A graph database for feature characterization of dislocation networks

Abstract

Three-dimensional dislocation networks control the mechanical properties such as strain hardening of crystals. Due to the complexity of dislocation networks and their temporal evolution, analysis tools are needed that fully resolve the dynamic processes of the intrinsic dislocation graph structure. We propose the use of a graph database for the analysis of three-dimensional dislocation networks obtained from discrete dislocation dynamics simulations. This makes it possible to extract (sub-)graphs and their features with relative ease. That allows for a more holistic view of the evolution of dislocation networks and for the extraction of homogenized graph features to be incorporated into continuum formulation. As an illustration, we describe the static and dynamic analysis of spatio-temporal dislocation graphs as well as graph feature analysis.

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• Graph Neural Network for Traffic Forecasting: A Survey
• Dislocation Networks and the Microstructural Origin of Strain Hardening
• Topological derivation of shape exponents for stretched exponential relaxation
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• Characterization of Lomer junctions based on the Lomer arm length distribution in dislocation networks

Cited by in corpus (1)

• Towards an automated workflow in materials science for combining multi-modal simulative and experimental information using data mining and large language models