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dc.contributor.authorSamarakoon, Anjana M.
dc.contributor.authorBarros, Kipton
dc.contributor.authorLi, Ying Wai
dc.contributor.authorEisenbach, Markus
dc.contributor.authorZhang, Qiang
dc.contributor.authorYe, Feng
dc.contributor.authorSharma, V.
dc.contributor.authorDun, Z. L.
dc.contributor.authorZhou, Haidong
dc.contributor.authorGrigera, Santiago A.
dc.contributor.authorBatista, Cristian D.
dc.contributor.authorTennant, D. Alan
dc.date.accessioned2020-02-19T10:30:09Z
dc.date.available2020-02-19T10:30:09Z
dc.date.issued2020-02-14
dc.identifier.citationSamarakoon , A M , Barros , K , Li , Y W , Eisenbach , M , Zhang , Q , Ye , F , Sharma , V , Dun , Z L , Zhou , H , Grigera , S A , Batista , C D & Tennant , D A 2020 , ' Machine-learning-assisted insight into spin ice Dy 2 Ti 2 O 7 ' , Nature Communications , vol. 11 , 892 . https://doi.org/10.1038/s41467-020-14660-yen
dc.identifier.issn2041-1723
dc.identifier.otherPURE: 266433548
dc.identifier.otherPURE UUID: 0e97ca48-4030-44c7-9488-cbfb50003738
dc.identifier.othercrossref: 10.1038/s41467-020-14660-y
dc.identifier.otherScopus: 85079361589
dc.identifier.otherWOS: 000564261700001
dc.identifier.urihttps://hdl.handle.net/10023/19496
dc.description.abstractComplex 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.extent9
dc.language.isoeng
dc.relation.ispartofNature Communicationsen
dc.rightsCopyright 2020 the Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly fromt he copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/en
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectQC Physicsen
dc.subjectQD Chemistryen
dc.subjectT Technologyen
dc.subjectNDASen
dc.subjectBDCen
dc.subjectR2Cen
dc.subject.lccQA75en
dc.subject.lccQCen
dc.subject.lccQDen
dc.subject.lccTen
dc.titleMachine-learning-assisted insight into spin ice Dy2Ti2O7en
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews. School of Physics and Astronomyen
dc.contributor.institutionUniversity of St Andrews. Condensed Matter Physicsen
dc.identifier.doihttps://doi.org/10.1038/s41467-020-14660-y
dc.description.statusPeer revieweden
dc.identifier.urlhttps://www.nature.com/articles/s41467-020-14660-y#Sec14en


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