Machine learning-based classification of dislocation microstructures

Abstract

Dislocations—the carrier of plastic deformation—are responsible for a wide range of mechanical properties of metals or semiconductors. Those line-like objects tend to form complex networks that are very difficult to characterize or to link to macroscopic properties on the specimen scale. In this work a machine learning based approach for classification of coarse-grained dislocation microstructures in terms of different dislocation density field variables is used. The performance of the model combined with domain knowledge from the underlying physics helps to shed light on the interplay between coarse-graining voxel size and the set of suitable or even required density variables for a faithful microstructure characterization.