Integration of machine learning with neutron scattering for the Hamiltonian tuning of spin ice under pressure

Autores
Samarakoon, Anjana; Tennant, D. Alan; Ye, Feng; Zhang, Qiang; Grigera, Santiago Andrés
Año de publicación
2022
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Quantum materials research requires co-design of theory with experiments and involves demanding simulations and the analysis of vast quantities of data, usually including pattern recognition and clustering. Artificial intelligence is a natural route to optimise these processes and bring theory and experiments together. Here, we propose a scheme that integrates machine learning with high-performance simulations and scattering measurements, covering the pipeline of typical neutron experiments. Our approach uses nonlinear autoencoders trained on realistic simulations along with a fast surrogate for the calculation of scattering in the form of a generative model. We demonstrate this approach in a highly frustrated magnet, Dy2Ti2O7, using machine learning predictions to guide the neutron scattering experiment under hydrostatic pressure, extract material parameters and construct a phase diagram. Our scheme provides a comprehensive set of capabilities that allows direct integration of theory along with automated data processing and provides on a rapid timescale direct insight into a challenging condensed matter system.
Fil: Samarakoon, Anjana. Oak Ridge National Laboratory; Estados Unidos. Argonne National Laboratory; Estados Unidos
Fil: Tennant, D. Alan. Oak Ridge National Laboratory; Estados Unidos
Fil: Ye, Feng. Oak Ridge National Laboratory; Estados Unidos
Fil: Zhang, Qiang. Oak Ridge National Laboratory; 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
Materia
Machine learning
Frustrated systems
Neutron scattering
Magnetic materials
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:11336/215364
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Integration of machine learning with neutron scattering for the Hamiltonian tuning of spin ice under pressureSamarakoon, AnjanaTennant, D. AlanYe, FengZhang, QiangGrigera, Santiago AndrésMachine learningFrustrated systemsNeutron scatteringMagnetic materialshttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1Quantum materials research requires co-design of theory with experiments and involves demanding simulations and the analysis of vast quantities of data, usually including pattern recognition and clustering. Artificial intelligence is a natural route to optimise these processes and bring theory and experiments together. Here, we propose a scheme that integrates machine learning with high-performance simulations and scattering measurements, covering the pipeline of typical neutron experiments. Our approach uses nonlinear autoencoders trained on realistic simulations along with a fast surrogate for the calculation of scattering in the form of a generative model. We demonstrate this approach in a highly frustrated magnet, Dy2Ti2O7, using machine learning predictions to guide the neutron scattering experiment under hydrostatic pressure, extract material parameters and construct a phase diagram. Our scheme provides a comprehensive set of capabilities that allows direct integration of theory along with automated data processing and provides on a rapid timescale direct insight into a challenging condensed matter system.Fil: Samarakoon, Anjana. Oak Ridge National Laboratory; Estados Unidos. Argonne National Laboratory; Estados UnidosFil: Tennant, D. Alan. Oak Ridge National Laboratory; Estados UnidosFil: Ye, Feng. Oak Ridge National Laboratory; Estados UnidosFil: Zhang, Qiang. Oak Ridge National Laboratory; 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; ArgentinaSpringer2022-11info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/215364Samarakoon, Anjana; Tennant, D. Alan; Ye, Feng; Zhang, Qiang; Grigera, Santiago Andrés; Integration of machine learning with neutron scattering for the Hamiltonian tuning of spin ice under pressure; Springer; Communications Materials; 3; 1; 11-2022; 1-112662-4443CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.nature.com/articles/s43246-022-00306-7info:eu-repo/semantics/altIdentifier/doi/10.1038/s43246-022-00306-7info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:12:19Zoai:ri.conicet.gov.ar:11336/215364instacron: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:34982025-09-29 10:12:19.725CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Integration of machine learning with neutron scattering for the Hamiltonian tuning of spin ice under pressure
title Integration of machine learning with neutron scattering for the Hamiltonian tuning of spin ice under pressure
spellingShingle Integration of machine learning with neutron scattering for the Hamiltonian tuning of spin ice under pressure
Samarakoon, Anjana
Machine learning
Frustrated systems
Neutron scattering
Magnetic materials
