2019/06/30 by Anjana M Samarakoon, Anjana M. Samarakoon, Kipton Barros +10 · 1 citation
Materials Science · Physics and Astronomy · #Advanced Condensed Matter Physics #Autoencoder #Machine Learning in Materials Science #Magnetic field #Multiferroics and related materials #Neutron scattering #Scattering #Spin (aerodynamics) #Spin engineering #Spin glass #Spin ice #cond-mat.dis-nn #cond-mat.str-el
paper · pdf · doi:10.1038/s41467-020-14660-y
18 pages, 6 figures
openalex created_date 2019/08/13 · openalex publication_date 2020/02/14 · arxiv created 2020/11/11 · arxiv updated 2020/11/13 · openalex updated_date 2026/08/06
Abstract Complex behavior poses challenges in extracting models from experiment. An example is spin liquid formation in frustrated magnets like Dy 2 Ti 2 O 7 . 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 Dy 2 Ti 2 O 7 . 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.