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.
@article{arxiv.2509.07853,
title = {Decoratypes: An Extensible Crystal Taxonomy for Machine Learning-Guided Materials Discovery},
author = {Kyle D. Miller and Michele Campbell and Danilo Puggioni and James M. Rondinelli},
journal= {arXiv preprint arXiv:2509.07853},
year = {2025}
}