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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...

$X-ray Diffraction of Biological Samples$ 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: Aug 10, 2025

$Microfluidic Chips for In Situ Crystal X-ray Diffraction and In Situ Dynamic Light Scattering for Serial Crystallography$

Microfluidic Chips for In Situ Crystal X-ray Diffraction and In Situ Dynamic Light Scattering for Serial Crystallography

Published on: April 24, 2018

Data reduction for X-ray serial crystallography using machine learning.

Vahid Rahmani¹, Shah Nawaz¹, David Pennicard¹

¹Deutsches Elektronen-Synchrotron DESY, Notkestraße 85, 22607 Hamburg, Germany.

Journal of Applied Crystallography

|February 13, 2023

Summary

A new machine learning pipeline effectively distinguishes useful data (hits) from unusable data (misses) in serial crystallography experiments. The oriented FAST and rotated BRIEF (ORB) feature extractor with a multilayer perceptron classifier shows superior performance.

Keywords:

data reduction feature extraction machine learning serial crystallography

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$Microfluidic Chips for In Situ Crystal X-ray Diffraction and In Situ Dynamic Light Scattering for Serial Crystallography$

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Area of Science:

Structural Biology
Biophysics
Computational Science

Background:

Serial crystallography generates vast datasets, but a significant portion is unusable for analysis.
Efficiently identifying high-quality diffraction images (hits) from low-quality ones (misses) is crucial for maximizing experimental yield.

Purpose of the Study:

To develop and evaluate a novel machine learning pipeline for categorizing serial crystallography image data.
To identify the optimal combination of feature extraction and classification methods for this task.

Main Methods:

A pipeline was developed involving image feature extraction, feature summarization using the 'bag of visual words' model, and machine learning classification.
A comparative study evaluated various feature extractors and machine learning classifiers on serial crystallography data.
The oriented FAST and rotated BRIEF (ORB) feature extractor and multilayer perceptron classifier were identified as optimal.

Main Results:

The proposed pipeline successfully categorizes serial crystallography images.
The oriented FAST and rotated BRIEF (ORB) feature extractor combined with a multilayer perceptron classifier demonstrated the highest accuracy.
This combination outperformed other tested methods on both synthetic and experimental datasets.

Conclusions:

The ORB feature extractor and multilayer perceptron classifier provide a robust and efficient solution for hit/miss data categorization in serial crystallography.
This approach can significantly improve data processing efficiency and the quality of structural biology research.