Integration of machine learning with neutron scattering for the Hamiltonian tuning of spin ice under pressure
Designing and understanding quantum materials requires continuous feedback between experimental observations and theoretical modelling. Here, a machine learning scheme integrates experiments with theory and modelling on experimental timescales for extracting material parameters and properties of Dy2...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-11-01
|
| Series: | Communications Materials |
| Online Access: | https://doi.org/10.1038/s43246-022-00306-7 |
| _version_ | 1811328679855259648 |
|---|---|
| author | Anjana Samarakoon D. Alan Tennant Feng Ye Qiang Zhang Santiago A. Grigera |
| author_facet | Anjana Samarakoon D. Alan Tennant Feng Ye Qiang Zhang Santiago A. Grigera |
| author_sort | Anjana Samarakoon |
| collection | DOAJ |
| description | Designing and understanding quantum materials requires continuous feedback between experimental observations and theoretical modelling. Here, a machine learning scheme integrates experiments with theory and modelling on experimental timescales for extracting material parameters and properties of Dy2Ti2O7 spin-ice under pressure. |
| first_indexed | 2024-04-13T15:30:28Z |
| format | Article |
| id | doaj.art-d0605a609ad14fbe9d0466970f848d1e |
| institution | Directory Open Access Journal |
| issn | 2662-4443 |
| language | English |
| last_indexed | 2024-04-13T15:30:28Z |
| publishDate | 2022-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Materials |
| spelling | doaj.art-d0605a609ad14fbe9d0466970f848d1e2022-12-22T02:41:24ZengNature PortfolioCommunications Materials2662-44432022-11-013111110.1038/s43246-022-00306-7Integration of machine learning with neutron scattering for the Hamiltonian tuning of spin ice under pressureAnjana Samarakoon0D. Alan Tennant1Feng Ye2Qiang Zhang3Santiago A. Grigera4Neutron Scattering Division, Oak Ridge National LaboratoryNeutron Scattering Division, Oak Ridge National LaboratoryNeutron Scattering Division, Oak Ridge National LaboratoryNeutron Scattering Division, Oak Ridge National LaboratoryInstituto de Física de Líquidos y Sistemas Biológicos, UNLP-CONICETDesigning and understanding quantum materials requires continuous feedback between experimental observations and theoretical modelling. Here, a machine learning scheme integrates experiments with theory and modelling on experimental timescales for extracting material parameters and properties of Dy2Ti2O7 spin-ice under pressure.https://doi.org/10.1038/s43246-022-00306-7 |
| spellingShingle | Anjana Samarakoon D. Alan Tennant Feng Ye Qiang Zhang Santiago A. Grigera Integration of machine learning with neutron scattering for the Hamiltonian tuning of spin ice under pressure Communications Materials |
| title | 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_short | 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 |
| url | https://doi.org/10.1038/s43246-022-00306-7 |
| work_keys_str_mv | AT anjanasamarakoon integrationofmachinelearningwithneutronscatteringforthehamiltoniantuningofspiniceunderpressure AT dalantennant integrationofmachinelearningwithneutronscatteringforthehamiltoniantuningofspiniceunderpressure AT fengye integrationofmachinelearningwithneutronscatteringforthehamiltoniantuningofspiniceunderpressure AT qiangzhang integrationofmachinelearningwithneutronscatteringforthehamiltoniantuningofspiniceunderpressure AT santiagoagrigera integrationofmachinelearningwithneutronscatteringforthehamiltoniantuningofspiniceunderpressure |