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Doping is the most common strategy employed in the development of new andimproved materials. However, predicting the effects of doping on theatomic-scale structure of a material is often difficult or limited to high-endexperimental techniques. Doping can induce phase separation in a material,undermining the material's stability. A further complication is that dopantatoms can segregate to interfaces in a material such as grain boundaries (GBs),with consequences for key macroscopic properties of the material such as itsconductivity. Here, we describe a computational methodology based on semi-grandcanonical Monte Carlo which can be used to probe these phenomena at the atomicscale for metal oxide solid solutions. The methodology can provide precisepredictions of the thermodynamic conditions at which phase separation occurs.It can also provide the segregation patterns exhibited by GBs at givenconditions. We apply the methodology to one of the most important catalyticmaterials, ceria-zirconia. Our calculations reveal an interesting richness inthe GB segregation in this system. Most GBs we examined exhibited continuousincreases in Zr segregation upon Zr doping, with a concomitant reduction in theformation enthalpies of the GBs. However, a few GBs exhibited no segregation atlow temperatures. We also observed evidence of first-order complexiontransitions in some GBs.

Grain boundary segregation and phase separation in ceria-zirconia from atomistic simulation