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Determination of Crystal Structures

Determination of Crystal Structures

In the late 1800s, the revelation that light extended beyond visible wavelengths led to the discovery of X-rays by Wilhelm Roentgen. Recognized as high-energy electromagnetic radiation with short wavelengths, X-rays prompted exploration into their interaction with crystals. Max von Laue proposed in 1912 that the periodic arrangement of atoms, ions, or molecules in crystals would cause them to diffract X-rays, a hypothesis confirmed through experiments with copper sulfate and zinc sulfide...

$X-ray Diffraction of Biological Samples$ 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...

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: Mar 1, 2026

$X-ray Powder Diffraction in Conservation Science: Towards Routine Crystal Structure Determination of Corrosion Products on Heritage Art Objects$

X-ray Powder Diffraction in Conservation Science: Towards Routine Crystal Structure Determination of Corrosion Products on Heritage Art Objects

Published on: June 8, 2016

Equivariant diffusion solution for inorganic crystal structure determination from powder X-ray diffraction data.

Dongfang Yu^1,2, Zhewen Zhu^1,2, Fucheng Leng³

¹School of Materials Science and Engineering, Zhejiang University, Hangzhou, China.

Nature Communications

|February 27, 2026

Summary

This summary is machine-generated.

We developed a fast AI model to determine inorganic crystal structures from X-ray diffraction patterns, improving accuracy and speed for materials discovery.

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Last Updated: Mar 1, 2026

$X-ray Powder Diffraction in Conservation Science: Towards Routine Crystal Structure Determination of Corrosion Products on Heritage Art Objects$

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$High Pressure Single Crystal Diffraction at PX^2$

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Area of Science:

Materials Science
Crystallography
Artificial Intelligence

Background:

Crystal structure determination is crucial for understanding material properties.
Solving structures from Powder X-ray Diffraction (PXRD) data is challenging and often requires manual expert interpretation.
Existing databases contain numerous incomplete or inaccurate structural models.

Purpose of the Study:

To develop an automated method for directly inferring atomic coordinates from PXRD patterns.
To significantly accelerate the process of crystal structure solution.
To improve the accuracy and completeness of structural models in materials databases.

Main Methods:

Development of an equivariant graph neural network-based diffusion model.
Direct inference of atomic coordinates from PXRD data, starting from random noise.
Iterative refinement of coordinates to generate chemically valid structures matching the experimental pattern.

Main Results:

The model achieves high success rates: 82.3% on simulated and 81.6% on experimental datasets.
Crystal structure solution is achieved in an average of 0.6 seconds on a single GPU, orders of magnitude faster than previous methods.
Successfully corrected 39 unfavorable database entries and completed 912 entries lacking atomic positions.

Conclusions:

The developed AI model provides a robust and automated solution for crystal structure determination from diffraction data.
This approach significantly enhances the speed and accuracy of materials characterization.
It paves the way for autonomous materials discovery through closed-loop systems.