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Convergent beam electron diffraction (CBED) performed on two-dimensional (2D) materials recently emerged as a powerful tool to study structural and stacking defects, adsorbates, atomic 3D displacements in the layers, and the interlayer…

Mesoscale and Nanoscale Physics · Physics 2022-01-25 Tatiana Latychevskaia , Sarah Haigh , Kostya Novoselov

Scanning transmission electron microscopy (STEM) has been extensively used for imaging complex materials down to atomic resolution. The most commonly employed STEM modality, annular dark-field imaging, produces easily-interpretable…

Microscopy is one of the most essential imaging techniques in life sciences. High-quality images are required in order to solve (potentially life-saving) biomedical research problems. Many microscopy techniques do not achieve sufficient…

Computer Vision and Pattern Recognition · Computer Science 2018-10-24 Joris Roels , Jan Aelterman , Jonas De Vylder , Hiep Luong , Yvan Saeys , Wilfried Philips

Analyzing large X-ray diffraction (XRD) datasets is a key step in high-throughput mapping of the compositional phase diagrams of combinatorial materials libraries. Optimizing and automating this task can help accelerate the process of…

MeV ultrafast electron diffraction (MUED) is a pump-probe technique used to study the dynamic structural evolution of materials. An ultrashort laser pulse triggers structural changes, which are then probed by an ultrashort relativistic…

Three-dimensional phase contrast imaging of multiply-scattering samples in X-ray and electron microscopy is extremely challenging, due to small numerical apertures, the unavailability of wavefront shaping optics, and the highly nonlinear…

Computational Physics · Physics 2021-06-02 Philipp M Pelz , Hamish G Brown , Jim Ciston , Scott D Findlay , Yaqian Zhang , Mary Scott , Colin Ophus

We propose a novel Graph Neural Network-based method for segmentation based on data fusion of multimodal Scanning Electron Microscope (SEM) images. In most cases, Backscattered Electron (BSE) images obtained using SEM do not contain…

Computer Vision and Pattern Recognition · Computer Science 2025-06-24 Samuel Repka , Bořek Reich , Fedor Zolotarev , Tuomas Eerola , Pavel Zemčík

Four-dimensional scanning transmission electron microscopy (4D-STEM) provides rich, atomic-scale insights into materials structures. However, extracting specific physical properties - such as polarization directions essential for…

Scanning transmission electron microscopy (STEM) has advanced rapidly in the last decade thanks to the ability to correct the major aberrations of the probe forming lens. Now atomic-sized beams are routine, even at accelerating voltages as…

Microstructural analysis with electron backscatter diffraction (EBSD) involves sectioning and polishing to create a flat and preparation-artifact free surface. The quality of EBSD analysis is often dependant on this step, and this motivates…

Materials Science · Physics 2022-03-31 Ning Fang , Ruth Birch , T. Ben Britton

We report a multiscale approach of broad applicability to stochastic reconstruction of multiphase materials, including porous ones. The approach devised uses an optimization method, such as the simulated annealing (SA) and the so-called…

Materials Science · Physics 2018-11-13 R. Piasecki , W. Olchawa , D. Frączek , R. Wiśniowski

The performance of electron energy-loss spectrometers can often be limited by their electron-optical aberrations. Due to recent developments in high energy-resolution and momentum-resolved electron energy loss spectroscopy (EELS), there is…

Materials Science · Physics 2023-02-28 Yueming Guo , Andrew R. Lupini

We introduce a denoising method for four-dimensional scanning transmission electron microscopy (4D-STEM) that relies on processing local, scan position-independent electron event-sparse data stacks, called event-sparse stack denoising. This…

Medical Physics · Physics 2025-12-12 Gregory Nordahl , Rebekka Klemmt , Espen Drath Bøjesen

Machine learning is attracting surging interest across nearly all scientific areas by enabling the analysis of large datasets and the extraction of scientific information from incomplete data. Data-driven science is rapidly growing,…

Applied Physics · Physics 2025-03-17 Sung Yun Lee , Do Hyung Cho , Chulho Jung , Daeho Sung , Daewoong Nam , Sangsoo Kim , Changyong Song

We establish a series of deep convolutional neural networks to automatically analyze position averaged convergent beam electron diffraction patterns. The networks first calibrate the zero-order disk size, center position, and rotation…

Data Analysis, Statistics and Probability · Physics 2018-06-05 Weizong Xu , James M. LeBeau

Electron microscopy (EM) is a foundational tool for directly assessing the structure of materials. Recent advances in direct electron detectors have improved signal-to noise ratios via single-electron counting. However, accurately counting…

The recently developed information-theoretic approach to crystallographic symmetry classifications and quantifications in two dimensions (2D) from digital transmission electron and scanning probe microscope images is adapted for the…

Materials Science · Physics 2022-08-10 Peter Moeck , Lukas von Koch

Density functional theory (DFT) has become a standard tool for the study of point defects in materials. However, finding the most stable defective structures remains a very challenging task as it involves the solution of a multimodal…

Materials Science · Physics 2020-11-16 Marco Arrigoni , Georg K. H. Madsen

The structure of low-carbon steel after twist extrusion is tested with using electron backscattered diffraction. It has been shown that warm twist extrusion results in grain refinement with conservation of a substantial part of high-angle…

Bearing fault diagnosis under varying working conditions faces challenges, including a lack of labeled data, distribution discrepancies, and resource constraints. To address these issues, we propose a progressive knowledge distillation…

Machine Learning · Computer Science 2025-11-04 Mohammadreza Kavianpour , Parisa Kavianpour , Amin Ramezani , Mohammad TH Beheshti
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