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Unsupervised Machine Learning to Distill Structural-Property Insights from 4D-STEM

With recent advances in data acquisition techniques, scanning transmission electron microscopy (STEM), is now able to record the entire scattering distribution, generating a four-dimensional (4D) dataset that includes two real-space dimensions and two reciprocal-space dimensions for every probe position [1,2]. Other studies have shown that 4D-STEM enables probing electromagnetic fields by differential phase contrast (DPC) on segmented detectors [3,4] or by a simplified quantum theoretical interpretation of the pixelated detector [5].