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Machine-learning-assisted insight into spin ice Dy2Ti2O7

Author

Listed:
  • Anjana M. Samarakoon

    (Neutron Scattering Division, Oak Ridge National Laboratory)

  • Kipton Barros

    (Theoretical Division and CNLS, Los Alamos National Laboratory)

  • Ying Wai Li

    (National Center for Computational Sciences, Oak Ridge National Laboratory)

  • Markus Eisenbach

    (National Center for Computational Sciences, Oak Ridge National Laboratory
    Materials Science and Technology Division, Oak Ridge National Laboratory)

  • Qiang Zhang

    (Neutron Scattering Division, Oak Ridge National Laboratory
    Louisiana State University)

  • Feng Ye

    (Neutron Scattering Division, Oak Ridge National Laboratory)

  • V. Sharma

    (University of Tennessee)

  • Z. L. Dun

    (University of Tennessee)

  • Haidong Zhou

    (University of Tennessee)

  • Santiago A. Grigera

    (Instituto de Física de Líquidos y Sistemas Biológicos, UNLP-CONICET
    University of St Andrews)

  • Cristian D. Batista

    (Neutron Scattering Division, Oak Ridge National Laboratory
    University of Tennessee)

  • D. Alan Tennant

    (Materials Science and Technology Division, Oak Ridge National Laboratory)

Abstract

Complex behavior poses challenges in extracting models from experiment. An example is spin liquid formation in frustrated magnets like Dy2Ti2O7. Understanding has been hindered by issues including disorder, glass formation, and interpretation of scattering data. Here, we use an automated capability to extract model Hamiltonians from data, and to identify different magnetic regimes. This involves training an autoencoder to learn a compressed representation of three-dimensional diffuse scattering, over a wide range of spin Hamiltonians. The autoencoder finds optimal matches according to scattering and heat capacity data and provides confidence intervals. Validation tests indicate that our optimal Hamiltonian accurately predicts temperature and field dependence of both magnetic structure and magnetization, as well as glass formation and irreversibility in Dy2Ti2O7. The autoencoder can also categorize different magnetic behaviors and eliminate background noise and artifacts in raw data. Our methodology is readily applicable to other materials and types of scattering problems.

Suggested Citation

  • 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, 2020. "Machine-learning-assisted insight into spin ice Dy2Ti2O7," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-14660-y
    DOI: 10.1038/s41467-020-14660-y
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    Cited by:

    1. Sathya R. Chitturi & Zhurun Ji & Alexander N. Petsch & Cheng Peng & Zhantao Chen & Rajan Plumley & Mike Dunne & Sougata Mardanya & Sugata Chowdhury & Hongwei Chen & Arun Bansil & Adrian Feiguin & Alex, 2023. "Capturing dynamical correlations using implicit neural representations," Nature Communications, Nature, vol. 14(1), pages 1-8, December.
    2. Naween Anand & Kevin Barry & Jennifer N. Neu & David E. Graf & Qing Huang & Haidong Zhou & Theo Siegrist & Hitesh J. Changlani & Christianne Beekman, 2022. "Investigation of the monopole magneto-chemical potential in spin ices using capacitive torque magnetometry," Nature Communications, Nature, vol. 13(1), pages 1-8, December.

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