Active Learning Training Strategy for Predicting O Adsorption Free Energy on Perovskite Catalysts using Inexpensive Catalyst Features

The study reports geometric features that can be generalized to catalyst compositions beyond d-band metals. By employing these into an active learning workflow, required prediction accuracies are achieved without expensive an-initio features. workflow itself enables effective data labelling, but feeding it a forecasting model further limits the number of labelling.