Decoratypes: An Extensible Crystal Taxonomy for Machine Learning-Guided Materials Discovery
Kyle D. Miller, Michele Campbell, Danilo Puggioni, James M. Rondinelli
公開日: 2025/9/9
Abstract
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 decoratypes 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.