Machine-learning-assisted insight into spin ice Dy2Ti2O7

Autores
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
Año de publicación
2020
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
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.
Fil: Samarakoon, Anjana M.. Oak Ridge National Laboratory; Estados Unidos
Fil: Barros, Kipton. Los Alamos National High Magnetic Field Laboratory; Estados Unidos
Fil: Li, Ying Wai. Oak Ridge National Laboratory; Estados Unidos
Fil: Eisenbach, Markus. Oak Ridge National Laboratory; Estados Unidos
Fil: Zhang, Qiang. Oak Ridge National Laboratory; Estados Unidos. State University of Louisiana; Estados Unidos
Fil: Ye, Feng. Oak Ridge National Laboratory; Estados Unidos
Fil: Sharma, V.. University of Tennessee; Estados Unidos
Fil: Dun, Z. L.. University of Tennessee; Estados Unidos
Fil: Zhou, Haidong. University of Tennessee; Estados Unidos
Fil: Grigera, Santiago Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física de Líquidos y Sistemas Biológicos. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física de Líquidos y Sistemas Biológicos; Argentina. University of St. Andrews; Reino Unido
Fil: Batista, Cristian D.. Oak Ridge National Laboratory; Estados Unidos. University of Tennessee; Estados Unidos
Fil: Tennant, D. Alan. Oak Ridge National Laboratory; Estados Unidos
Materia
MACHINE LEARNING
CONDENSED MATTER
FRUSTRATION
MAGNETISM
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/154430
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/154430
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Machine-learning-assisted insight into spin ice Dy2Ti2O7Samarakoon, Anjana M.Barros, KiptonLi, Ying WaiEisenbach, MarkusZhang, QiangYe, FengSharma, V.Dun, Z. L.Zhou, HaidongGrigera, Santiago AndrésBatista, Cristian D.Tennant, D. AlanMACHINE LEARNINGCONDENSED MATTERFRUSTRATIONMAGNETISMhttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1Complex 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.Fil: Samarakoon, Anjana M.. Oak Ridge National Laboratory; Estados UnidosFil: Barros, Kipton. Los Alamos National High Magnetic Field Laboratory; Estados UnidosFil: Li, Ying Wai. Oak Ridge National Laboratory; Estados UnidosFil: Eisenbach, Markus. Oak Ridge National Laboratory; Estados UnidosFil: Zhang, Qiang. Oak Ridge National Laboratory; Estados Unidos. State University of Louisiana; Estados UnidosFil: Ye, Feng. Oak Ridge National Laboratory; Estados UnidosFil: Sharma, V.. University of Tennessee; Estados UnidosFil: Dun, Z. L.. University of Tennessee; Estados UnidosFil: Zhou, Haidong. University of Tennessee; Estados UnidosFil: Grigera, Santiago Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física de Líquidos y Sistemas Biológicos. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física de Líquidos y Sistemas Biológicos; Argentina. University of St. Andrews; Reino UnidoFil: Batista, Cristian D.. Oak Ridge National Laboratory; Estados Unidos. University of Tennessee; Estados UnidosFil: Tennant, D. Alan. Oak Ridge National Laboratory; Estados UnidosNature Publishing Group2020-02-14info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/154430Samarakoon, Anjana M.; Barros, Kipton; Li, Ying Wai; Eisenbach, Markus; Zhang, Qiang; et al.; Machine-learning-assisted insight into spin ice Dy2Ti2O7; Nature Publishing Group; Nature Communications; 11; 14-2-2020; 1-92041-1723CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1038/s41467-020-14660-yinfo:eu-repo/semantics/altIdentifier/url/https://www.nature.com/articles/s41467-020-14660-yinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-10T13:04:28Zoai:ri.conicet.gov.ar:11336/154430instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-10 13:04:28.463CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Machine-learning-assisted insight into spin ice Dy2Ti2O7
title Machine-learning-assisted insight into spin ice Dy2Ti2O7
spellingShingle Machine-learning-assisted insight into spin ice Dy2Ti2O7
Samarakoon, Anjana M.
MACHINE LEARNING
CONDENSED MATTER
FRUSTRATION
MAGNETISM
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
dc.creator.none.fl_str_mv 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 Samarakoon, Anjana M.
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_role author
author2 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
author2_role author
author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv MACHINE LEARNING
CONDENSED MATTER
FRUSTRATION
MAGNETISM
topic MACHINE LEARNING
CONDENSED MATTER
FRUSTRATION
MAGNETISM
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv 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.
Fil: Samarakoon, Anjana M.. Oak Ridge National Laboratory; Estados Unidos
Fil: Barros, Kipton. Los Alamos National High Magnetic Field Laboratory; Estados Unidos
Fil: Li, Ying Wai. Oak Ridge National Laboratory; Estados Unidos
Fil: Eisenbach, Markus. Oak Ridge National Laboratory; Estados Unidos
Fil: Zhang, Qiang. Oak Ridge National Laboratory; Estados Unidos. State University of Louisiana; Estados Unidos
Fil: Ye, Feng. Oak Ridge National Laboratory; Estados Unidos
Fil: Sharma, V.. University of Tennessee; Estados Unidos
Fil: Dun, Z. L.. University of Tennessee; Estados Unidos
Fil: Zhou, Haidong. University of Tennessee; Estados Unidos
Fil: Grigera, Santiago Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física de Líquidos y Sistemas Biológicos. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física de Líquidos y Sistemas Biológicos; Argentina. University of St. Andrews; Reino Unido
Fil: Batista, Cristian D.. Oak Ridge National Laboratory; Estados Unidos. University of Tennessee; Estados Unidos
Fil: Tennant, D. Alan. Oak Ridge National Laboratory; Estados Unidos
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.
publishDate 2020
dc.date.none.fl_str_mv 2020-02-14
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/154430
Samarakoon, Anjana M.; Barros, Kipton; Li, Ying Wai; Eisenbach, Markus; Zhang, Qiang; et al.; Machine-learning-assisted insight into spin ice Dy2Ti2O7; Nature Publishing Group; Nature Communications; 11; 14-2-2020; 1-9
2041-1723
CONICET Digital
CONICET
url http://hdl.handle.net/11336/154430
identifier_str_mv Samarakoon, Anjana M.; Barros, Kipton; Li, Ying Wai; Eisenbach, Markus; Zhang, Qiang; et al.; Machine-learning-assisted insight into spin ice Dy2Ti2O7; Nature Publishing Group; Nature Communications; 11; 14-2-2020; 1-9
2041-1723
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1038/s41467-020-14660-y
info:eu-repo/semantics/altIdentifier/url/https://www.nature.com/articles/s41467-020-14660-y
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Nature Publishing Group
publisher.none.fl_str_mv Nature Publishing Group
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.004268

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