Fig. 3: Application of AI-STEM to synthetic, polycrystalline data. | npj Computational Materials

Fig. 3: Application of AI-STEM to synthetic, polycrystalline data.

From: Automatic identification of crystal structures and interfaces via artificial-intelligence-based electron microscopy

Fig. 3: Application of AI-STEM to synthetic, polycrystalline data.The alternative text for this image may have been generated using AI.

a The simulated image has 4 crystalline regions with different structural order, including three crystalline (Cu fcc [100], Fe bcc [100], Ti hcp [0001]) and one amorphous grain. Each grain is rectangular with an edge length of 40 Å. The sliding window is 1.2 × 1.2 nm (100 pixels) and is visualized in the top left corner. b The Bayesian CNN employed in the AI-STEM workflow (cf. Fig. 1) provides a distribution and not only point estimates in the final output layer. The averaged classification probabilities can be used to identify the most likely class (c). An uncertainty estimate (a scalar value, cf. (b) and Eq. (6)) can be obtained via the mutual information (d), revealing the grain boundaries as well as the amorphous region. The scale bar is 1 nm in a.

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