Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Three-Dimensional Analysis of Strain01:29

Three-Dimensional Analysis of Strain

421
Three-dimensional strain analysis is crucial for understanding how materials deform under stress, particularly in elastic, homogeneous materials. This method employs principal stress axes to simplify complex stress states into more understandable forms. Subjected to stress, a small cubic element within a material either expands or contracts along these axes, transforming into a rectangular parallelepiped. This transformation effectively illustrates the material's deformation. The principal...
421
Electron Microscope Tomography and Single-particle Reconstruction01:07

Electron Microscope Tomography and Single-particle Reconstruction

2.6K
Transmission electron microscopy (TEM) can be used to determine the 3D structure of biological samples with the help of techniques such as electron microscope tomography and single-particle reconstruction. While single-particle reconstruction can examine macromolecules and macromolecular complexes in vitro conditions only, tomography permits the study of cell components or small cells in vivo.
Electron Tomography
Electron tomography can be performed either in TEM or STEM (scanning transmission...
2.6K

You might also read

Related Articles

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

Sort by
Same author

Mitochondria-related pathogenic genes in acute and chronic kidney disease: a Mendelian randomization study.

Renal failure·2026
Same author

Multimodal Feature Prototype Learning for Interpretable and Discriminative Cancer Survival Prediction.

IEEE journal of biomedical and health informatics·2026
Same author

Clinical significance of genetic mutations in adult patients with focal segmental glomerulosclerosis.

Kidney research and clinical practice·2026
Same author

Network-informed multi-trait genomic analysis decodes the shared genetic architecture and therapeutic landscape of pelvic floor disorders.

npj aging·2026
Same author

Synthesis of functionalized hydrosilanes via titanium-catalyzed hydroboration and hydrogenation of C(sp<sup>3</sup>)-Si bonds in silacyclobutanes.

Nature communications·2026
Same author

Multimodal Distillation and Fusion for Enhanced Age-Related Macular Degeneration Classification.

IEEE journal of biomedical and health informatics·2026

Related Experiment Video

Updated: Nov 11, 2025

Picometer-Precision Atomic Position Tracking through Electron Microscopy
15:04

Picometer-Precision Atomic Position Tracking through Electron Microscopy

Published on: July 3, 2021

7.9K

Training artificial neural networks for precision orientation and strain mapping using 4D electron diffraction

Renliang Yuan1, Jiong Zhang2, Lingfeng He3

  • 1Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; Materials Research Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.

Ultramicroscopy
|March 28, 2021
PubMed
Summary
This summary is machine-generated.

Artificial neural networks (ANNs) and convolutional neural networks (CNNs) can now precisely map crystal orientation and strain. This new method uses simulated electron diffraction patterns for advanced materials analysis.

Keywords:
4D-STEMArtificial neural networksOrientation mappingScanning electron nanodiffractionStrain analysis

More Related Videos

Synchrotron X-ray Microdiffraction and Fluorescence Imaging of Mineral and Rock Samples
10:12

Synchrotron X-ray Microdiffraction and Fluorescence Imaging of Mineral and Rock Samples

Published on: June 19, 2018

9.3K
Stereo-Imaging System DLT Calibration to Capture 3D In Situ Displacements of Stretched Peripheral Nerves
06:26

Stereo-Imaging System DLT Calibration to Capture 3D In Situ Displacements of Stretched Peripheral Nerves

Published on: January 12, 2024

584

Related Experiment Videos

Last Updated: Nov 11, 2025

Picometer-Precision Atomic Position Tracking through Electron Microscopy
15:04

Picometer-Precision Atomic Position Tracking through Electron Microscopy

Published on: July 3, 2021

7.9K
Synchrotron X-ray Microdiffraction and Fluorescence Imaging of Mineral and Rock Samples
10:12

Synchrotron X-ray Microdiffraction and Fluorescence Imaging of Mineral and Rock Samples

Published on: June 19, 2018

9.3K
Stereo-Imaging System DLT Calibration to Capture 3D In Situ Displacements of Stretched Peripheral Nerves
06:26

Stereo-Imaging System DLT Calibration to Capture 3D In Situ Displacements of Stretched Peripheral Nerves

Published on: January 12, 2024

584

Area of Science:

  • Materials Science
  • Computational Materials Science
  • Crystallography

Background:

  • Dynamical diffraction theory accurately simulates electron diffraction patterns from crystal structures.
  • Artificial neural networks (ANNs) can be trained using these simulations for structural analysis.
  • High-resolution mapping of crystal properties is crucial in materials science.

Purpose of the Study:

  • To develop and demonstrate ANN-based techniques for analyzing crystal structural properties.
  • To train ANNs using simulated dynamical electron diffraction patterns.
  • To map crystal orientation and strain at high spatial resolution using 4D datasets.

Main Methods:

  • Simulating dynamical electron diffraction patterns using crystal structure models and scattering potentials.
  • Training ANNs and CNNs with simulated diffraction patterns as input.
  • Applying trained ANNs to four-dimensional diffraction datasets (4D-DD) from scanning electron nanodiffraction (SEND) or 4D-STEM.

Main Results:

  • Achieved a 30-fold improvement in angular resolution for orientation mapping (0.009°).
  • Demonstrated high sensitivity for strain mapping (0.04% or less).
  • Showcased significant improvements in computational performance compared to previous methods.

Conclusions:

  • ANNs and CNNs offer a powerful, high-precision approach for analyzing crystal orientation and strain.
  • The developed methods enable high-resolution mapping of structural properties in materials.
  • This work advances the application of machine learning in electron diffraction analysis.