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

J Chapman and TM Hsu and X Chen and TW Heo and BC Wood, NATURE COMMUNICATIONS, 14 (2023).

DOI: 10.1038/s41467-023-39755-0

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. Level of atomic disorder in materials is critical to understanding the effect of local structure on materials properties. Here the authors present a workflow combining structure-aware graph neural networks and physics-inspired order parameter to characterize structural disorder on a per atom basis.

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