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X-ray Crystallography

X-ray Crystallography

The size of the unit cell and the arrangement of atoms in a crystal may be determined from measurements of the diffraction of X-rays by the crystal, termed X-ray crystallography.
Diffraction
Diffraction is the change in the direction of travel experienced by an electromagnetic wave when it encounters a physical barrier whose dimensions are comparable to those of the wavelength of the light. X-rays are electromagnetic radiation with wavelengths about as long as the distance between neighboring...

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$X-ray Diffraction of Biological Samples$ 01:10

X-ray Diffraction of Biological Samples

X-ray diffraction or XRD is an analytical tool that utilizes X-rays to study ordered structures such as crystalline organic and inorganic samples, polycrystalline materials, proteins, carbohydrates, and drugs.
According to Bragg's law, when X-rays strike the sample positioned on a stage, the rays are scattered by the electron clouds around the sample atoms. The X-ray diffraction or scattering is caused by constructive interference of the X-ray waves that reflect off the internal...

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Related Experiment Video

Updated: Oct 6, 2025

$Measurements of Long-range Electronic Correlations During Femtosecond Diffraction Experiments Performed on Nanocrystals of Buckminsterfullerene$

Measurements of Long-range Electronic Correlations During Femtosecond Diffraction Experiments Performed on Nanocrystals of Buckminsterfullerene

Published on: August 22, 2017

BraggNN: fast X-ray Bragg peak analysis using deep learning.

Zhengchun Liu¹, Hemant Sharma², Jun-Sang Park²

¹Data Science and Learning Division, Argonne National Laboratory, Lemont, IL 60439, USA.

|January 21, 2022

Summary

This summary is machine-generated.

We developed BraggNN, a deep-learning method for rapid X-ray diffraction peak analysis. This AI approach significantly speeds up high-energy diffraction microscopy (HEDM) by quickly determining peak positions, enabling faster experimental feedback.

Keywords:

WAXS X-ray microscopy high-pressure powder diffraction materials science structure prediction

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Last Updated: Oct 6, 2025

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Area of Science:

Materials Science
Crystallography
Computational Science

Background:

X-ray diffraction microscopy requires precise peak position determination for accurate analysis.
Conventional methods like pseudo-Voigt fitting are computationally intensive, hindering real-time experimental feedback.
Advancements in detector technology and experimental complexity increase the need for faster analysis.

Purpose of the Study:

To introduce BraggNN, a novel deep-learning method for rapid and accurate determination of X-ray diffraction peak positions.
To demonstrate the performance and accuracy of BraggNN compared to traditional pseudo-Voigt fitting.
To highlight the potential of BraggNN for real-time analysis in advanced microscopy techniques.

Main Methods:

Development of a deep-learning model, BraggNN, for identifying X-ray diffraction peak positions.
Validation of BraggNN against simulated datasets using conventional pseudo-Voigt fitting.
Testing BraggNN on experimental data from near-field high-energy diffraction microscopy (HEDM) with ground-truth grain positions.

Main Results:

BraggNN peak positions showed minimal deviation (less than 0.29 and 0.57 pixels for 75% and 95% of peaks, respectively) compared to pseudo-Voigt fitting.
Application to experimental HEDM data resulted in 15% smaller errors in grain position calculations using BraggNN versus pseudo-Voigt.
BraggNN demonstrated significant speed improvements, running over 200 times faster than conventional methods on a consumer GPU.

Conclusions:

BraggNN offers a substantial performance improvement for X-ray diffraction peak analysis.
The deep-learning approach enables rapid, high-precision peak determination crucial for real-time feedback in HEDM.
BraggNN represents a significant advancement for computational efficiency in diffraction microscopy.