Machine-learning-assisted insight into spin ice Dy2Ti2O7

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...

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Detalles Bibliográficos
Autores principales: Samarakoon, Anjana M., Barros, Kipton, Li, Ying Wai, Eisenbach, Markus, Zhang, Qiang, Ye, Feng, Sharma, V., Dun, Z. L., Zhou, Haidong, Grigera, Santiago Andrés, Batista, Cristian D., Tennant, D. Alan
Formato: Articulo
Lenguaje:Inglés
Publicado: 2020
Materias:
Física
Model Hamiltonians
Autoencoder
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/119733
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-119733
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Física
Model Hamiltonians
Autoencoder
spellingShingle Física
Model Hamiltonians
Autoencoder
Samarakoon, Anjana M.
Barros, Kipton
Li, Ying Wai
Eisenbach, Markus
Zhang, Qiang
Ye, Feng
Sharma, V.
Dun, Z. L.
Zhou, Haidong
Grigera, Santiago Andrés
Batista, Cristian D.
Tennant, D. Alan
Machine-learning-assisted insight into spin ice Dy2Ti2O7
topic_facet Física
Model Hamiltonians
Autoencoder
description 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.
format Articulo
Articulo
author Samarakoon, Anjana M.
Barros, Kipton
Li, Ying Wai
Eisenbach, Markus
Zhang, Qiang
Ye, Feng
Sharma, V.
Dun, Z. L.
Zhou, Haidong
Grigera, Santiago Andrés
Batista, Cristian D.
Tennant, D. Alan
author_facet Samarakoon, Anjana M.
Barros, Kipton
Li, Ying Wai
Eisenbach, Markus
Zhang, Qiang
Ye, Feng
Sharma, V.
Dun, Z. L.
Zhou, Haidong
Grigera, Santiago Andrés
Batista, Cristian D.
Tennant, D. Alan
author_sort Samarakoon, Anjana M.
title Machine-learning-assisted insight into spin ice Dy2Ti2O7
title_short Machine-learning-assisted insight into spin ice Dy2Ti2O7
title_full Machine-learning-assisted insight into spin ice Dy2Ti2O7
title_fullStr Machine-learning-assisted insight into spin ice Dy2Ti2O7
title_full_unstemmed Machine-learning-assisted insight into spin ice Dy2Ti2O7
title_sort machine-learning-assisted insight into spin ice dy2ti2o7
publishDate 2020
url http://sedici.unlp.edu.ar/handle/10915/119733
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