Graph-component approach to defect identification in large atomistic simulations

Bilbrey, Jenna A.; Chen, Nanjun; Hu, Shenyang; Sushko, Peter V.

doi:10.1016/j.commatsci.2022.111700

Title: Graph-component approach to defect identification in large atomistic simulations

Journal Article · 25 August 2022 · Computational Materials Science

DOI: https://doi.org/10.1016/j.commatsci.2022.111700 · OSTI ID:1886603

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Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

In this work, the graph-theoretical concept of connected components is employed to extract the evolution of defect configurations in a polycrystalline aluminum structure containing ~8.3 million atoms. This graph-component approach is applied to reveal details of defect formation, transport, and transformation in the polycrystalline Al under large shear deformation. Building upon standard nearest neighbor analysis, graph theory and associated tools are used to reduce the multi-million-atom system into discrete component subgraphs that represent distinct structural defects. This method allows the automated identification, characterization, and tracking of defective regions within large volumes of data representing atomic-scale processes. Such analysis elucidates relationships between external stimuli, such as strain, and defect distributions, which have a large influence on material properties. The Graph Analytics for Large Atomistic Simulations (GALAS) codebase that implements this analysis, together with user guidance, is openly available at https://github.com/pnnl/galas.

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Research Organization:: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

Sponsoring Organization:: USDOE; USDOE Laboratory Directed Research and Development (LDRD) Program

Grant/Contract Number:: AC05-76RL01830

OSTI ID:: 1886603

Report Number(s):: PNNL-SA-173243

Journal Information:: Computational Materials Science, Journal Name: Computational Materials Science Vol. 214; ISSN 0927-0256

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Title: Graph-component approach to defect identification in large atomistic simulations

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