Doping is the most common strategy employed in the development of new and improved materials. However, predicting the effects of doping on the atomic-scale structure of a material is often difficult or limited to high-end experimental techniques. Doping can induce phase separation in a material, undermining the material’s stability. A further complication is that dopant atoms can segregate to interfaces in a material such as grain boundaries (GBs), with consequences for key macroscopic properties of the material such as its conductivity. Here, we demonstrate a computational methodology based on semi-grand canonical Monte Carlo which can be used to probe these phenomena at the atomic scale for metal oxide solid solutions. The methodology can provide precise predictions of the thermodynamic conditions at which phase separation occurs. It can also provide the segregation patterns exhibited by GBs at given conditions. We apply the methodology to one of the most important catalytic materials, ceria–zirconia. Our calculations reveal an interesting richness in the GB segregation in this system. Most GBs we examined exhibited continuous increases in Zr segregation upon Zr doping, with a concomitant reduction in the formation enthalpies of the GBs. However, a few GBs exhibited no segregation at low temperatures. We also observed evidence of first-order complexion transitions in some GBs.

中文翻译：

---

原子模拟中氧化铈-氧化锆中晶界相关的偏析和相分离

掺杂是开发新材料和改进材料时最常用的策略。然而，预测掺杂对材料原子级结构的影响通常很困难或仅限于高端实验技术。掺杂会引起材料中的相分离，从而破坏材料的稳定性。更复杂的是，掺杂剂原子可能会偏析到材料中的界面，例如晶界 (GB)，从而影响材料的关键宏观特性，例如其电导率。在这里，我们演示了一种基于半正则蒙特卡罗的计算方法，该方法可用于在金属氧化物固溶体的原子尺度上探测这些现象。该方法可以精确预测发生相分离的热力学条件。它还可以提供 GB 在给定条件下表现出的分离模式。我们将该方法应用于最重要的催化材料之一——氧化铈-氧化锆。我们的计算揭示了该系统中 GB 分离的有趣丰富性。我们检查的大多数GBs在Zr掺杂后表现出Zr偏析的持续增加，同时GBs的形成焓降低。然而，一些GB在低温下没有表现出偏析。我们还在一些 GB 中观察到一级肤色转变的证据。