STEM Diffraction Pattern Analysis with Deep Learning Networks
Journal:
arXiv
Published Date:
Jul 2, 2025
Abstract
Accurate grain orientation mapping is essential for understanding and
optimizing the performance of polycrystalline materials, particularly in
energy-related applications. Lithium nickel oxide (LiNiO$_{2}$) is a promising
cathode material for next-generation lithium-ion batteries, and its
electrochemical behaviour is closely linked to microstructural features such as
grain size and crystallographic orientations. Traditional orientation mapping
methods--such as manual indexing, template matching (TM), or Hough
transform-based techniques--are often slow and noise-sensitive when handling
complex or overlapping patterns, creating a bottleneck in large-scale
microstructural analysis. This work presents a machine learning-based approach
for predicting Euler angles directly from scanning transmission electron
microscopy (STEM) diffraction patterns (DPs). This enables the automated
generation of high-resolution crystal orientation maps, facilitating the
analysis of internal microstructures at the nanoscale. Three deep learning
architectures--convolutional neural networks (CNNs), Dense Convolutional
Networks (DenseNets), and Shifted Windows (Swin) Transformers--are evaluated,
using an experimentally acquired dataset labelled via a commercial TM
algorithm. While the CNN model serves as a baseline, both DenseNets and Swin
Transformers demonstrate superior performance, with the Swin Transformer
achieving the highest evaluation scores and the most consistent microstructural
predictions. The resulting crystal maps exhibit clear grain boundary
delineation and coherent intra-grain orientation distributions, underscoring
the potential of attention-based architectures for analyzing diffraction-based
image data. These findings highlight the promise of combining advanced machine
learning models with STEM data for robust, high-throughput microstructural
characterization.
