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,w...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.展开更多
基金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)+1 种基金the National Natural Science Foundation of China(Grant No.12135002)the Fundamental Research Funds for the Central Universities,Peking University,the Beijing Natural Science Foundation(Grant No.QY23030)。
文摘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.
