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arXiv · 2509.07853

Decoratypes: An Extensible Crystal Taxonomy for Machine Learning-Guided Materials Discovery

Kyle D. Miller·Michele Campbell·Danilo Puggioni·James M. Rondinelli
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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.

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Kyle D. Miller, Michele Campbell, Danilo Puggioni, James M. Rondinelli. 2025-09-09. Decoratypes: An Extensible Crystal Taxonomy for Machine Learning-Guided Materials Discovery. https://doi.org/10.1103/14yr-q3z1

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