  Discovering new materials that efficiently catalyze the oxygen reduction and
evolution reactions is critical for facilitating the widespread adoption of
solid oxide fuel cell and electrolyzer (SOFC/SOEC) technologies. Here, we
develop machine learning (ML) models to predict perovskite catalytic properties
critical for SOFC/SOEC applications, including oxygen surface exchange, oxygen
diffusivity, and area specific resistance (ASR). The models are based on
trivial-to-calculate elemental features and are more accurate and dramatically
faster than the best models based on ab initio-derived features, potentially
eliminating the need for ab initio calculations in descriptor-based screening.
Our model of ASR enables temperature-dependent predictions, has well calibrated
uncertainty estimates and online accessibility. Use of temporal
cross-validation reveals our model to be effective at discovering new promising
materials prior to their initial discovery, demonstrating our model can make
meaningful predictions. Using the SHapley Additive ExPlanations (SHAP)
approach, we provide detailed discussion of different approaches of model
featurization for ML property prediction. Finally, we use our model to screen
more than 19 million perovskites to develop a list of promising cheap,
earth-abundant, stable, and high performing materials, and find some top
materials contain mixtures of less-explored elements (e.g., K, Bi, Y, Ni, Cu)
worth exploring in more detail.
