Work

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

Year: 2025

Type: article

Abstract: 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.

Source: Physical Review Materials

DOI: 10.1103/14yr-q3z1

Authors: Kyle D. Miller, Michele Campbell, Danilo Puggioni, James M. Rondinelli

Institution: Northwestern University

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, Semiconductor Research Corporation