Machine-learning-assisted insight into spin ice Dy2Ti2O7

Developing an understanding of a material’s magnetic behaviour based on neutron scattering measurements often relies on extracting an effective spin model. Samarakoon et al. demonstrate an automated machine learning approach to this problem, leading to more robust inferences from complex data.

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Autores principales:	Anjana M. Samarakoon, Kipton Barros, Ying Wai Li, Markus Eisenbach, Qiang Zhang, Feng Ye, V. Sharma, Z. L. Dun, Haidong Zhou, Santiago A. Grigera, Cristian D. Batista, D. Alan Tennant
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/5964d7ee7a0c4c278294454fdb52db06
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spelling	oai:doaj.org-article:5964d7ee7a0c4c278294454fdb52db062021-12-02T16:50:02ZMachine-learning-assisted insight into spin ice Dy2Ti2O710.1038/s41467-020-14660-y2041-1723https://doaj.org/article/5964d7ee7a0c4c278294454fdb52db062020-02-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-14660-yhttps://doaj.org/toc/2041-1723Developing an understanding of a material’s magnetic behaviour based on neutron scattering measurements often relies on extracting an effective spin model. Samarakoon et al. demonstrate an automated machine learning approach to this problem, leading to more robust inferences from complex data.Anjana M. SamarakoonKipton BarrosYing Wai LiMarkus EisenbachQiang ZhangFeng YeV. SharmaZ. L. DunHaidong ZhouSantiago A. GrigeraCristian D. BatistaD. Alan TennantNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-9 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Anjana M. Samarakoon Kipton Barros Ying Wai Li Markus Eisenbach Qiang Zhang Feng Ye V. Sharma Z. L. Dun Haidong Zhou Santiago A. Grigera Cristian D. Batista D. Alan Tennant Machine-learning-assisted insight into spin ice Dy2Ti2O7
description	Developing an understanding of a material’s magnetic behaviour based on neutron scattering measurements often relies on extracting an effective spin model. Samarakoon et al. demonstrate an automated machine learning approach to this problem, leading to more robust inferences from complex data.
format	article
author	Anjana M. Samarakoon Kipton Barros Ying Wai Li Markus Eisenbach Qiang Zhang Feng Ye V. Sharma Z. L. Dun Haidong Zhou Santiago A. Grigera Cristian D. Batista D. Alan Tennant
author_facet	Anjana M. Samarakoon Kipton Barros Ying Wai Li Markus Eisenbach Qiang Zhang Feng Ye V. Sharma Z. L. Dun Haidong Zhou Santiago A. Grigera Cristian D. Batista D. Alan Tennant
author_sort	Anjana M. Samarakoon
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
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/5964d7ee7a0c4c278294454fdb52db06
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Machine-learning-assisted insight into spin ice Dy2Ti2O7

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