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Related Concept Videos

X-ray Crystallography02:18

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 Groups01:29

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 and...
Determination of Crystal Structures01:29

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...
Lattice Energies of Ionic Crystals01:27

Lattice Energies of Ionic Crystals

Lattice energy represents the energy released when gaseous cations and anions combine to form an ionic solid, reflecting the strength of electrostatic interactions within the crystal. This process is fundamentally governed by Coulombic attraction between oppositely charged ions, where the potential energy varies inversely with the interionic distance and directly with the product of ionic charges. As ions approach one another, the electrostatic energy becomes increasingly negative, indicating a...

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Related Experiment Video

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Fully Autonomous Characterization and Data Collection from Crystals of Biological Macromolecules
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Predictive crystallography at scale: mapping, validating, and learning from 1000 crystal energy landscapes.

Christopher R Taylor1, Patrick W V Butler1, Graeme M Day1

  • 1School of Chemistry, University of Southampton, Southampton, SO17 1BJ, UK. g.m.day@soton.ac.uk.

Faraday Discussions
|September 20, 2024
PubMed
Summary

Computational crystal structure prediction (CSP) reliably identifies experimental organic crystal structures. This powerful materials discovery tool enables large-scale analysis and machine learning model development for solid-state materials.

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Solid-State Physics

Background:

  • Computational crystal structure prediction (CSP) is vital for materials discovery.
  • CSP reveals trends and insights beyond observed crystal structures.
  • Previous CSP studies were limited in scope and scale.

Purpose of the Study:

  • To demonstrate the reliability and scalability of CSP for small, rigid organic molecules.
  • To perform the largest survey of CSP for over 1000 organic compounds.
  • To enable large-scale data generation for materials design.

Main Methods:

  • Force-field-based CSP investigations.
  • Analysis of over 1000 small, rigid organic molecules.
  • Development of machine-learned energy potentials (neural network lattice energy correction, MACE equivariant message-passing neural network).

Main Results:

  • CSP located 99.4% of observed experimental structures.
  • 74% of observed structures were ranked among the most stable.
  • Developed transferable machine-learned potentials improving energy rankings.

Conclusions:

  • The CSP workflow is highly reliable and scalable for organic molecular crystals.
  • Large CSP datasets provide broad utility and explanatory power for materials design.
  • This approach facilitates insights into crystal properties and rationalizes empirical rules.