A Physically-informed Graph-based Order Parameter for the Universal Characterization of Atomic Structures

2021/06/15 by James Chapman, Nir Goldman, Brandon C. Wood +1
Engineering · Materials Science · Mathematics · Physics and Astronomy · #Characterization (materials science) #Combinatorics #Computer science #Graph #Machine Learning in Materials Science #Materials science #Mathematics #Nanotechnology #Surface Chemistry and Catalysis #Template #Theoretical computer science #Topology (electrical circuits) #X-ray Diffraction in Crystallography #cond-mat.mtrl-sci

paper · pdf · doi:10.1038/s41524-022-00717-7

arxiv created 2021/06/15 · openalex publication_date 2022/03/11 · arxiv updated 2022/03/22 · openalex created_date 2022/04/03 · openalex updated_date 2026/08/02

Abstract

A new graph-based order parameter is introduced for the characterization of atomistic structures. The order parameter is universal to any material/chemical system, and is transferable to all structural geometries. Three sets of data are used to validate both the generalizability and accuracy of the algorithm: (1) liquid lithium configurations spanning up to 300 GPa, (2) condensed phases of carbon along with nanotubes and buckyballs at ambient and high temperature, and (3) a diverse set of aluminum configurations including surfaces, compressed and expanded lattices, point defects, grain boundaries, liquids, nanoparticles, all at non-zero temperatures. The aluminum configurations are also compared to existing characterization methods for both speed and accuracy. Our order parameter uniquely classifies every configuration and outperforms all crystalline order parameters studied here, opening the door for its use in a multitude of complex application spaces that can require fine configurational characterization of materials.

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