A New Data-Driven Interacting-Defect Model Describing Nanoscopic Grain Boundary Compositions in Ceramics
A new data-driven interacting-defect model has quantitatively described the nanoscopic com- position of high solute concentrations at grain boundaries in ion-conducting ceramics. The successful model is a data-driven Cahn-Hilliard methodology for interfaces and surfaces, introduced and demonstrated in this report. The model is applied to high spatial resolu- tion composition data gathered at grain boundaries in calcium-doped ceria. The statistical methodology for the data-driven procedure shows definitively that gradient terms are re- quired to quantitatively describe the local grain boundary composition data. The model additionally shows co-accumulation 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.