Plastic deformation of superionic water ices

Abstract

Due to their potential role in the peculiar geophysical properties of the ice giants Neptune and Uranus, there has been a growing interest in superionic (SI) phases of water ice. So far, however, little attention has been given to their mechanical properties, even though plastic deformation processes in the interiors of planets are known to affect long-term processes, such as plate tectonics and mantle convection. Here, using density functional theory calculations and machine learning techniques, we assess the mechanical response of high-pressure/temperature solid phases of water in terms of their ideal shear strength (ISS) and dislocation behavior. The ISS results are well described by the renormalized Frenkel model of ideal strength and indicate that the SI ices are expected to be highly ductile. This is further supported by deep neural network molecular dynamics simulations for the behavior of lattice dislocations for the SI face-centered cubic (fcc) phase. Dislocation velocity data indicate effective shear viscosities that are orders of magnitude smaller than that of Earth’s lower mantle, suggesting that the plastic flow of the internal icy layers in Neptune and Uranus may be significantly faster than previously foreseen.

Lattice thermal conductivity and mechanical properties of the single-layer penta-NiN2 explored by a deep-learning interatomic potential

“…42 Matusalem et al employed AIMD computations and deep learning techniques to scrutinize the plastic deformation behavior of high-pressure, high-temperature water ices. 43 Additionally, Du et al used the deep potential model to predict the melting points and elastic constants of face-centered cubic Cu. 44 In order to explore the segregation of W into ZrB 2 grain boundaries and the strengthening effect on grain boundaries induced by segregation at high temperatures, Dai et al developed a deep learning potential for the Zr 1− x W x B 2 system.…”