Yttria-stabilised zirconia and gadolinium-doped ceria are key oxide ion conductors used in solid oxide fuel cells, and they are often combined in multilayer electrolyte architectures. During high-temperature fabrication and operation, cation interdiffusion can generate a mixed YSZ/GDC interdiffusion layer that is commonly associated with additional interfacial resistance. Classical molecular dynamics can provide useful atomistic insight into local cation chemistry and oxygen vacancy transport. However, empirical force fields developed separately, even when compatible and based on similar parametrisation strategies, may not provide a consistent common benchmark for comparing different oxide electrolytes, as illustrated by cases where they invert the experimentally established transport ordering between YSZ and GDC. This discrepancy motivates reference-based approaches that can bridge first-principles accuracy and molecular dynamics time and length scales.

Here, we develop a workflow to adapt foundation machine-learned interatomic potentials to YSZ, GDC, and mixed Ce, Zr, Y, Gd oxide compositions. Charge-neutral fluorite models were constructed to represent bulk-like endmembers and YSZ/GDC mixed compositions with controlled cation and oxygen vacancy environments. A consistent first-principles reference dataset was generated from strained structures, randomly displaced configurations, finite-temperature snapshots, and oxygen vacancy hop environments. Foundation potentials were then fine-tuned using nested training subsets to quantify how much first-principles data is required to achieve reliable accuracy.

The resulting models are evaluated not only by energy and force errors, but also by oxygen mean-squared displacements, tracer diffusivities, Arrhenius behaviour, and vacancy hop energetics. The results indicate that oxygen transport depends strongly on the sampled cation and oxygen vacancy environments, and that improving the fit to first-principles data does not by itself guarantee recovery of experimental transport trends. Instead, the persistent discrepancy points to an upstream limitation in the reference description, defect chemistry, or sampled vacancy environments, rather than simply to insufficient model fitting accuracy. This work establishes a physics-grounded benchmark for foundation machine-learned interatomic potentials in oxide ion conductors and provides a route toward predictive modelling of transport limitations in realistic YSZ/GDC interdiffusion layers.