Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Improving protein and protein interactions using pseudo-dimers derived from monomeric proteins.

Nature communications·2026

Same author

CRA5 a high-fidelity compressed reanalysis atmospheric dataset for weather and climate research.

Scientific data·2026

Same author

Gaussian boson sampling with 1,024 squeezed states in 8,176 modes.

Nature·2026

Same author

Evidential deep learning for interatomic potentials.

Nature communications·2025

Same author

Glasses-free 3D display with ultrawide viewing range using deep learning.

Nature·2025

Same author

TripoSG: High-Fidelity 3D Shape Synthesis Using Large-Scale Rectified Flow Models.

IEEE transactions on pattern analysis and machine intelligence·2025

Same journal

On-Cell Detection of Polysaccharide One-Bond <sup>1</sup>J<sub>CH</sub> Couplings by Proton-Detected Solid-State NMR.

Journal of the American Chemical Society·2026

Same journal

Correction to "Unraveling the Effects of Fe Incorporation on High-Performance Water-Splitting Photoanodes".

Journal of the American Chemical Society·2026

Same journal

Proximity-Driven Protein Ligation Beyond the Concentration Limit.

Journal of the American Chemical Society·2026

Same journal

GraPhAI: Neural Networks for Solving Centrosymmetric Crystal Structures.

Journal of the American Chemical Society·2026

Same journal

Probing Stage Transition Kinetics in Li-Graphite Intercalation Compounds by Time-Resolved In Situ Solid-State NMR via <sup>13</sup>C Labeling.

Journal of the American Chemical Society·2026

Same journal

Dynamic Covalent Programming at DNA Base-Pairing Interfaces.

Journal of the American Chemical Society·2026

See all related articles

Search research articles

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.

More Related Videos

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

Picometer-Precision Atomic Position Tracking through Electron Microscopy

Picometer-Precision Atomic Position Tracking through Electron Microscopy

Published on: July 3, 2021

Related Experiment Videos

Last 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

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

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.