Decoratype-based materials informatics: Polaritype identification, convex hull DFT calculations, training data, and predicted compounds data

We introduce decoratypes as a structure taxonomy that classifies compounds based on site decorations of specific structural prototypes. Building on this foundation, a ferroelectric materials discovery framework is developed, integrating decorotypes with an active learning approach to accelerate exploration. In addition, six novel ferroelectric candidates are predicted, including three strain-activated ferroelectrics and three strain-activated hyperferroelectrics. These findings highlight the potential of the decoratype taxonomy to enhance our understanding of structure-driven material properties and facilitate the discovery of promising yet underexplored regions of chemical space. This repository contains density functional theory (DFT) convex hull calculations, materials data used to train the polaritype-based active learning model, and candidate compounds predicted by the recommender model.