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Pink-beam Dark-Field X-ray Microscopy (pDFXM) is a powerful emerging technique for time-resolved studies of microstructure and strain evolution in bulk crystalline materials. In this work, we systematically assess the performance of pDFXM…

Generative models trained on public databases of protein structures, most of which have been determined by X-ray crystallography, now provide powerful priors for structure prediction. However, they are not readily conditioned on the…

Machine Learning · Computer Science 2026-05-18 Minseo Kim , Huanghao Mai , Jay Shenoy , Alec Follmer , Gordon Wetzstein , Frederic Poitevin

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

Coherent diffraction imaging is a high-resolution imaging technique whose potential can be greatly enhanced by applying the extrapolation method presented here. We demonstrate enhancement in resolution of a non-periodical object…

The use of strongly bent crystals in spectrometers for pulses of a hard x-ray free-electron laser is explored theoretically. Diffraction is calculated in both dynamical and kinematical theories. It is shown that diffraction can be treated…

Instrumentation and Detectors · Physics 2019-05-28 Vladimir Kaganer , Ilia Petrov , Liubov Samoylova

We investigate five different models to reconstruct the 3D $\gamma$-ray hit coordinates in five large \lacls monolithic crystals optically coupled to pixelated silicon photomultipliers. These scintillators have a base surface of 50 $\times$…

Pinwheel patterns and their higher dimensional generalisations display continuous circular or spherical symmetries in spite of being perfectly ordered. The same symmetries show up in the corresponding diffraction images. Interestingly, they…

Mathematical Physics · Physics 2008-01-19 Michael Baake , Dirk Frettlöh , Uwe Grimm

Deepfake techniques generate highly realistic data, making it challenging for humans to discern between actual and artificially generated images. Recent advancements in deep learning-based deepfake detection methods, particularly with…

Computer Vision and Pattern Recognition · Computer Science 2024-07-02 Alvaro Lopez Pellcier , Yi Li , Plamen Angelov

We present here an overview of Coherent X-ray Diffraction Imaging (CXDI) with its application to nanostructures. This imaging approach has become especially important recently due to advent of X-ray Free-Electron Lasers (XFEL) and its…

Mesoscale and Nanoscale Physics · Physics 2013-04-22 Ivan A. Vartanyants , Oleksandr M. Yefanov

The design of novel materials hinges on the understanding of structure-property relationships. However, in recent times, our capability to synthesize a large number of materials has outpaced our speed at characterizing them. While the…

The association of scanning transmission electron microscopy (STEM) and the detection of a diffraction pattern at each probe position (so-called 4D-STEM) represents one of the most promising approaches to analyze structural properties of…

Applied Physics · Physics 2023-01-26 Leonardo Corrêa , Eduardo Ortega , Arturo Ponce , Mônica Cotta , Daniel Ugarte

X-ray resonant magnetic reflectivity (XRMR) is a powerful method to determine the optical, structural and magnetic depth profiles of a variety of thin films. Here, we investigate samples of different complexity all measured at the Pt L$_3$…

Dynamical diffraction effects always play a role when working with perfect single crystals. The penetration of X-rays respect to the surface normal during diffraction (extinction depth, $1/\sigma_e$) in perfect single crystals does not have…

Instrumentation and Detectors · Physics 2021-01-20 Marcelo G. Honnicke , Sergio L. Morelhao

The diffraction patterns of crystalline materials with local order contain sharp Bragg reflections as well as highly structured diffuse scattering. The instrumental requirements, experimental parameters and data processing techniques for…

Materials Science · Physics 2023-11-29 Romy Poppe , Joke Hadermann

Crystal property prediction, governed by quantum mechanical principles, is computationally prohibitive to solve exactly for large many-body systems using traditional density functional theory. While machine learning models have emerged as…

Materials Science · Physics 2026-01-28 Bin Cao , Yang Liu , Longhan Zhang , Yifan Wu , Zhixun Li , Yuyu Luo , Hong Cheng , Yang Ren , Tong-Yi Zhang

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 2022-02-15 Peter Moeck , Lukas von Koch

The xPDFsuite software program is described. It is for processing and analyzing atomic pair distribution functions (PDF) from X-ray powder diffraction data. It provides a convenient GUI for SrXplanr and PDFgetX3, allowing the users to…

Materials Science · Physics 2015-02-25 Xiaohao Yang , Pavol Juhas , Christopher L. Farrow , Simon J. L. Billinge

Discovering functional crystalline materials through computational methods remains a formidable challenge in materials science. Here, we introduce VQCrystal, an innovative deep learning framework that leverages discrete latent…

Materials Science · Physics 2024-09-11 ZiJie Qiu , Luozhijie Jin , Zijian Du , Hongyu Chen , Yan Cen , Siqi Sun , Yongfeng Mei , Hao Zhang

Powder X-ray diffraction (PXRD) and neutron powder diffraction (NPD) have been used to investigate the crystal structure of CoFe2O4 nanoparticles prepared via different hydrothermal synthesis routes, with particular attention given to…

A method for estimating the relative content of crystalline phases of a multiphase sample, based on probabilistic analysis of the intensities of the diffraction pattern reflexes, has been developed. The method is based on the introduction…

Materials Science · Physics 2023-11-21 S. V. Gabielkov , I. V. Zhyganiuk , A. D. Skorbun
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