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Statistically sound crystallographic symmetry classifications are obtained with information theory based methods in the presence of approximately Gaussian distributed noise. A set of three synthetic patterns with strong Fedorov type…

Materials Science · Physics 2022-05-03 Peter Moeck

We propose a novel data-driven approach for analyzing synchrotron Laue X-ray microdiffraction scans based on machine learning algorithms. The basic architecture and major components of the method are formulated mathematically. We…

Materials Science · Physics 2020-10-12 Yintao Song , Nobumichi Tamura , Chenbo Zhang , Mostafa Karami , Xian Chen

Owing to their tunability and versatility, the two-dimensional materials are an excellent platform to conduct a variety of experiments. However, laborious device fabrication procedures remain as a major experimental challenge. One…

Mesoscale and Nanoscale Physics · Physics 2023-05-12 Stephan Kim

Ground-truth RGBD data are fundamental for a wide range of computer vision applications; however, those labeled samples are difficult to collect and time-consuming to produce. A common solution to overcome this lack of data is to employ…

Computer Vision and Pattern Recognition · Computer Science 2024-05-28 L. Papa , P. Russo , I. Amerini

Crystalline defects, such as line-like dislocations, play an important role for the performance and reliability of many metallic devices. Their interaction and evolution still poses a multitude of open questions to materials science and…

Computer Vision and Pattern Recognition · Computer Science 2023-07-13 Kishan Govind , Daniela Oliveros , Antonin Dlouhy , Marc Legros , Stefan Sandfeld

Studying porous rock materials with X-Ray Computed Tomography (XRCT) has been established as a standard procedure for the non-destructive visualization of flow and transport in opaque porous media. Despite the recent advances in the field…

Machine Learning · Computer Science 2022-05-19 Dongwon Lee , Nikolaos Karadimitriou , Matthias Ruf , Holger Steeb

The challenge in fine-grained visual categorization lies in how to explore the subtle differences between different subclasses and achieve accurate discrimination. Previous research has relied on large-scale annotated data and pre-trained…

Computer Vision and Pattern Recognition · Computer Science 2024-05-16 Tianxu Wu , Shuo Ye , Shuhuang Chen , Qinmu Peng , Xinge You

Atomic scale simulations are a key element of modern science in that they allow to understand, and even predict, complex physical or chemical phenomena on the basis of the fundamental laws of nature. Among the different existing atomic…

Materials Science · Physics 2021-07-20 Alexandre Boulle , Alain Chartier , Aurélien Debelle , Xin Jin , Jean-Paul Crocombette

Intense short-wavelength pulses from free-electron lasers and high-harmonic-generation sources enable diffractive imaging of individual nano-sized objects with a single x-ray laser shot. The enormous data sets with up to several million…

Material properties strongly depend on the nature and concentration of defects. Characterizing these features may require nano- to atomic-scale resolution to establish structure-property relationships. 4D-STEM, a technique where diffraction…

Materials Science · Physics 2023-05-03 Stephanie M. Ribet , Colin Ophus , Roberto dos Reis , Vinayak P. Dravid

The use of coherent x-ray beams has been greatly developing for the past decades. They are now used by a wide scientific community to study biological materials, phase transitions in crystalline materials, soft matter, magnetism, strained…

Materials Science · Physics 2010-09-01 V. L. R. Jacques , S. Ravy , D. Le Bolloc'h , E. Pinsolle , M. Sauvage-Simkin , F. Livet

We demonstrate a strategy for simulating wide-range X-ray scattering patterns, which spans the small- and wide scattering angles as well as the scattering angles typically used for Pair Distribution Function (PDF) analysis. Such simulated…

We report an interpretation method for deep learning models that allows us to handle high-dimensional spectral data in materials science. The proposed method uses feature extraction and clustering analysis to categorize materials into…

Materials Science · Physics 2025-10-21 Akira Takahashi , Yu Kumagai , Arata Takamatsu , Fumiyasu Oba

X-ray diffraction is ideal for probing sub-surface state during complex or rapid thermomechanical loading of crystalline materials. However, challenges arise as the size of diffraction volumes increases due to spatial broadening and…

Materials Science · Physics 2025-01-10 Rachel E. Lim , Shun-Li Shang , Chihpin Chuang , Thien Q. Phan , Zi-Kui Liu , Darren C. Pagan

High-throughput analysis of multidimensional transmission electron microscopy (TEM) datasets remains a significant challenge, limiting the broader impact on strategic materials research. Conventional workflows typically involve sequential,…

Materials Science · Physics 2025-07-16 Arda Genc , Ravit Silverstein

Recently PANalytical introduced the XrdML file format as a new data platform for powder diffraction experiments. We will explain why an industrial standard (XML) was chosen and show the XML schema used to precisely describe the instrumental…

Computational Physics · Physics 2007-05-23 Dr. Thomas Degen

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

A growing number of shock compression experiments, especially those involving laser compression, are taking advantage of in situ x-ray diffraction as a tool to interrogate structure and microstructure evolution. Although these experiments…

Three-dimensional X-ray diffraction (3DXRD) is shown to be feasible at the I12 Joint Engineering, Environmental and Processing (JEEP) beamline of Diamond Light Source. As a demonstration, a microstructually simple low-carbon ferritic steel…

Microstructure reconstruction, a major component of inverse computational materials engineering, is currently advancing at an unprecedented rate. While various training-based and training-free approaches are developed, the majority of…

Materials Science · Physics 2022-11-28 Christian Düreth , Paul Seibert , Dennis Rücker , Stephanie Handford , Markus Kästner , Maik Gude
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