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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

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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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Updated: May 9, 2025

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

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

Published on: June 8, 2016

Ab initio structure solutions from nanocrystalline powder diffraction data via diffusion models.

Gabe Guo^1,2, Tristan Luca Saidi³, Maxwell W Terban⁴

¹Columbia University, Department of Computer Science, New York, NY, USA. gabeguo@stanford.edu.

Nature Materials

|April 28, 2025

Summary

This summary is machine-generated.

A new machine learning model, PXRDnet, can determine the atomic structures of nanomaterials from powder diffraction patterns. This data-driven approach successfully solves structures as small as 10 Å, advancing materials science discovery.

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Area of Science:

Materials Science
Crystallography
Machine Learning

Background:

Determining the structure of nanomaterials is a significant challenge in materials science.
Existing methods struggle with nanoscale object structural determination.

Purpose of the Study:

To develop a machine learning approach for solving the structure of nanometre-sized objects.
To utilize generative diffusion models trained on known structures for accurate structural determination.

Main Methods:

A generative machine learning model, PXRDnet, based on diffusion processes was developed.
The model was trained on 45,229 known material structures.
PXRDnet factors measured diffraction patterns and statistical priors on unit cell structures.

Main Results:

PXRDnet successfully solved simulated nanocrystals as small as 10 Å across 200 materials.
The model achieved high accuracy, determining structural candidates four out of five times with an average R-factor error of 7%.
PXRDnet demonstrated capability in solving structures from noisy, real-world experimental diffraction patterns.

Conclusions:

Data-driven approaches, augmented by theoretical simulations, offer a promising path for solving previously undetermined nanomaterial structures.
PXRDnet represents a significant advancement in the structural analysis of nanomaterials.
This method has the potential to accelerate the discovery of new materials.