Properties of radiation defects and threshold energy of displacement in zirconium hydride obtained by new deep-learning potential

doi:10.1088/1674-1056/ad362b

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}$.

Keywords: zirconium hydride deep learning potential radiation defects molecular dynamics threshold energy of displacement

Received: 03 February 2024
Revised: 15 March 2024
Accepted manuscript online: 21 March 2024

PACS:	61.66.Dk	(Alloys )
	07.05.Mh	(Neural networks, fuzzy logic, artificial intelligence)
	34.20.Cf	(Interatomic potentials and forces)
	82.20.Wt	(Computational modeling; simulation)

Fund: Project supported by the Joint Fund of the National Natural Science Foundation of China–“Ye Qisun” Science Fund (Grant No. U2341251).

Corresponding Authors: Zhi-Xiao Liu, Hui-Qiu Deng
E-mail: zxliu@hnu.edu.cn;hqdeng@hnu.edu.cn

Cite this article:

Xi Wang(王玺), Meng Tang(唐孟), Ming-Xuan Jiang(蒋明璇), Yang-Chun Chen(陈阳春), Zhi-Xiao Liu(刘智骁), and Hui-Qiu Deng(邓辉球) Properties of radiation defects and threshold energy of displacement in zirconium hydride obtained by new deep-learning potential 2024 Chin. Phys. B 33 076103

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Properties of radiation defects and threshold energy of displacement in zirconium hydride obtained by new deep-learning potential

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