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X-ray Crystallography02:18

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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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Machine learning in crystallography and structural science.

Simon J L Billinge1, Thomas Proffen2

  • 1Department of Applied Physics and Applied Mathematics, Columbia University, New York, USA.

Acta Crystallographica. Section A, Foundations and Advances
|January 26, 2024
PubMed
Summary
This summary is machine-generated.

This overview introduces machine learning (ML) applications in crystallography and structural science. It provides foundational AI and ML concepts and a historical perspective from IUCr journals.

Keywords:
IUCr Journalsartificial intelligencedeep learningmachine learningneural networks

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Area of Science:

  • Crystallography and structural science
  • Computational science
  • Data science

Background:

  • Machine learning (ML) is increasingly applied in scientific research.
  • Crystallography and structural science generate large datasets suitable for ML analysis.
  • A growing body of literature on ML in these fields is emerging.

Purpose of the Study:

  • To provide an overview of a virtual collection of articles on ML in crystallography and structural science.
  • To introduce fundamental artificial intelligence (AI) and ML concepts.
  • To present a historical context of ML applications within IUCr journals.

Main Methods:

  • Literature review of articles within the IUCr journal family (Acta Crystallographica Sections A, B, D, IUCrJ, Journal of Synchrotron Radiation).
  • Conceptual explanation of key AI and ML terms.
  • Historical analysis of ML's emergence in structural science publications.

Main Results:

  • Identification and curation of a virtual collection focusing on ML in crystallography and structural science.
  • Accessible definitions of core AI and ML terminology.
  • A timeline illustrating the historical integration of ML into structural science research.

Conclusions:

  • The virtual collection serves as a valuable resource for researchers entering the field.
  • Understanding AI and ML fundamentals is crucial for leveraging these tools in structural science.
  • The historical perspective highlights the progressive adoption and impact of ML in the discipline.