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X-ray Crystallography02:18

X-ray Crystallography

24.5K
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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X-ray Diffraction of Biological Samples01:10

X-ray Diffraction of Biological Samples

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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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Crystal Field Theory - Octahedral Complexes02:58

Crystal Field Theory - Octahedral Complexes

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Crystal Field Theory
To explain the observed behavior of transition metal complexes (such as colors), a model involving electrostatic interactions between the electrons from the ligands and the electrons in the unhybridized d orbitals of the central metal atom has been developed. This electrostatic model is crystal field theory (CFT). It helps to understand, interpret, and predict the colors, magnetic behavior, and some structures of coordination compounds of transition metals.
CFT focuses on...
28.3K
Crystal Field Theory - Tetrahedral and Square Planar Complexes02:46

Crystal Field Theory - Tetrahedral and Square Planar Complexes

45.2K
Tetrahedral Complexes
Crystal field theory (CFT) is applicable to molecules in geometries other than octahedral. In octahedral complexes, the lobes of the dx2−y2 and dz2 orbitals point directly at the ligands. For tetrahedral complexes, the d orbitals remain in place, but with only four ligands located between the axes. None of the orbitals points directly at the tetrahedral ligands. However, the dx2−y2 and dz2 orbitals (along the Cartesian axes) overlap with the ligands less than the dxy,...
45.2K
Ionic Crystal Structures02:42

Ionic Crystal Structures

15.6K
Ionic crystals consist of two or more different kinds of ions that usually have different sizes. The packing of these ions into a crystal structure is more complex than the packing of metal atoms that are the same size.
Most monatomic ions behave as charged spheres, and their attraction for ions of opposite charge is the same in every direction. Consequently, stable structures for ionic compounds result (1) when ions of one charge are surrounded by as many ions as possible of the opposite...
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Related Experiment Video

Updated: Oct 15, 2025

Fully Autonomous Characterization and Data Collection from Crystals of Biological Macromolecules
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Fully Autonomous Characterization and Data Collection from Crystals of Biological Macromolecules

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Robust recognition and exploratory analysis of crystal structures via Bayesian deep learning.

Andreas Leitherer1, Angelo Ziletti2, Luca M Ghiringhelli2

  • 1Fritz-Haber-Institut der Max-Planck-Gesellschaft, 14195, Berlin-Dahlem, Germany. leitherer@fhi-berlin.mpg.de.

Nature Communications
|October 30, 2021
PubMed
Summary

ARISE, a novel Bayesian deep learning method, accurately identifies crystal structures from noisy materials science data. This approach handles complex patterns and provides uncertainty estimates, advancing data analysis in the field.

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Area of Science:

  • Materials Science
  • Artificial Intelligence
  • Crystallography

Background:

  • Traditional materials science data analysis struggles with complex patterns and noise.
  • Neural networks offer potential for advanced pattern recognition in scientific data.

Purpose of the Study:

  • Introduce ARISE, a Bayesian deep learning method for robust crystal structure identification.
  • Enable analysis of noisy atomic structural data from computations and experiments.

Main Methods:

  • Developed ARISE, a crystal-structure identification method utilizing Bayesian deep learning.
  • Trained the model on ideal crystal structures, demonstrating robustness to noise and perturbations.
  • Applied unsupervised learning to internal neural network representations for structural analysis.

Main Results:

  • ARISE correctly characterizes over 100 crystal structures, even in perturbed systems.
  • Provides principled uncertainty estimates correlated with crystalline order in metallic nanoparticles.
  • Reveals grain boundaries and structural regions with interpretable geometrical properties.

Conclusions:

  • ARISE represents a paradigm shift in materials science data analysis.
  • Enables accurate characterization of noisy and complex atomic structural data.
  • Facilitates deeper understanding of material properties through advanced AI.