Grouped representation of interatomic distances as a similarity measure for crystal structures. (28th November 2022)

• Record Type: Journal Article
• Authors: Zhang, Rui-Zhi; Seth, Sohan; Cumby, James
• Is Part Of: Digital discovery. Volume 2:Number 1(2023)
• Page Start: 81
• Page End: 90
• Publication Date: 2022-11-28
• Subjects: Chemistry -- Data processing -- Periodicals; Medical sciences -- Data processing -- Periodicals; Machine learning -- Periodicals; 542.85
• DOI: 10.1039/d2dd00054g
• Languages: English
• ISSNs: 2635-098X

Abstract

Abstract : Grouped Representation of Interatomic Distances (GRID) accurately quantifies similarity between crystal structures and can be used to predict physical properties. 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.