New Data-Driven Interacting-Defect Model Describing Nanoscopic Grain Boundary Compositions in Ceramics

A data-driven, interacting-defect model for inhomogeneous systems has quantitatively described the nanoscopic composition of high solute concentrations at grain boundaries in ion-conducting ceramics. The data-driven Cahn–Hilliard model was applied to high-spatial-resolution composition data gathered at grain boundaries in calcium-doped ceria. The statistical methodology for the data-driven procedure shows definitively that an inhomogeneous thermodynamics approach (gradient terms) is required to quantitatively describe the local grain boundary composition. The model additionally shows coaccumulation of negatively charged acceptor dopants and positively charged oxygen vacancies at the interface, which is qualitatively in accordance with atom probe tomography evidence in acceptor-doped ceria. The reported model is the first to quantitatively explain microscopic experiments in ion-conducting ceramics.