Topology Augmented with Geometry in the Assembly of Structural Databases: Kagome Intermetallics

Gulay, Nataliya L, Wen, Dongsheng ORCID: 0000-0002-0728-0231, Griffiths, Joshua E, Clymo, Judith, Daniels, Luke M ORCID: 0000-0002-7077-6125, Alaria, Jonathan ORCID: 0000-0001-5868-0318, Dyer, Matthew S ORCID: 0000-0002-4923-3003, Claridge, John B ORCID: 0000-0003-4849-6714 and Rosseinsky, Matthew J ORCID: 0000-0002-1910-2483 (2025) Topology Augmented with Geometry in the Assembly of Structural Databases: Kagome Intermetallics ADVANCED SCIENCE, 12 (35). e17041-. ISSN 2198-3844, 2198-3844

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Abstract

Creation of well-curated databases tailored to specific structural motifs can underpin and drive materials discovery, as the properties of materials are governed by composition and structure. The role of such motifs in directing the intricate interplay between structure and properties is exemplified by intermetallic compounds with structures that contain kagome layers that exhibit a variety of exotic physical states. Two prevailing approaches have previously been applied to identify such materials: evaluation of structural topology or geometry assessment, however, both present limitations if deployed individually. We augment topological screening with geometrical filtering to allow versatile control over the identification of kagome layers. Applying this approach with minimal further constraints labels over 9000 kagome-containing intermetallics which are assigned to four structural classes, revealing connections between symmetry, composition, direct space structure, and flatband electronic structures in reciprocal space. A machine learning model is used to predict new element combinations that favour the formation of kagome layers. Several highly-ranked phase fields correspond to known kagome-containing materials that were absent from the training dataset, demonstrating that the workflow can identify chemistries affording kagome layers. This motivates the extension of the approach beyond kagome to other property-conferring motifs, such as honeycomb, square planar or triangular plane nets.

Item Type:	Article
Uncontrolled Keywords:	database, intermetallic phases, kagome, machine learning, structural nets
Divisions:	Faculty of Science & Engineering Faculty of Science & Engineering > School of Physical Sciences
Depositing User:	Symplectic Admin
Date Deposited:	07 Aug 2025 08:16
Last Modified:	16 Jun 2026 17:07
DOI:	10.1002/advs.202417041
Open Access URL:	https://doi.org/10.1002/advs.202417041
Related Websites:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3193948
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