SearcharxivSearch

arXiv subjects

Arthur R. C. McCray

Publications and source records attributed to Arthur R. C. McCray.

11 recordsLinked to original sources

Real-space overlap is not enough: ambiguity in nanobeam iterative ptychography

High-resolution iterative ptychography typically relies on a well-aligned, high-convergence-angle electron probe. Here we explore whether it can instead be performed at small convergence angles, relaxing the need for probe correctors and enabling experiments at low accelerating voltages or with a de-excited objective lens, as in Lorentz mode. Through experiments and simulations, we show that once the convergence angle is small enough that no diffracted disks overlap, the resulting reconstruction is ambiguous, posing a significant challenge for robust interpretation of results. This challenge arises because of the lack of interference between Bragg disks in the recorded diffraction pattern intensity, leading to no phase information for each reflection. Reconstructions with these data lead to degenerate objects in which rigid translations of the lattice and reversals of contrast of the object produce the same error between experimental data and the ptychography forward model. Increasing the real-space overlap between probe positions does not lift this degeneracy. An amorphous substrate can supply the missing phase relationships by giving the Bragg beams support in the gaps between disks. This phasing is fragile, however, and survives only where the forward model matches the experiment. At fixed dose, either constraining the object to be a pure phase object or introducing thermal motion into the forward model is enough on its own to make the solution non-unique, highlighting why our experimental reconstructions below the overlap threshold are ambiguous despite ample dose and real-space redundancy. Most troublingly, the lattice spacing and orientation are always recovered correctly, so a non-unique reconstruction looks convincing and can be diagnosed only by repeating the reconstruction from different starting points.

cond-mat.mtrl-sci

Mapping Order in Semicrystalline Polymers using Machine Learning of Nanobeam Electron Diffraction

Organic mixed ionic electronic conductors (OMIECs) are a promising class of polymer materials for applications spanning neuromorphic computation to energy efficient electronics and bioelectronics. Despite being highly tunable, the relationship between structural features and key performance properties such as charge carrier mobility is poorly understood. Scanning nanodiffraction in the transmission electron microscope (TEM) is a powerful probe for elucidating this structure-property relationship, but produces large, noisy datasets that are difficult to interpret because polymer reflections exhibit several distinct morphologies. To address the complexity, we trained a machine learning (ML) model to detect these polymer diffraction peaks and their intensities from synthetic data. Compared to correlative peak detection algorithms, the conventional method for analyzing nanobeam 4D scanning transmission electron microscopy (4DSTEM) data, we show that the ML model is significantly faster and outperforms correlative algorithms in almost all cases, opening up the possibility of near-live visualization of 4DSTEM experiments.

cond-mat.mtrl-sci

Unsupervised segmentation and clustering workflow for efficient processing of 4D-STEM and 5D-STEM data

Four-dimensional scanning transmission electron microscopy (4D-STEM) enables mapping of diffraction information with nanometer-scale spatial resolution, offering detailed insight into local structure, orientation, and strain. However, as data dimensionality and sampling density increase, particularly for in situ scanning diffraction experiments (5D-STEM), robust segmentation of structurally consistent behavior across sequential measurements becomes essential for efficient and physically meaningful analysis. Here, we introduce a clustering framework that identifies crystallographically distinct domains from 4D-STEM datasets. By using local diffraction-pattern similarity as a metric, the method extracts closed contours delineating spatially contiguous regions. This approach produces cluster-averaged diffraction patterns that improve signal quality while reducing data volume by orders of magnitude, enabling rapid and accurate orientation, phase, and strain mapping. We demonstrate the applicability of this approach to in situ liquid-cell 4D-STEM data of gold nanoparticle growth. Our method provides a scalable and generalizable route for spatially coherent segmentation, data compression, and quantitative structure-strain mapping across diverse 4D-STEM modalities. The full analysis code and example workflows are publicly available to support reproducibility and reuse.

cond-mat.mtrl-sci

Model-free Analysis of Scattering and Imaging Data with Escort-Weighted Shannon Entropy and Divergence Matrices

