Issue 1, 2023

Grouped representation of interatomic distances as a similarity measure for crystal structures

Abstract

Determining how similar two materials are in terms of both atomic composition and crystallographic structure remains a challenge, the solution of which would enable generalised machine learning using crystal structure data. We demonstrate a new method of describing crystal structures based on interatomic distances, termed the Grouped Representation of Interatomic Distances (GRID). This fast to compute descriptor can equally be applied to crystalline or disordered materials, and encodes additional information beyond pairwise distances, such as coordination environments. Combined with earth mover's distance as a measure of similarity, we show that GRID is able to quantitatively compare materials involving both short- and long-range structural variation. Using this new material descriptor, we show that it can accurately predict bulk moduli using a simple nearest-neighbour model, and that the resulting similarity shows good generalisability across multiple materials properties.

Graphical abstract: Grouped representation of interatomic distances as a similarity measure for crystal structures

Supplementary files

Article information

Article type
Paper
Submitted
02 Jun 2022
Accepted
15 Nov 2022
First published
24 Nov 2022
This article is Open Access
Creative Commons BY license

Digital Discovery, 2023,2, 81-90

Grouped representation of interatomic distances as a similarity measure for crystal structures

R. Zhang, S. Seth and J. Cumby, Digital Discovery, 2023, 2, 81 DOI: 10.1039/D2DD00054G

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

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