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Deconstructing Diffraction: A set of computational tools to analyze and interpret Four-Dimensional Scanning Transmission Electron Microscopy datasets

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Abstract

Electron Microscopy (EM) has become a tool of choice to quantify materials structure at high resolution. Four-Dimensional Scanning Transmission Electron Microscopy (4D-STEM) is a versatile diffraction EM technique that can provide a high spatial resolution map of material structures and properties. However, 4D-STEM datasets can contain hundreds of thousands of patterns and can thus be challenging to manually interpret. Robust computational tools are required to gain insights from 4D-STEM datasets.

In this work, we present a set of computational methods for interpreting 4D-STEM datasets. There are two broad categories these methods fall into. The first category, unsupervised learning, allows for similarities across a dataset to be mapped with no prior knowledge regarding the structures in the dataset. To perform unsupervised learning on 4D-STEM datasets, we implement feature engineering protocols that distill the information in each individual diffraction pattern down. We then analyze the performance of both Non-negative Matrix Factorization (NNMF) and consensus clustering on a set of simulated datasets to highlight the versatility of different feature extraction techniques.

The second category of computational methods presented in this work is based on template-matching procedures. If the crystal structure(s) in a material are known, templates can be used to quantify the structures present in each diffraction pattern. Here, we present preliminary work towards mapping the spatial distribution of phases in multi-phase materials using Non-negative Least Squares (NNLS). Together, unsupervised and template-matching approaches allow researchers using 4D-STEM to extract information from their datasets more readily.

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This item is under embargo until September 27, 2025.