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dc.contributor.author
Samarakoon, Anjana M.
dc.contributor.author
Barros, Kipton
dc.contributor.author
Li, Ying Wai
dc.contributor.author
Eisenbach, Markus
dc.contributor.author
Zhang, Qiang
dc.contributor.author
Ye, Feng
dc.contributor.author
Sharma, V.
dc.contributor.author
Dun, Z. L.
dc.contributor.author
Zhou, Haidong
dc.contributor.author
Grigera, Santiago Andrés
dc.contributor.author
Batista, Cristian D.
dc.contributor.author
Tennant, D. Alan
dc.date.available
2022-04-05T19:52:40Z
dc.date.issued
2020-02-14
dc.identifier.citation
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
dc.identifier.uri
http://hdl.handle.net/11336/154430
dc.description.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.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Nature Publishing Group
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by/2.5/ar/
dc.subject
MACHINE LEARNING
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CONDENSED MATTER
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FRUSTRATION
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MAGNETISM
dc.subject.classification
Física de los Materiales Condensados
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Ciencias Físicas
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CIENCIAS NATURALES Y EXACTAS
dc.title
Machine-learning-assisted insight into spin ice Dy2Ti2O7
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2021-04-23T19:06:45Z
dc.identifier.eissn
2041-1723
dc.journal.volume
11
dc.journal.pagination
1-9
dc.journal.pais
Reino Unido
dc.description.fil
Fil: Samarakoon, Anjana M.. Oak Ridge National Laboratory; Estados Unidos
dc.description.fil
Fil: Barros, Kipton. Los Alamos National High Magnetic Field Laboratory; Estados Unidos
dc.description.fil
Fil: Li, Ying Wai. Oak Ridge National Laboratory; Estados Unidos
dc.description.fil
Fil: Eisenbach, Markus. Oak Ridge National Laboratory; Estados Unidos
dc.description.fil
Fil: Zhang, Qiang. Oak Ridge National Laboratory; Estados Unidos. State University of Louisiana; Estados Unidos
dc.description.fil
Fil: Ye, Feng. Oak Ridge National Laboratory; Estados Unidos
dc.description.fil
Fil: Sharma, V.. University of Tennessee; Estados Unidos
dc.description.fil
Fil: Dun, Z. L.. University of Tennessee; Estados Unidos
dc.description.fil
Fil: Zhou, Haidong. University of Tennessee; Estados Unidos
dc.description.fil
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
dc.description.fil
Fil: Batista, Cristian D.. Oak Ridge National Laboratory; Estados Unidos. University of Tennessee; Estados Unidos
dc.description.fil
Fil: Tennant, D. Alan. Oak Ridge National Laboratory; Estados Unidos
dc.journal.title
Nature Communications
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1038/s41467-020-14660-y
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.nature.com/articles/s41467-020-14660-y
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