Argonne National Laboratory

Research Highlight | Materials Science

Harnessing machine learning to address X-ray diffraction
In a study published in Proceedings of the National Academy of Sciences, researchers report that machine learning can reveal all the critical features within X-ray scattering ​“big data” required to determine the origin of structural correlations.
(a) Crystal structure of Cd₂Re₂O₇. (b) The (0kl) plane of the reciprocal lattice, showing Bragg peaks with (c) distinct temperature dependences assigned to two clusters (green and yellow) by unsupervised machine learning. These peaks are surrounded by diffuse scattering clusters (red and blue) from Goldstone mode fluctuations.

Scientific Achievement

Machine learning identifies both the primary order parameter and the Goldstone mode fluctuations that drive symmetry breaking in Cd2Re2O7.

Significance and Impact

Machine learning reveals all the critical features within x-ray scattering ​“big data” required to determine the origin of structural correlations.

Research Details

  • High-energy synchrotron x-ray scattering, containing thousands of Brillouin Zones, was measured on Cd2Re2O7.
  • Machine learning classified both Bragg peaks and diffuse scattering into clusters by their temperature dependence.
  • Results show that the structural phase transitions at 112K and 200K are consistent with an Eu order parameter, with Goldstone mode fluctuations between the two Eu components at intermediate temperatures.

DOI: 10.1073/pnas.2109665119

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