Machine-learning-assisted insight into spin ice Dy2Ti2O7
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.
Citation
Samarakoon , 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-y
Publication
Nature Communications
Status
Peer reviewed
ISSN
2041-1723Type
Journal article
Rights
Copyright 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/
Collections
Items in the St Andrews Research Repository are protected by copyright, with all rights reserved, unless otherwise indicated.