文献类型： 外文期刊

作者： Guan, Shu-Hui ¹ ; Shang, Cheng ¹ ; Liu, Zhi-Pan ¹ ;

作者机构： 1.Fudan Univ, Dept Chem, Key Lab Computat Phys Sci, Collaborat Innovat Ctr Chem Energy Mat,Shanghai K, Shanghai 200433, Peoples R China

2.Shanghai Acad Agr Sci, Shanghai 201403, Peoples R China

期刊名称：JOURNAL OF PHYSICAL CHEMISTRY C （ 影响因子：4.126； 五年影响因子：4.467 ）

ISSN： 1932-7447

年卷期： 2020 年 124 卷 28 期

页码：

收录情况： SCI

摘要： 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.