Fig. 8: Error sensitivity of our unsupervised ML method on grain identification.
From: Machine learning enabled autonomous microstructural characterization in 3D samples
a–c Snapshots of the atomistic fcc Al sample with varying number of randomly perturbed local structure labels. d–f Grain size distribution plots of the system. Local variance filter with a 90-percentile thresholding is used for grain boundary identification, which alleviates the error sensitivity of the method to different voxelization bin sizes. Plots from left to right correspond to the amount of data variation in a–c, whereas from top to bottom, the voxelization bin size changes from 5.5 Å to 4.5 Å to 3.5 Å. As the bin size increases, the method become more resilient to variations in data due to more data averaging from down-sampling. This however comes at the expense of losing fine structures in the grain size distribution.
