Autonomous Accelerated Workflow for the Discovery of High-Entropy Oxide Electrodes for Protonic Ceramic Fuel Cells

We develop and implement an autonomous multi-fidelity computational workflow to explore the chemical space of high-entropy perovskite oxide materials with general formula ABO₃, screening for stable, high-performance cathodes for low-temperature protonic ceramic fuel cells. The workflow, implemented in the framework of Density Functional Theory (DFT), is based on the calculation of thermodynamic, electric and kinetic properties, which include phase and electrochemical stability, electronic conductivity, and ionic diffusivity. To accelerate the calculation of the kinetic properties, we employ accelerated methods that leverage recent advances in machine learning for materials science to predict transition barriers for ionic diffusion. The computational cost of the workflow is additionally decreased, while retaining the quality of results, through a thorough examination of the required level of theory for all descriptive properties. Moreover, the aim is a general and chemistry-neutral approach that can be applied to other crystal prototypes and materials screenings.