Fig. 5: Visualizing neural-network representations of local crystalline and defective atomic structure in experimental images.

For each of the three experimental images in Fig. 4, we apply the fragmentation procedure of AI-STEM (cf. Fig. 1b), and extract the neural-network representations of these local windows (for the last fully connected layer before the classification, cf. Fig. 2b). The dimension-reduction (via Uniform Manifold Approximation and Projection, short UMAP) of these high-dimensional NN representation is shown in Fig. (a, b), where in (a) the color scale corresponds to the AI-STEM assignments, and in (b) the color scale corresponds to the mutual information that quantifies model uncertainty. All images are separated into three connected regions. In each of these, two connected clusters can be seen that correspond to the crystalline grains, while the connections indicate the grain boundary region. Notably, the boundary regions, which correspond to distinct interface types and are of critical importance for the material properties, do not intersect and are thus not confused by AI-STEM.