中国物理B ›› 2026, Vol. 35 ›› Issue (1): 13101-013101.doi: 10.1088/1674-1056/ae15f1

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Structures and dynamics of helium in liquid lithium: A study by deep potential molecular dynamics

Xinyu Zhu(朱新宇)1,†, Jianchuan Liu(刘建川)2,†,‡, Tao Chen(陈涛)1, Xinyue Xie(谢炘玥)1, Jin Wang(王进)3, Yi Xie(谢懿)4, Chenxu Wang(王晨旭)3, and Mohan Chen(陈默涵)1,‡   

  1. 1 HEDPS, CAPT, School of Physics and College of Engineering, Peking University, Beijing 100871, China;
    2 School of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610039, China;
    3 State Key Laboratory of Nuclear Physics and Technology, CAPT, Peking University, Beijing 100871, China;
    4 College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
  • 收稿日期:2025-08-07 修回日期:2025-10-11 接受日期:2025-10-22 发布日期:2025-12-22
  • 通讯作者: Jianchuan Liu, Mohan Chen E-mail:liujianchuan@xhu.edu.cn;mohanchen@pku.edu.cn
  • 基金资助:
    Project supported by the Excellence Research Group Program for Multiscale Problems in Nonlinear Mechanics of the National Natural Science Foundation of China (Grant No. 12588201), the National Key R&D Program of China (Grant No. 2025YFB3003603), the National Natural Science Foundation of China (Grant No. 12135002), the Fundamental Research Funds for the Central Universities, Peking University, and the Beijing Natural Science Foundation (Grant No. QY23030). The numerical simulations were performed on the high-performance computing platform of CAPT and the “Bohrium” cloud computing platform of DP Technology Co., LTD.

Structures and dynamics of helium in liquid lithium: A study by deep potential molecular dynamics

Xinyu Zhu(朱新宇)1,†, Jianchuan Liu(刘建川)2,†,‡, Tao Chen(陈涛)1, Xinyue Xie(谢炘玥)1, Jin Wang(王进)3, Yi Xie(谢懿)4, Chenxu Wang(王晨旭)3, and Mohan Chen(陈默涵)1,‡   

  1. 1 HEDPS, CAPT, School of Physics and College of Engineering, Peking University, Beijing 100871, China;
    2 School of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610039, China;
    3 State Key Laboratory of Nuclear Physics and Technology, CAPT, Peking University, Beijing 100871, China;
    4 College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
  • Received:2025-08-07 Revised:2025-10-11 Accepted:2025-10-22 Published:2025-12-22
  • Contact: Jianchuan Liu, Mohan Chen E-mail:liujianchuan@xhu.edu.cn;mohanchen@pku.edu.cn
  • Supported by:
    Project supported by the Excellence Research Group Program for Multiscale Problems in Nonlinear Mechanics of the National Natural Science Foundation of China (Grant No. 12588201), the National Key R&D Program of China (Grant No. 2025YFB3003603), the National Natural Science Foundation of China (Grant No. 12135002), the Fundamental Research Funds for the Central Universities, Peking University, and the Beijing Natural Science Foundation (Grant No. QY23030). The numerical simulations were performed on the high-performance computing platform of CAPT and the “Bohrium” cloud computing platform of DP Technology Co., LTD.

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摘要： Current experimental techniques still face challenges in clarifying the structural and dynamic properties of helium (He) in liquid lithium (Li). A critical example of this technical hurdle is the formation of He bubbles, which significantly affects the transport of He within liquid Li — a vital aspect when considering liquid Li as a plasma-facing material in nuclear fusion reactors. We develop a machine-learning-based deep potential (DP) with ab initio accuracy for the Li—He system and perform molecular dynamics simulations at temperatures ranging from 470 K to 1270 K with a wide range of He concentrations. We observe that He atoms exhibit a tendency to aggregate and form clusters and bubbles in liquid Li. Notably, He clusters exhibit a significant increase in size at elevated temperatures and high concentrations of He, accompanied by the phase separation of Li and He atoms. We also observe an anomalous non-linear relationship between the diffusion coefficient of He and temperature, which is attributed to the larger cluster size at higher temperatures. Our study provides a deeper understanding of the behavior of He in liquid Li and further supports the potential application of liquid Li under extreme conditions.

关键词: MD simulation, machine-learning-based deep potential, plasma-facing material, He in liquid Li

Abstract: Current experimental techniques still face challenges in clarifying the structural and dynamic properties of helium (He) in liquid lithium (Li). A critical example of this technical hurdle is the formation of He bubbles, which significantly affects the transport of He within liquid Li — a vital aspect when considering liquid Li as a plasma-facing material in nuclear fusion reactors. We develop a machine-learning-based deep potential (DP) with ab initio accuracy for the Li—He system and perform molecular dynamics simulations at temperatures ranging from 470 K to 1270 K with a wide range of He concentrations. We observe that He atoms exhibit a tendency to aggregate and form clusters and bubbles in liquid Li. Notably, He clusters exhibit a significant increase in size at elevated temperatures and high concentrations of He, accompanied by the phase separation of Li and He atoms. We also observe an anomalous non-linear relationship between the diffusion coefficient of He and temperature, which is attributed to the larger cluster size at higher temperatures. Our study provides a deeper understanding of the behavior of He in liquid Li and further supports the potential application of liquid Li under extreme conditions.

Key words: MD simulation, machine-learning-based deep potential, plasma-facing material, He in liquid Li

中图分类号:  (Molecular dynamics and other numerical methods)

  • 31.15.xv
31.15.at (Molecule transport characteristics; molecular dynamics; electronic structure of polymers) 31.15.E (Density-functional theory) 31.15.es (Applications of density-functional theory (e.g., to electronic structure and stability; defect formation; dielectric properties, susceptibilities; viscoelastic coefficients; Rydberg transition frequencies))