We demonstrate a model-free data analysis framework that leverages escort-weighted Shannon Entropy and several divergence matrices to detect phase transitions in scattering and imaging datasets. By establishing a connection between physical entropy and informational entropy, this approach provides a sensitive method for identifying phase transitions without an explicit physical model or order parameter. We further show that pairwise divergence matrices, including Kullback-Leibler divergence, Jeffrey Divergence, Jensen-Shannon Divergence and antisymmetric Kullback-Leibler divergence, provide more comprehensive measures of statistical changes than scalar entropy alone. Our approach successfully detects the onset of both long- and short-range order in neutron and X-ray scattering data, as well as a non-trivial phase transition in magnetic skyrmion lattices observed through Lorentz-transition electron microscopy. These results establish a framework for automated, model-free analysis of experimental data with broad applications in materials science and condensed matter physics.

cond-mat.mtrl-sci

A Gaussian Parameterization for Direct Atomic Structure Identification in Electron Tomography

Atomic electron tomography (AET) enables the determination of 3D atomic structures by acquiring a sequence of 2D tomographic projection measurements of a particle and then computationally solving for its underlying 3D representation. Classical tomography algorithms solve for an intermediate volumetric representation that is post-processed into the atomic structure of interest. In this paper, we reformulate the tomographic inverse problem to solve directly for the locations and properties of individual atoms. We parameterize an atomic structure as a collection of Gaussians, whose positions and properties are learnable. This representation imparts a strong physical prior on the learned structure, which we show yields improved robustness to real-world imaging artifacts. Simulated experiments and a proof-of-concept result on experimentally-acquired data confirm our method's potential for practical applications in materials characterization and analysis with Transmission Electron Microscopy (TEM). Our code is available at https://github.com/nalinimsingh/gaussian-atoms.

eess.IV

Missing Wedge Inpainting and Joint Alignment in Electron Tomography through Implicit Neural Representations

Electron tomography is a powerful tool for understanding the morphology of materials in three dimensions, but conventional reconstruction algorithms typically suffer from missing-wedge artifacts and data misalignment imposed by experimental constraints. Recently proposed supervised machine-learning-enabled reconstruction methods to address these challenges rely on training data and are therefore difficult to generalize across materials systems. We propose a fully self-supervised implicit neural representation (INR) approach using a neural network as a regularizer. Our approach enables fast inline alignment through pose optimization, missing wedge inpainting, and denoising of low dose datasets via model regularization using only a single dataset. We apply our method to simulated and experimental data and show that it produces high-quality tomograms from diverse and information limited datasets. Our results show that INR-based self-supervised reconstructions offer high fidelity reconstructions with minimal user input and preprocessing, and can be readily applied to a wide variety of materials samples and experimental parameters.

eess.IV

Deep generative priors for robust and efficient electron ptychography

Electron ptychography enables dose-efficient atomic-resolution imaging, but conventional reconstruction algorithms suffer from noise sensitivity, slow convergence, and extensive manual hyperparameter tuning for regularization, especially in three-dimensional multislice reconstructions. We introduce a deep generative prior (DGP) framework for electron ptychography that uses the implicit regularization of convolutional neural networks to address these challenges. Two DGPs parameterize the complex-valued sample and probe within an automatic-differentiation mixed-state multislice forward model. Compared to pixel-based reconstructions, DGPs offer four key advantages: (i) greater noise robustness and improved information limits at low dose; (ii) markedly faster convergence, especially at low spatial frequencies; (iii) improved depth regularization; and (iv) minimal user-specified regularization. The DGP framework promotes spatial coherence and suppresses high-frequency noise without extensive tuning, and a pre-training strategy stabilizes reconstructions. Our results establish DGP-enabled ptychography as a robust approach that reduces expertise barriers and computational cost, delivering robust, high-resolution imaging across diverse materials and biological systems.

eess.IV

Understanding the effect of curvature on the magnetization reversal of three-dimensional nanohelices

