. Chinese Chemical Letters, 2024, 35(12): 110014-. doi: 10.1016/j.cclet.2024.110014
2. [2]
  Yu Peng , Yue Wang , Tian-Jiao Chen , Jing-Jing Chen , Jin-Ling Yang , Ting Gong , Ping Zhu . A fungal CYP from Beauveria bassiana with promiscuous steroid hydroxylation capabilities. Chinese Chemical Letters, 2024, 35(5): 108818-. doi: 10.1016/j.cclet.2023.108818
3. [3]
  Yao Wang , Jun Ouyang , Huadong Yuan , Jianmin Luo , Shihui Zou , Jianwei Nai , Xinyong Tao , Yujing Liu . Impact of local amorphous environment on the diffusion of sodium ions at the solid electrolyte interface in sodium-ion batteries. Chinese Chemical Letters, 2025, 36(10): 110412-. doi: 10.1016/j.cclet.2024.110412
4. [4]
  Guihuang Fang , Ying Liu , Yangyang Feng , Ying Pan , Hongwei Yang , Yongchuan Liu , Maoxiang Wu . Tuning the ion-dipole interactions between fluoro and carbonyl (EC) by electrolyte design for stable lithium metal batteries. Chinese Chemical Letters, 2025, 36(1): 110385-. doi: 10.1016/j.cclet.2024.110385
5. [5]
  Peng Wang , Guanyu Zhao , Yicai Pan , Yujing Li , Chenxi Fu , Shipeng Sun , Junqi Gai , Jinping Mu , Xue Bai , Xiaohui Li , Jinfeng Sun , Xiaodong Shi , Rui He . Dual-salt electrolyte strategy enables stable interface reaction and high-performance lithium-ion batteries at low temperature. Chinese Chemical Letters, 2025, 36(11): 111190-. doi: 10.1016/j.cclet.2025.111190
6. [6]
  Lu Li , Jianing Shen , Qinkun Xiao , Chaozheng He , Jinzhou Zheng , Chaoqin Chu , Chen Chen . Stable crystal structure prediction using machine learning-based formation energy and empirical potential function. Chinese Chemical Letters, 2025, 36(11): 110421-. doi: 10.1016/j.cclet.2024.110421
7. [7]
  Jinqi Yang , Xiaoxiang Hu , Yuanyuan Zhang , Lingyu Zhao , Chunlin Yue , Yuan Cao , Yangyang Zhang , Zhenwen Zhao . Direct observation of natural products bound to protein based on UHPLC-ESI-MS combined with molecular dynamics simulation. Chinese Chemical Letters, 2025, 36(5): 110128-. doi: 10.1016/j.cclet.2024.110128
8. [8]
  Xu He , Wenjie Gao , Jinglei Xu , Zhanjun Cheng , Wenchao Peng , Beibei Yan , Guanyi Chen , Ning Li . Machine learning-assisted construction of C=O and pyridinic N active sites in sludge-based catalysts. Chinese Chemical Letters, 2025, 36(12): 111019-. doi: 10.1016/j.cclet.2025.111019
9. [9]
  Rui Yang , Hui Li , Qingfei Meng , Wenjie Li , Jiliang Wu , Yongjin Fang , Chi Huang , Yuliang Cao . Influence of PC-based Electrolyte on High-Rate Performance in Li/CrO_x Primary Battery. Acta Physico-Chimica Sinica, 2024, 40(9): 2308053-0. doi: 10.3866/PKU.WHXB202308053
10. [10]
  Shule Liu . Application of SPC/E Water Model in Molecular Dynamics Teaching Experiments. University Chemistry, 2024, 39(4): 338-342. doi: 10.3866/PKU.DXHX202310029
11. [11]
  Xiaochen Zhang , Fei Yu , Jie Ma . Cutting-Edge Applications of Multi-Angle Numerical Simulations for Capacitive Deionization. Acta Physico-Chimica Sinica, 2024, 40(11): 2311026-0. doi: 10.3866/PKU.WHXB202311026
12. [12]
  Zonglin Li , Shihua Zou , Zining Wang , Georgeta Postole , Liang Hu , Hongying Zhao . Machine learning in electrochemical oxidation process: A mini-review. Chinese Chemical Letters, 2025, 36(8): 110526-. doi: 10.1016/j.cclet.2024.110526
13. [13]
  Yunzhe Zheng , Si Sun , Jiali Liu , Qingyu Zhao , Heng Zhang , Jing Zhang , Peng Zhou , Zhaokun Xiong , Chuan-Shu He , Bo Lai . Application of machine learning for material prediction and design in the environmental remediation. Chinese Chemical Letters, 2025, 36(9): 110722-. doi: 10.1016/j.cclet.2024.110722
14. [14]
  Bingwei Wang , Yihong Ding , Xiao Tian . Benchmarking model chemistry composite calculations for vertical ionization potential of molecular systems. Chinese Chemical Letters, 2025, 36(2): 109721-. doi: 10.1016/j.cclet.2024.109721
15. [15]
  Zhimin Song , Zhe Tang , Yu Zhang , Yanru Zhou , Xiaozheng Duan , Yan Du , Chong-Bo Ma . DNA-modulated Mo-Zn single-atom nanozymes: Insights from molecular dynamics simulations to smartphone-assisted biosensing. Chinese Chemical Letters, 2025, 36(10): 110680-. doi: 10.1016/j.cclet.2024.110680
16. [16]
  Li Lin , Song-Lin Tian , Zhen-Yu Hu , Yu Zhang , Li-Min Chang , Jia-Jun Wang , Wan-Qiang Liu , Qing-Shuang Wang , Fang Wang . Molecular crowding electrolytes for stabilizing Zn metal anode in rechargeable aqueous batteries. Chinese Chemical Letters, 2024, 35(7): 109802-. doi: 10.1016/j.cclet.2024.109802
17. [17]
  Zixing Xu , Ruiying Chen , Chuanming Hao , Qionghong Xie , Chunhui Deng , Nianrong Sun . Peptidome data-driven comprehensive individualized monitoring of membranous nephropathy with machine learning. Chinese Chemical Letters, 2024, 35(5): 108975-. doi: 10.1016/j.cclet.2023.108975
18. [18]
  Yiwen Lin , Yijie Chen , Chunhui Deng , Nianrong Sun . Integration of resol/block-copolymer carbonization and machine learning: A convenient approach for precise monitoring of glycan-associated disorders. Chinese Chemical Letters, 2024, 35(12): 109813-. doi: 10.1016/j.cclet.2024.109813
19. [19]
  Han-Bin Liu , Xiaoyu Cheng , Zhou Guo , Juan Yang , Fuwen Tan , Donghui Lan , Jian-Ping Tan , Bing Yi , Weixin Zhai , Qing-Hui Guo . CrownBind-IA: A machine learning model predicting binding constants between crown ethers and alkali metal ions. Chinese Chemical Letters, 2025, 36(12): 111149-. doi: 10.1016/j.cclet.2025.111149
20. [20]
  Yujuan Zhou , Jie Yang . Exploring the potential and challenges of molecular-squeeze adsorption in sponge-like crystals. Chinese Chemical Letters, 2025, 36(6): 111004-. doi: 10.1016/j.cclet.2025.111004

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