title_short Integration of machine learning with neutron scattering for the Hamiltonian tuning of spin ice under pressure
title_full Integration of machine learning with neutron scattering for the Hamiltonian tuning of spin ice under pressure
title_fullStr Integration of machine learning with neutron scattering for the Hamiltonian tuning of spin ice under pressure
title_full_unstemmed Integration of machine learning with neutron scattering for the Hamiltonian tuning of spin ice under pressure
title_sort Integration of machine learning with neutron scattering for the Hamiltonian tuning of spin ice under pressure
dc.creator.none.fl_str_mv Samarakoon, Anjana
Tennant, D. Alan
Ye, Feng
Zhang, Qiang
Grigera, Santiago Andrés
author Samarakoon, Anjana
author_facet Samarakoon, Anjana
Tennant, D. Alan
Ye, Feng
Zhang, Qiang
Grigera, Santiago Andrés
author_role author
author2 Tennant, D. Alan
Ye, Feng
Zhang, Qiang
Grigera, Santiago Andrés
author2_role author
author
author
author
dc.subject.none.fl_str_mv Machine learning
Frustrated systems
Neutron scattering
Magnetic materials
topic Machine learning
Frustrated systems
Neutron scattering
Magnetic materials
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Quantum materials research requires co-design of theory with experiments and involves demanding simulations and the analysis of vast quantities of data, usually including pattern recognition and clustering. Artificial intelligence is a natural route to optimise these processes and bring theory and experiments together. Here, we propose a scheme that integrates machine learning with high-performance simulations and scattering measurements, covering the pipeline of typical neutron experiments. Our approach uses nonlinear autoencoders trained on realistic simulations along with a fast surrogate for the calculation of scattering in the form of a generative model. We demonstrate this approach in a highly frustrated magnet, Dy2Ti2O7, using machine learning predictions to guide the neutron scattering experiment under hydrostatic pressure, extract material parameters and construct a phase diagram. Our scheme provides a comprehensive set of capabilities that allows direct integration of theory along with automated data processing and provides on a rapid timescale direct insight into a challenging condensed matter system.
Fil: Samarakoon, Anjana. Oak Ridge National Laboratory; Estados Unidos. Argonne National Laboratory; Estados Unidos
Fil: Tennant, D. Alan. Oak Ridge National Laboratory; Estados Unidos
Fil: Ye, Feng. Oak Ridge National Laboratory; Estados Unidos
Fil: Zhang, Qiang. Oak Ridge National Laboratory; 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
description Quantum materials research requires co-design of theory with experiments and involves demanding simulations and the analysis of vast quantities of data, usually including pattern recognition and clustering. Artificial intelligence is a natural route to optimise these processes and bring theory and experiments together. Here, we propose a scheme that integrates machine learning with high-performance simulations and scattering measurements, covering the pipeline of typical neutron experiments. Our approach uses nonlinear autoencoders trained on realistic simulations along with a fast surrogate for the calculation of scattering in the form of a generative model. We demonstrate this approach in a highly frustrated magnet, Dy2Ti2O7, using machine learning predictions to guide the neutron scattering experiment under hydrostatic pressure, extract material parameters and construct a phase diagram. Our scheme provides a comprehensive set of capabilities that allows direct integration of theory along with automated data processing and provides on a rapid timescale direct insight into a challenging condensed matter system.
publishDate 2022
dc.date.none.fl_str_mv 2022-11
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/11336/215364
Samarakoon, Anjana; Tennant, D. Alan; Ye, Feng; Zhang, Qiang; Grigera, Santiago Andrés; Integration of machine learning with neutron scattering for the Hamiltonian tuning of spin ice under pressure; Springer; Communications Materials; 3; 1; 11-2022; 1-11
2662-4443
CONICET Digital
CONICET
url http://hdl.handle.net/11336/215364
identifier_str_mv Samarakoon, Anjana; Tennant, D. Alan; Ye, Feng; Zhang, Qiang; Grigera, Santiago Andrés; Integration of machine learning with neutron scattering for the Hamiltonian tuning of spin ice under pressure; Springer; Communications Materials; 3; 1; 11-2022; 1-11
2662-4443
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.nature.com/articles/s43246-022-00306-7
info:eu-repo/semantics/altIdentifier/doi/10.1038/s43246-022-00306-7
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 Springer
publisher.none.fl_str_mv Springer
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 13.070432

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