Quantifying disorder one atom at a time using an interpretable graph neural network paradigm

James Chapman (), Tim Hsu (), Xiao Chen, Tae Wook Heo and Brandon C. Wood ()
Additional contact information
James Chapman: Boston University
Tim Hsu: Lawrence Livermore National Laboratory
Xiao Chen: Lawrence Livermore National Laboratory
Tae Wook Heo: Lawrence Livermore National Laboratory
Brandon C. Wood: Lawrence Livermore National Laboratory

Nature Communications, 2023, vol. 14, issue 1, 1-12

Abstract: Abstract Quantifying the level of atomic disorder within materials is critical to understanding how evolving local structural environments dictate performance and durability. Here, we leverage graph neural networks to define a physically interpretable metric for local disorder, called SODAS. This metric encodes the diversity of the local atomic configurations as a continuous spectrum between the solid and liquid phases, quantified against a distribution of thermal perturbations. We apply this methodology to four prototypical examples with varying levels of disorder: (1) grain boundaries, (2) solid-liquid interfaces, (3) polycrystalline microstructures, and (4) tensile failure/fracture. We also compare SODAS to several commonly used methods. Using elemental aluminum as a case study, we show how our paradigm can track the spatio-temporal evolution of interfaces, incorporating a mathematically defined description of the spatial boundary between order and disorder. We further show how to extract physics-preserved gradients from our continuous disorder fields, which may be used to understand and predict materials performance and failure. Overall, our framework provides a simple and generalizable pathway to quantify the relationship between complex local atomic structure and coarse-grained materials phenomena.

Date: 2023
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DOI: 10.1038/s41467-023-39755-0

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