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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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Updated: Jul 11, 2025

Workflow and Tools for Crystallographic Fragment Screening at the Helmholtz-Zentrum Berlin

Workflow and Tools for Crystallographic Fragment Screening at the Helmholtz-Zentrum Berlin

Published on: March 3, 2021

Automated crystal system identification from electron diffraction patterns using multiview opinion fusion machine

Jie Chen¹, Hengrui Zhang¹, Carolin B Wahl^2,3

¹Department of Mechanical Engineering, Northwestern University, Evanston, IL 60208.

Proceedings of the National Academy of Sciences of the United States of America

|November 9, 2023

Summary

This summary is machine-generated.

This study introduces a new machine learning framework for classifying crystal structures from electron diffraction patterns, even with random orientations. This accelerates nanomaterials discovery by automating structural characterization in high-throughput experiments.

Keywords:

crystal system electron diffraction patterns machine learning multiview opinion fusion

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Area of Science:

Materials Science
Crystallography
Machine Learning

Background:

High-throughput nanomaterials discovery is hindered by slow structural characterization.
Current machine learning methods struggle with electron diffraction patterns from randomly oriented crystals.

Purpose of the Study:

To develop an automated framework for crystal system classification from electron diffraction patterns with arbitrary orientations.
To improve the speed and accuracy of structural characterization in nanomaterials discovery.

Main Methods:

Developed a framework inspired by human decision-making for automated crystal system classification.
Trained a convolutional neural network using evidential deep learning.
Quantified and leveraged predictive uncertainties to fuse multiview predictions using vector map representations of diffraction patterns.

Main Results:

Achieved a testing accuracy of 0.94 in the considered examples.
Demonstrated robustness to noise in diffraction patterns.
Retained remarkable accuracy with experimental data.

Conclusions:

Machine learning can significantly accelerate experimental high-throughput materials data analytics.
The developed framework offers a robust solution for automated crystal structure classification.
This approach addresses a key bottleneck in nanomaterials discovery.