Automated Indexing Of TEM Diffraction Patterns Using Machine Learning

Indexing Transmission Electron Microscopy (TEM) diffraction patterns is a critical step in materials characterization. Despite the manually intensive indexing process, work related to Machine Learning (ML) in its space is sparse. We present an evaluation of current state-of-the-art classification models and a Convolutional Neural Network (CNN), found through a Neural Architecture Search (NAS), in the TEM diffraction domain. Both convolution and transformer-based architectures were considered. Our NAS model achieved the greatest top-1 accuracy of 77.03% and F1 score of 0.751. The convolution-based architectures performed better, with EfficientNet-B3 achieving the highest average accuracy of 71.82% and tying the NAS model with the largest average F1 score of 0.686. These results can be used to guide further research into the better classification and creation of TEM diffraction data.