Enhancing nanoscale SEM image segmentation and reconstruction with crystallographic orientation data and machine learning

Current methods of image segmentation and reconstructions from scanning electron micrographs can be inadequate for resolving nanoscale gaps in composite materials (1-20 nm). Such information is critical to both accurate material characterizations and models of piezoresistive response. The current work proposes the use of crystallographic orientation data and machine learning for enhancing this process. It is first shown how a machine learning algorithm can be used to predict the connectivity of nanoscale grains in a Nickel nanostrand/epoxy composite. This results in 71.9% accuracy for a 2D algorithm and 62.4% accuracy in 3D. Finally, it is demonstrated how these algorithms can be used to predict the location of gaps between distinct nanostrands - gaps which would otherwise not be detected with the sole use of a scanning electron microscope.