Mapping strain at the atomic scale with PyNanospacing: An AI-assisted approach to TEM image processing and visualization

The diverse spectrum of material characteristics, including band gap, mechanical moduli, color, phonon and electronic density of states, along with catalytic and surface properties, are intricately intertwined with the atomic structure and the corresponding interatomic bond lengths. This interconnection extends to the manifestation of interplanar spacings within a crystalline lattice. Analysis of these interplanar spacings and the comprehension of any deviations-whether it be lattice compression or expansion, commonly referred to as strain, hold paramount significance in unraveling various unknowns within the field. Transmission Electron Microscopy (TEM) is widely used to capture these atomic-scale ordering, facilitating direct investigation of interplanar spacings. However, creating critical contour maps for visualizing and interpreting lattice stresses in TEM images remains a challenging task. This study introduces an open-source, AI-assisted application, developed entirely in Python, for processing TEM images to facilitate strain analysis through advanced visualization techniques. This application is designed to process a diverse range of materials, including nanoparticles, 2D materials, pure crystals, and solid solutions. By converting local variations in interplanar spacings into contour maps, it provides a visual representation of lattice expansion and compression. With highly versatile settings, as detailed in this paper, the tool is readily accessible for TEM image-based material analysis. It facilitates an in-depth exploration of strain engineering by generating strain contour maps at the atomic scale, offering valuable insights into material properties.