Decoratypes: An extensible crystal taxonomy for machine learning-guided materials discovery

The authors present decoratypes, a new extensible crystal taxonomy that offers a more granular lens for structure--property relationships by classifying materials by hierarchical property-site mappings. For example, the framework generalizes anti-structures into polaritypes, a subclass of decoratype. They demonstrate its utility by building a polaritype-based active learning workflow for discovering ferroelectric and hyperferroelectric materials. Our approach identified six novel candidates, including three strain-activated ferroelectrics and three strain-activated hyperferroelectrics. These findings highlight how decoratypes can provide a novel perspective on the search for functional materials in underexplored chemical spaces.