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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 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: Apr 21, 2026

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

CrystalX: High-Accuracy Crystal Structure Analysis Using Deep Learning.

Kaipeng Zheng^1,2,3, Weiran Huang^1,2,3, Wanli Ouyang²

¹School of Computer Science, Shanghai Jiao Tong University, Shanghai 200240, China.

Journal of the American Chemical Society

|April 20, 2026

Summary

This summary is machine-generated.

Deep learning model CrystalX automates atomic structure analysis for crystalline materials. It accurately deciphers complex patterns and corrects errors in published data, enabling faster discovery in self-driving laboratories.

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Picometer-Precision Atomic Position Tracking through Electron Microscopy

Picometer-Precision Atomic Position Tracking through Electron Microscopy

Published on: July 3, 2021

Area of Science:

Crystallography
Materials Science
Chemical Science

Background:

Atomic structure analysis is crucial for chemical and material sciences.
Current methods require extensive crystallography knowledge and software expertise, challenging daily demands.
Automating routine structure analysis is a significant unmet need.

Purpose of the Study:

To develop a deep learning model for fully automated, full-atom level routine structure analysis.
To validate the model's performance against experimental data and existing methods.
To demonstrate the model's capability in identifying and correcting errors in scientific literature.

Main Methods:

Developed CrystalX, a deep learning model for automated structure analysis.
Trained and validated CrystalX using over 50,000 X-ray diffraction measurements.
Employed a temporal validation scheme separating training and test data by publication time.

Main Results:

CrystalX significantly outperformed automated baselines in structure analysis.
The model accurately deciphered intricate geometric patterns in crystalline materials.
CrystalX identified and corrected expert interpretation errors in peer-reviewed publications, even those missed by CheckCIF alerts.

Conclusions:

CrystalX enables fully automated, human-free structure analysis of new compounds.
The model represents a new era of automation in routine structural analysis for self-driving laboratories.
CrystalX demonstrates the potential of deep learning to enhance accuracy and efficiency in crystallography.