Resolving the Temperature and Composition Dependence of Ion Conductivity for Yttria-Stabilized Zirconia from Machine Learning Simulation

Abstract

The temperature and composition dependence of the ion conductivity for yttria-stabilized zirconia (YSZ) have been hotly studied over the past 50 years. Due to the sluggish oxygen anion diffusion and the low doping of oxide, the computation of ion conductivity traditionally has to be performed with empirical force field potentials in order to achieve the required long timescale, which, however, fails to reproduce some critical observations in experiment, e.g., the conductivity maximum achieved at 8 mol % YSZ at the operating temperatures. Here by using our recently developed Y−Zr−O global neural network (G-NN) potential, we are able to carry out a series of long-time molecular dynamics simulations for YSZ at 6.7, 8, 10, and 14.3 mol % over a wide temperature range (800−2000 K). This finally quantitatively resolves the effects of temperature and composition on the ion conductivity. We confirm the key experimental findings that (i) 8 mol % YSZ has the highest ion conductivity, 0.16−0.51 S/cm, at 1200−1600 K, agreeing with 0.16−0.55 S/cm in experiment, and the maximum conductivity shifts to the higher Y composition above 1600 K and (ii) over the wide temperature range (800−2000 K) the ion conductivity of 8YSZ exhibits the non-Arrhenius behaviors with two different activation energies. The physical origin for these peculiar phenomena is revealed at the atomic level by analyzing the MD pathways. The presence of monoclinic phase and the aggregation of oxygen vacancy along ⟨112⟩ are two key factors to retard oxygen diffusion. For 8 mol % YSZ, the oxygen movement is dominated by local vibrations below 1000 K but becomes delocalized above 1000 K, which results in the gradual aggregation of oxygen vacancy along a new ⟨112⟩ direction. Our results demonstrate that the G-NN potential from unbiased machine learning of the global potential energy surface can meet the high standard in both accuracy and speed required for material simulation.

Molecular dynamics study of oxygen-ion diffusion in yttria-stabilized zirconia grain boundaries

“…Furthermore, even though it is well known that experimentally, 8YSZ presents the highest oxygen diffusivity for a wide range of temperatures, in MD, the highest diffusivity under 2000 K is often found for Y 2 O 3 content below 8 mol%. 17,63,76–78 In contrast, at higher temperatures (1800−2300 K), 8 and 11 mol% are the optimum concentrations for O 2− mobility. Furthermore, it should be noted that according to our calculations, oxygen diffusion should be more impeded for concentrations ≥ 14 mol% at temperatures between 700–2300 K.…”

“…13,15,63 Furthermore, even though it is well known that experimentally, 8YSZ presents the highest oxygen diffusivity for a wide range of temperatures, in MD, the highest diffusivity under 2000 K is oen found for Y 2 O 3 content below 8 mol%. 17,63,[76][77][78] In Fig. 8 Mean square displacements at 2300 K for regions of 20 Å along z axis containing the GB core, (21À1) and (À511) grain interior regions of 8YSZ mixed GB system.…”

Modelling Bulk Electrolytes and Electrolyte Interfaces with Atomistic Machine Learning

“…A summary of these works is given in Table 1. Compared to experiments, the predicted diffusion activation energy has an error between 20 meV to 130 meV [134–140] …”

“…used uncertainty based active learning to conduct exploration, labelling and training steps, where the maximum uncertainty of forces within a predefined window was used as the criteria to select queries for labelling. Guan et al [140] . explored a global optimization technique, i. e. Stochastic Surface Walking (SSW), in the data generation for training a neutral network potential to investigate the oxygen anion diffusion.…”

Artificial Intelligence in Biomedical Applications of Zirconia

“…As an essential ion-conducting material, yttria-stabilized zirconia (YSZ) is widely applied in solid oxide fuel cells, oxygen sensors, and solid oxide electrolysis cells. Guan (71). They create the first Y-Zr-O ternary global neural network (G-NN) potential, construct the thermodynamic convex hull diagram for YSZ with different Y concentrations, and determine the most stable phase atom at each Y concentration, the so-called global minimum (GM).…”