Stability and anion diffusion kinetics of Yttria-stabilized zirconia resolved from machine learning global potential energy surface exploration

Yttria-stabilized zirconia (YSZ) is an important material with wide industrial applications particularly for its good conductivity in oxygen anion transportation. The conductivity is known to be sensitive to Y concentration: 8 mol. % YSZ (8YSZ) achieves the best performance, which, however, degrades remarkably under ∼1000 °C working conditions. Here, using the recently developed SSW-NN method, stochastic surface walking global optimization based on global neural network potential (G-NN), we establish the first ternary Y–Zr–O G-NN potential by fitting 28 803 first principles dataset screened from more than 107 global potential energy surface (PES) data and explore exhaustively the global PES of YSZ at different Y concentrations. Rich information on the thermodynamics and the anion diffusion kinetics of YSZ is, thus, gleaned, which helps resolve the long-standing puzzles on the stability and conductivity of the 8YSZ. We demonstrate that (i) 8YSZ is the cubic phase YSZ with the lowest possible Y concentrations. It is thermodynamically unstable, tending to segregate into the monoclinic phase of 6.7YSZ and the cubic phase of 20YSZ. (ii) The O anion diffusion in YSZ is mediated by O vacancy sites and moves along the ⟨100⟩ direction. In 8YSZ and 10YSZ, despite different Y concentrations, their anion diffusion barriers are similar, ∼ 1 eV, but in 8YSZ, the O diffusion distance is much longer due to the lack of O vacancy aggregation along the ⟨112⟩ direction. Our results illustrate the power of G-NN potential in solving challenging problems in material science, especially those requiring a deep knowledge on the complex PES.