From Atoms to Aggregates: Testing the Role of Grain Boundary Complexion Transitions in Deformation of Functional Ceramics
In this DPhil project, we will investigate how interfacial phase transitions—known as grain boundary complexion transitions—govern the mechanical and functional behaviour of ceramics used in energy technologies. Grain boundaries critically influence both ion transport and mechanical integrity, yet their role during deformation remains poorly understood. Building on recent evidence that complexion transitions can alter properties such as sintering, creep, and fracture, this project integrates advanced microscopy and mechanical testing with multi-scale computational modelling, aiming to link atomic-scale interface chemistry with macroscopic behaviour.
Experimental data from model systems will provide the structural foundation for simulation frameworks that couple density functional theory and machine-learning-based interatomic potential (MLIP) models with crystal plasticity and phase-field models. These tools will reveal how complexion-mediated interfaces affect stress distribution, plasticity, and fracture. The ultimate goal is to construct predictive structure–property–process maps that describe how grain boundary complexions can be engineered to enhance both mechanical resilience and functional performance in ceramics central to solid-state batteries, fuel cells, and hydrogen technologies.
References:
Paşca, L.-B., Liu, Y., Anker, A. S., Steier, L., & Deringer, V. L. (2025). Machine-learning-driven modelling of amorphous and polycrystalline BaZrS3. Journal of Materials Chemistry A, 13(41), 35447-35454. https://doi.org/10.1039/d5ta04536c
Salama, H., Kundin, J., Shchyglo, O., Mohles, V., Marquardt, K., & Steinbach, I. (2020). Role of inclination dependence of grain boundary energy on the microstructure evolution during grain growth. Acta Materialia, 188, 641-651. https://doi.org/10.1016/j.actamat.2020.02.043
The core project aim is to exploit multiscale framework for modeling grain‐boundary complexion transitions
Phase-field simulations following the approach of Salama et al. (Acta Mater. 2020) capture mesoscale microstructural evolution (a-d), while machine-learning-driven simulations (see an example from Paşca et al., J. Mater. Chem. A 2025), resolve atomic-scale structures and energetics within boundaries (e, f). In the project, we will aim to couple these methods enables a consistent description of structural, chemical, and thermodynamic complexion transitions. Figure based on Salama et al. (2020) and Paşca et al. (2025).(2020)
