Enhancing Solid Oxide Fuel Cells Development through Bayesian Active Learning

Abstract

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.