The properties of radiation defects and threshold energies of displacement in zirconium hydride obtained by a new deep-learning potential

Abstract

Abstract Zirconium hydride (ZrH2) is an ideal neutron moderator material. However, radiation effects significantly alter its properties, subsequently impacting 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 ZrH2. Molecular dynamics (MD) and ab initio molecular dynamics (AIMD) are two main methods for calculating the threshold energy of displacement. MD simulations with empirical potentials often fail to accurately depict the transitional states that lattice atoms must surpass to reach an interstitial state. Additionally, the AIMD method does not afford large-scale calculation, posing a computational challenge beyond the scope of density functional theory simulations. 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 developed an accurate potential energy model for the ZrH2 system by using the deep-potential (DP) method. The DP model has a high degree of agreement with first-principles calculations for the typic defect energies and mechanical properties of the ZrH2 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 have predicted the threshold energies of displacement of zirconium and hydrogen in ε-ZrH2.