Decoratypes: An Extensible Crystal Taxonomy for Machine Learning-Guided Materials Discovery
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866916994313355264 |
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| author | Miller, Kyle D. Campbell, Michele Puggioni, Danilo Rondinelli, James M. |
| author_facet | Miller, Kyle D. Campbell, Michele Puggioni, Danilo Rondinelli, James M. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_07853 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Decoratypes: An Extensible Crystal Taxonomy for Machine Learning-Guided Materials Discovery Miller, Kyle D. Campbell, Michele Puggioni, Danilo Rondinelli, James M. Materials Science 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. |
| title | Decoratypes: An Extensible Crystal Taxonomy for Machine Learning-Guided Materials Discovery |
| topic | Materials Science |
| url | https://arxiv.org/abs/2509.07853 |