Development of machine learning and empirical interatomic potentials for the binary Zr-Sn system

Abstract

Zirconium alloys are pivotal structural materials in nuclear reactors. Enhancing their properties and performance necessitates a profound understanding of the interactions between alloying elements and lattice defects. However, the atomic-level mechanisms that account for the effects of tin (Sn) on irradiation-induced defect evolution in zirconium (Zr) are not well understood yet. To bridge this gap, we conducted extensive first-principles calculations and utilized the obtained data to develop two interatomic potentials for the Zr-Sn system: a Zr-Sn moment tensor machine learning interatomic potential (MTP) and a modified embedded atom potential (MEAM) built upon existing unary ones. The reliability of the constructed potentials was validated through systematically comparing relevant physical properties of pure Zr, Sn metals, and Zr-Sn solid solutions, calculated using these potentials, to values obtained from density functional theory calculations and/or experiments. The developed potentials exhibit high fidelity and accuracy in capturing the behavior of the Zr-Sn system. Their applicability in terms of both reliability and computational efficiency was also discussed. These potentials provide a solid foundation for further exploration of the atomic-scale behavior of the Zr-Sn alloy systems, thereby facilitating the continuous optimization and application of the zirconium alloys.