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

IF 14.3 1区 材料科学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Nataliya L Gulay, Dongsheng Wen, Joshua E Griffiths, Judith Clymo, Luke M Daniels, Jonathan Alaria, Matthew S Dyer, John B Claridge, Matthew J Rosseinsky
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引用次数: 0

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.

结构数据库装配中的拓扑扩充与几何:Kagome金属间化合物。
由于材料的特性是由组成和结构决定的,因此为特定的结构主题量身定制精心策划的数据库可以支持和推动材料的发现。这种基序在指导结构和性质之间复杂的相互作用中的作用,可以通过金属间化合物的结构来例证,这些金属间化合物的结构包含具有各种奇异物理状态的kagome层。以前已经应用了两种流行的方法来识别这种材料:结构拓扑评估或几何评估,然而,如果单独部署,两者都存在局限性。我们增加拓扑筛选与几何滤波,以允许通用控制识别kagome层。应用这种方法,在最小的进一步约束下,标记了超过9000个含有kagome的金属间化合物,它们被分配到四个结构类,揭示了对称、组成、直接空间结构和互反空间中的平面带电子结构之间的联系。机器学习模型用于预测有利于kagome层形成的新元素组合。几个高阶相场对应于训练数据集中缺失的已知含kagome的材料,表明该工作流可以识别提供kagome层的化学物质。这激发了将方法从kagome扩展到其他赋予属性的主题,例如蜂窝,方形平面或三角形平面网。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Advanced Science
Advanced Science CHEMISTRY, MULTIDISCIPLINARYNANOSCIENCE &-NANOSCIENCE & NANOTECHNOLOGY
CiteScore
18.90
自引率
2.60%
发文量
1602
审稿时长
1.9 months
期刊介绍: Advanced Science is a prestigious open access journal that focuses on interdisciplinary research in materials science, physics, chemistry, medical and life sciences, and engineering. The journal aims to promote cutting-edge research by employing a rigorous and impartial review process. It is committed to presenting research articles with the highest quality production standards, ensuring maximum accessibility of top scientific findings. With its vibrant and innovative publication platform, Advanced Science seeks to revolutionize the dissemination and organization of scientific knowledge.
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