Machine Learning-Accelerated First-Principles Molecular Dynamics Explains Anomalous Lattice Thermal Expansion in BaZr0.78Y0.22O3-δ

Fuel cells are a vital clean energy technology that converts chemical energy directly into electricity with high efficiency, making them a cornerstone of a sustainable energy future. Herein, we investigate the thermal and chemical lattice expansion behavior of hydrated BaZr0.78Y0.22O3-δ using machine learning-accelerated ab initio molecular dynamics simulations. Our results reproduce the experimentally observed nonmonotonic and anomalous temperature dependence of lattice expansion, which we attribute to the competing effects of thermal expansion and dehydration, two mechanisms that influence the lattice expansion in opposite directions. This work provides fundamental insight into the coupling between hydration thermodynamics and lattice dynamics in BaZr1–xYxO3-δ, a prototypical proton-conducting perovskite, and offers a predictive framework for modeling temperature- and humidity-dependent behavior, critical to solid oxide fuel cell performance.