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A Unified Poisson-Cahn Methodology for Defect Segregation Near Grain Boundaries in Oxygen-Conducting Solid Electrolytes Xiaorui Tong In the Poisson-Cahn theory, the incorporation of defect interactions and the gradient effects has made it possible to model interfaces in solid solutions of high concentrations. This study aimed at building a unified Poisson-Cahn methodology capable of conforming to physically reasonable parameter spaces and incorporating potentially complex concentrationdependences of model parameters to enable broader future applications in various material systems. The first development established a kinetic model to predict defect segregation during thermal annealing of these systems. The complete process of dopant segregation from the initial flat distribution all the way to the equilibrium was firstly revealed, and the amount of equilibration time for dopant ions was obtained at different temperatures. Secondly, the Poisson-Cahn theory was applied to a two-step restricted equilibrium model in estimating the conductivity behavior of gadolinium-doped-ceria over a broad range of dopant concentrations. The consistency between the model predicted conductivity data and experimental measurement has demonstrated the effectiveness of this scheme. In order to specify parameter ranges with higher confidence and to gauge potential parameter dependences on defect concentrations, it is necessary to calibrate model parameters with direct grain boundary measurements. We fit the atom probe data of Nd-doped ceria with 10% and 30% dopant respectively to a restricted Poisson-Cahn equilibrium model with added polaron species but found the values for the gradient energy coefficients weren’t in agreement with what are reported in literature. This led to the formulation and implementation of a data-driven approach. Within the framework of a unified Poisson-Cahn methodology, a series of models are then designed through systematically incorporating different interaction energies and gradient energy effects in the form of discrepancy functions. Bayesian calibration is employed for each model using grain boundary compositions of Ca-doped ceria with 2%, 5% and 10% dopant concentrations. The development of this model framework has shown how the model capability improves by including key model parameters and revealed the concentration dependences of model parameters through quantitatively evaluating the calibration results. An optimal model structure was obtained with the guidance of the Bayes factor.