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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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Related Experiment Video

Updated: Jul 12, 2025

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

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Published on: April 24, 2018

Weakly supervised learning for pattern classification in serial femtosecond crystallography.

Jianan Xie, Ji Liu, Chi Zhang

|October 20, 2023

Summary

Weakly supervised learning significantly reduces the need for labeled data in classifying X-ray diffraction patterns. This advancement aids in processing large datasets from serial femtosecond crystallography experiments.

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Last Updated: Jul 12, 2025

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

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Published on: April 24, 2018

Lipidico Injection Protocol for Serial Crystallography Measurements at the Australian Synchrotron

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$Microcrystallography of Protein Crystals and In Cellulo Diffraction$

Microcrystallography of Protein Crystals and In Cellulo Diffraction

Published on: July 21, 2017

Area of Science:

Structural Biology
Biophysics
Data Science

Background:

Serial femtosecond crystallography (SFX) at X-ray free electron lasers (XFELs) enables high-resolution crystal structure determination.
Processing the vast number of diffraction patterns from SFX experiments presents a significant data challenge.

Purpose of the Study:

To investigate the application of machine learning, specifically weakly supervised algorithms, for classifying diffraction patterns in SFX.
To reduce the reliance on large, labeled datasets for training pattern classification models.

Main Methods:

Utilized weakly supervised learning algorithms for the classification of diffraction patterns.
Compared the performance of weakly supervised methods against fully supervised methods in terms of accuracy and data requirements.

Main Results:

Weakly supervised methods achieved comparable accuracy to fully supervised methods.
Demonstrated a significant reduction in the number of labeled diffraction patterns required for effective training.

Conclusions:

Weakly supervised learning offers a viable solution to overcome data annotation bottlenecks in SFX data processing.
This approach can make machine learning more accessible and efficient for analyzing large-scale crystallographic datasets.