Deep-learning interatomic potentials of the ε - ZrX2 series (X = H, D, and T)

KY Cheng and XJ Cheng and MY Shi and XY Zhou and JH Deng and G Jiang and JG Du, PHYSICAL REVIEW E, 111, 055303 (2025).

DOI: 10.1103/PhysRevE.111.055303

The e-ZrH2 species act as an important component in zirconium-based composite hydrides, which have various applications in nuclear energy, hydrogen storage, and catalysis. In this work, deep-learning interatomic potentials for e-ZrH2 have been developed by training density functional theory (DFT) data. The results indicate that the developed deep-learning interatomic potentials (DP) can accurately predict the structural, mechanical, and thermodynamic properties of e-ZrH2 with DFT level accuracy. These deep-learning interatomic potentials are shown to be superior to the conventional modified embedded atom method potential. The H-isotope effect was also taken into account in constructing the deep-learning interatomic potentials, which facilitates molecular dynamic (MD) simulations under irradiation conditions. The development of these deep-learning interatomic potentials offers improved options for MD simulations of e-ZrX2 (X = H, D, and T).

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