  Understanding lattice deformations is crucial in determining the properties
of nanomaterials, which can become more prominent in future applications
ranging from energy harvesting to electronic devices. However, it remains
challenging to reveal unexpected deformations that crucially affect material
properties across a large sample area. Here, we demonstrate a rapid and
semi-automated unsupervised machine learning approach to uncover lattice
deformations in materials. Our method utilizes divisive hierarchical clustering
to automatically unveil multi-scale deformations in the entire sample flake
from the diffraction data using four-dimensional scanning transmission electron
microscopy (4D-STEM). Our approach overcomes the current barriers of large 4D
data analysis and enables extraction of essential features even without a
priori knowledge of the sample. Using this purely data-driven analysis, we have
uncovered different types of material deformations, such as strain, lattice
distortion, bending contour, etc., which can significantly impact the band
structure and subsequent performance of nanomaterials-based devices. We
envision that this data-driven procedure will provide insight into the
intrinsic structures and accelerate the discovery of novel materials.
