Nature Communications (Feb 2020)

Machine-learning-assisted insight into spin ice Dy2Ti2O7

  • Anjana M. Samarakoon,
  • Kipton Barros,
  • Ying Wai Li,
  • Markus Eisenbach,
  • Qiang Zhang,
  • Feng Ye,
  • V. Sharma,
  • Z. L. Dun,
  • Haidong Zhou,
  • Santiago A. Grigera,
  • Cristian D. Batista,
  • D. Alan Tennant

DOI
https://doi.org/10.1038/s41467-020-14660-y
Journal volume & issue
Vol. 11, no. 1
pp. 1 – 9

Abstract

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Developing an understanding of a material’s magnetic behaviour based on neutron scattering measurements often relies on extracting an effective spin model. Samarakoon et al. demonstrate an automated machine learning approach to this problem, leading to more robust inferences from complex data.