Fig. 2: Application of our ML method on several representative polycrystalline metal samples. | npj Computational Materials

Fig. 2: Application of our ML method on several representative polycrystalline metal samples.

From: Machine learning enabled autonomous microstructural characterization in 3D samples

Fig. 2

Each of the samples (aluminum, iron, silicon, and titanium) is ~20 nm × 20 nm × 20 nm in size (~500,000 atoms). All samples have 300 grains. The plots show the target (in red) and predicted (in blue) grain size distributions. The distributions are normalized such that the shared area equates to the total number of grains. Polycrystallinity of these samples are visualized by snapshots shown next to the plots, where individual grains are colored by their sizes (smallest in red, largest in blue). The sample set consists of common polycrystal types: a fcc, b bcc, c diamond, d hcp.

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