Quantitative approaches for multiscale structural analysis with atomic resolution electron microscopy

Atomic-resolution scanning transmission electron microscopy (STEM) provides unparalleled insight into nanoscale structure--property relationships in functional materials. Robust, interpretable image processing is essential for turning complex datasets into quantitative measurements. In this work, the authors present algorithms combining real- and reciprocal-space information to extract subtle atomic displacements associated with symmetry breaking distortions and quantify nanoscale strain variations. Rooted in crystallography, these approaches emphasize physical interpretability as well as associated measurement uncertainties and regimes of validity. These algorithms are collected into an open-source Python package available to the community.