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 and D. Alan Tennant
Additional contact information
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
Qiang Zhang: Neutron Scattering Division, Oak Ridge National Laboratory
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
Cristian D. Batista: Neutron Scattering Division, Oak Ridge National Laboratory
D. Alan Tennant: Materials Science and Technology Division, Oak Ridge National Laboratory

Nature Communications, 2020, vol. 11, issue 1, 1-9

Abstract: 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.

Date: 2020
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DOI: 10.1038/s41467-020-14660-y

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