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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...

Crystallographic Point Groups

Crystallographic Point Groups

Crystallographic point groups represent the various symmetry operations that can occur within crystals. They are unique in that at least one point will always remain unchanged during these actions. For instance, consider the triclinic system. This system, devoid of any axis or plane of symmetry, aligns with the C1 and Ci point groups.where Cᵢ is characterized solely by a center of inversion.Contrastingly, the monoclinic system introduces an element of symmetry. This system with one plane...

Crystal Field Theory - Octahedral Complexes

Crystal Field Theory - Octahedral Complexes

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...

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Related Experiment Video

Updated: Mar 26, 2026

$Microcrystallography of Protein Crystals and In Cellulo Diffraction$

Microcrystallography of Protein Crystals and In Cellulo Diffraction

Published on: July 21, 2017

Computational crystallization.

Irem Altan¹, Patrick Charbonneau², Edward H Snell³

¹Department of Chemistry, Duke University, Durham, NC 27708, USA.

Archives of Biochemistry and Biophysics

|January 22, 2016

Summary

This summary is machine-generated.

Improving macromolecular crystallization involves computational tools and analyzing screening data. This approach moves beyond simple crystal/no crystal predictions to gain deeper biological insights and optimize reagent selection for successful structure determination.

Keywords:

Crystallization computational crystallization data crystallography macromolecular

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Last Updated: Mar 26, 2026

$Microcrystallography of Protein Crystals and In Cellulo Diffraction$

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Published on: July 21, 2017

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$On-Chip Crystallization and Large-Scale Serial Diffraction at Room Temperature$

On-Chip Crystallization and Large-Scale Serial Diffraction at Room Temperature

Published on: March 11, 2022

Area of Science:

Structural Biology
Biophysics
Computational Biology

Background:

Macromolecular crystallization is crucial for structure determination via crystallography.
Current experimental crystallization methods often rely on trial and error despite theoretical frameworks.
Existing computational tools typically predict binary outcomes (crystal or no crystal).

Purpose of the Study:

To discuss current efforts and theoretical underpinnings for improving macromolecular crystallization.
To highlight the potential of utilizing full data from crystallization screening experiments.
To explore computational analysis for predicting crystallization outcomes and identifying key reagents.

Main Methods:

Review of existing computational tools and mutational approaches for enhancing crystallization.
Theoretical underpinning using solubility phase diagrams.
Analysis of complete datasets from crystallization screening experiments.

Main Results:

Full analysis of crystallization screening data can yield more information than binary predictions.
New biological knowledge can be obtained by analyzing this data.
Targets can be sub-categorized to predict effective crystallization reagents.

Conclusions:

Computational analysis of crystallization requires complete and correctly formatted data.
Sparse data from massive screening efforts limits current potential.
Realizing the full potential of crystallization data requires addressing data completeness and formatting.