*Result*: 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. Program summary Program Title: PyNanoSpacing CPC Library link to program files: https://doi.org/10.17632/y864t5ykxx.1 Developer's repository link: https://github.com/malisarsil/PyNanoSpacing Licensing provisions: MIT license Programming language: Python 3.11 Nature of problem: Transmission Electron Microscopy (TEM) is widely used to analyze lattice structures in materials, but extracting quantitative strain information from TEM images remains challenging. Existing tools often lack automation, requiring manual calibration and region selection, leading to inconsistencies. Researchers need a user-friendly, automated solution to analyze local lattice strains and interplanar spacing variations efficiently. Solution method: The developed desktop application simplifies TEM image strain analysis by automating key steps. It extracts image details (such as scale and resolution) and detects atomic regions using AI-based segmentation. A correction step ensures proper alignment before measuring interlayer distances, which are then color-mapped to show strain variations. A smoothing technique is applied to reduce noise while keeping important details. The results can be exported to Excel, allowing further analysis. This user-friendly tool integrates AI and image processing to make strain mapping in TEM images faster and more accessible. [ABSTRACT FROM AUTHOR]*