Local Structure Order Parameters and Site Fingerprints for Quantification of Coordination Environment and Crystal Structure Similarity

03 May 2019, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

Structure characterization and classification is frequently based on local environment information of all or selected atomic sites in the crystal structure. Therefore, reliable and robust procedures to find coordinated neighbors and to evaluate the resulting coordination pattern (e.g., tetrahedral, square planar) are critically important for both traditional and machine learning approaches that aim to exploit site or structure information for predicting materials properties. Here, we introduce new local structure order parameters (LoStOPs) that are specifically designed to rapidly detect rotationally symmetric (e.g., tetrahedron) as well as asymmetric local coordination environments (e.g., square pyramid). Furthermore, we introduce a Monte Carlo optimization approach to ensure that the different LoStOPs are comparable with each other. We then apply the new local environment descriptors to define site and structure fingerprints and to measure similarity between 61 known coordination environments and 40 commonly studied crystal structures, respectively. After extensive testing and optimization, we determine the most accurate structure similarity assessment procedure to compute all 2.45 billion structure similarities between each pair of the ≈ 70,000 materials that are currently present in the Materials Project database.

Keywords

coordination pattern
coordination number
crystal structure analyses
materials property
materials database

Supplementary materials

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new ops site structure fingerprints SI
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