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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 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 materials’ intrinsic structures and accelerate the discovery of materials.

Single‐crystalline inorganic semiconductor nanomembranes (NMs) have attracted great attention over the last decade, which poses great advantages to complex device integration. Applications in heterogeneous electronics and flexible electronics have been demonstrated with various semiconductor nanomembranes. Single‐crystalline aluminum nitride (AlN), as an ultrawide‐bandgap semiconductor with great potential in applications such as high‐power electronics has not been demonstrated in its NM forms. This very first report demonstrates the creation, transfer‐printing, and characteristics of the high‐quality single‐crystalline AlN NMs. This work successfully transfers the AlN NMs onto various foreign substrates. The crystalline quality of the NMs has been characterized by a broad range of techniques before and after the transfer‐printing and no degradation in crystal quality has been observed. Interestingly, a partial relaxation of the tensile stress has been observed when comparing the original as‐grown AlN epi and the transferred AlN NMs. In addition, the transferred AlN NMs exhibits the presence of piezoelectricity at the nanoscale, as confirmed by piezoelectric force microscopy. This work also comments on the advantages and the challenges of the approach. Potentially, the novel approach opens a viable path for the development of the AlN‐based heterogeneous integration and future novel electronics and optoelectronics.