Nikhila C. Paranamana

arXiv2026

Fast 4D-STEM-based phase mapping for amorphous and mixed materials

Interpretation and mapping strategies for 4D-scanning transmission electron microscopy (4D-STEM) are well-developed for crystalline materials, yet in the case of amorphous and mixed materials it is significantly more challenging to separate different phases. Non-negative matrix factorization (NMF) in principle would allow separation of 4D-STEM data into components with interpretable diffraction signatures and intensity maps, independent of the crystalline, amorphous or mixed nature of the material. However, adoption of NMF in this field is hampered by large datasets and conceptual hurdles: NMF tackles a non-convex optimization problem, requiring iterative algorithms. Additionally, the stopping condition has to be chosen carefully. In this work, we show that the factorization of large 4D-STEM datasets can be drastically accelerated using a QB decomposition (i.e. randomized NMF or RNMF), leading to much shorter time per iteration. This allows structure-independent phase mapping on very large 4D-STEM datasets. We validate this approach on a synthetic literature dataset (mixed ZrCuAl), before mapping a thin TiO$_2$ layer on top of SiO$_2$, and an interface between a lithium-ion cathode and solid state electrolyte. We also demonstrate that, before using NMF to transform the data on an interpretable, nonnegative basis, principal component analysis (PCA) can be used for fast exploratory analysis to assess dataset dimensionality and linearity.

cond-mat.mtrl-sci↗

arXiv2026

Using superpixels for interpretable feature reduction in large 2D diffraction datasets

Large 2D diffraction datasets, consisting of hundreds or thousands of measurements, are commonly acquired with 4D-STEM electron diffraction or at synchrotron X-ray beamlines. Machine learning and artificial intelligence offer great promise for analyzing these datasets. However, the sheer volume of data presents a significant data processing bottleneck. Cropping the detector and pixel binning are standard ways to reduce data size. Here we propose grouping and averaging pixels into superpixels of variable area. High-information regions are sampled densely, while low-information areas are collected into larger superpixels. In the process, symmetries in the data are captured and exploited, making this approach suitable for preprocessing 2D diffraction data. We compare two variance-minimizing methods: K-means clustering (top-down) and agglomerative clustering (bottom-up) and demonstrate superior scaling and interpretability for the bottom-up method. As these methods are distance-based, we demonstrate that the construction of superpixels can be accelerated using Gaussian random projection. Finally we show over 100-fold acceleration for phase mapping with Non-negative matrix factorization on a 4D-STEM dataset when superpixels are used as a preprocessing step.

cond-mat.mtrl-sci↗