关键词: zirconium hydride, deep learning potential, radiation defects, molecular dynamics, threshold energy of displacement

Abstract: Zirconium hydride (ZrH$_{2}$) is an ideal neutron moderator material. However, radiation effect significantly changes its properties, which affect its behavior and the lifespan of the reactor. The threshold energy of displacement is an important quantity of the number of radiation defects produced, which helps us to predict the evolution of radiation defects in ZrH$_{2}$. Molecular dynamics (MD) and ab initio molecular dynamics (AIMD) are two main methods of calculating the threshold energy of displacement$.$ The MD simulations with empirical potentials often cannot accurately depict the transitional states that lattice atoms must surpass to reach an interstitial state. Additionally, the AIMD method is unable to perform large-scale calculation, which poses a computational challenge beyond the simulation range of density functional theory. Machine learning potentials are renowned for their high accuracy and efficiency, making them an increasingly preferred choice for molecular dynamics simulations. In this work, we develop an accurate potential energy model for the ZrH$_{2}$ system by using the deep-potential (DP) method. The DP model has a high degree of agreement with first-principles calculations for the typical defect energy and mechanical properties of the ZrH$_{2}$ system, including the basic bulk properties, formation energy of point defects, as well as diffusion behavior of hydrogen and zirconium. By integrating the DP model with Ziegler-Biersack-Littmark (ZBL) potential, we can predict the threshold energy of displacement of zirconium and hydrogen in $\varepsilon $-ZrH$_{2}$.

Key words: zirconium hydride, deep learning potential, radiation defects, molecular dynamics, threshold energy of displacement