Enhancing Solid Oxide Fuel Cells Development through Bayesian Active Learning

Ensuring the sustainable operation of solid-oxide fuel cells (SOFCs) requires an understanding of the components' lifespan. Multiphase-field simulation studies play a major role in understanding the underlying microstructural changes and the resulting property alterations in SOFCs over time. The primary challenge in such simulations lies in identifying a suitable model and defining its parametrization. This study presents an Active Learning framework combined with Bayesian Optimization to identify optimal model parameters to simulate the aging of nickel-gadolinium doped ceria (Ni-GDC) anodes. The study overcomes incompleteness and inconsistency of literature data, and navigates the complex, high-dimensional parameter space, by leveraging experimental microstructure data and the power of the AL framework. The successful parameter search enables simulation studies of Ni-GDC anode aging and performance during long-term SOFC operation. This approach improves the accuracy of phase-field simulations and offers a versatile tool for broader applications in SOFC development, predicting material behavior under various operational conditions.