Comprehending the interaction between geometry and magnetism in three-dimensional (3D) nanostructures is of importance to understand the fundamental physics of domain wall (DW) formation and pinning. Here, we use focused electron beam-induced deposition to fabricate magnetic nanohelices with increasing helical curvature with height. Using electron tomography and Lorentz transmission electron microscopy, we reconstruct the 3D structure and magnetization of the nanohelices. The surface curvature, helical curvature and torsion of the nanohelices are then quantified from the tomographic reconstructions. Furthermore, by using the experimental 3D reconstructions as inputs for micromagnetic simulations we can reveal the influence of surface and helical curvature on the magnetic reversal mechanism. Hence, we can directly correlate the magnetic behavior of a 3D nanohelix to its experimental structure. These results demonstrate how control of geometry in nanohelices can be utilized in the stabilization of DWs and control of the response of the nanostructure to applied magnetic fields.

cond-mat.mtrl-sci

AI-enabled Lorentz microscopy for quantitative imaging of nanoscale magnetic spin textures

The manipulation and control of nanoscale magnetic spin textures is of rising interest as they are potential foundational units in next-generation computing paradigms. Achieving this requires a quantitative understanding of the spin texture behavior under external stimuli using in situ experiments. Lorentz transmission electron microscopy (LTEM) enables real-space imaging of spin textures at the nanoscale, but quantitative characterization of in situ data is extremely challenging. Here, we present an AI-enabled phase-retrieval method based on integrating a generative deep image prior with an image formation forward model for LTEM. Our approach uses a single out-of-focus image for phase retrieval and achieves significantly higher accuracy and robustness to noise compared to existing methods. Furthermore, our method is capable of isolating sample heterogeneities from magnetic contrast, as shown by application to simulated and experimental data. This approach allows quantitative phase reconstruction of in situ data and can also enable near real-time quantitative magnetic imaging.

cond-mat.mtrl-sci

Thermal Hysteresis Behavior of Skyrmion Lattices in the van der Waals Ferromagnet Fe3GeTe2

Understanding the physics of phase transitions in two-dimensional (2D) systems underpins the research in diverse fields including statistical mechanics, quantum systems, nanomagnetism, and soft condensed matter. However, many fundamental aspects of 2D phase transitions are still not well understood, including the effects of interparticle potential, polydispersity, and particle shape. Magnetic skyrmions, which are non-trivial chiral spin structures, can be considered as quasi-particles that form two-dimensional lattices. Here we show, by real-space imaging using in situ cryo-Lorentz transmission electron microscopy coupled with machine learning, the ordering behavior of Néel skyrmion lattices in van der Waals Fe3GeTe2. We demonstrate a distinct change in the skyrmion size distribution during field-cooling, which leads to a loss of lattice order and an evolution of the skyrmion liquid phase. Remarkably, the lattice order is restored during field heating and demonstrates a thermal hysteresis. Our quantitative analysis explains this behavior based on the energy landscape of skyrmions and demonstrates the potential to control the lattice order in 2D phase transitions.

cond-mat.mtrl-sci

Field-Dependent Magnetic Domain Behavior in van der Waals Fe$_3$GeTe$_2$

Two-dimensional magnetic van der Waals (vdW) materials can show a variety of topological nontrivial spin textures, such as Bloch- or Néel-type stripe, skyrmion or bubble domains under certain external stimuli. It is critical to understand the magnetic domain behavior in vdW materials in order to control their size, and density in response to external stimuli such as electric and magnetic fields. Here we examine the magnetic field dependence of topologically non-trivial magnetization spin textures in vdW Fe$_3$GeTe$_2$. Néel-type stripe domains and skyrmions are formed depending on the magnetic field-cooling protocol used during in-situ Lorentz transmission electron microscopy (LTEM) experiments. Use of quantitative reconstruction of magnetic induction maps, and micromagnetic simulations, allow for understanding the LTEM results of Néel-type stripe domains as well as skyrmions. In addition, the deformation of skyrmion contrast is observed as a result of the introduction of an in-plane magnetic field. We demonstrate the stability of the stripe domains and skyrmions in response to externally applied magnetic field due to energy barrier for domain wall annihilation. Our results establish an understanding of the energy landscape that governs the behavior of the topologically non-trivial spin textures in vdW materials which can be harnessed for spintronic applications.

cond-mat.mtrl-sci