Integration of machine learning with neutron scattering for the Hamiltonian tuning of spin ice under pressure
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 e...
| Autores principales: | , , , , |
|---|---|
| Formato: | Articulo |
| Lenguaje: | Inglés |
| Publicado: |
2022
|
| Materias: | |
| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/154709 |
| Aporte de: |
| id |
I19-R120-10915-154709 |
|---|---|
| record_format |
dspace |
| spelling |
I19-R120-10915-1547092023-06-27T20:07:19Z http://sedici.unlp.edu.ar/handle/10915/154709 issn:2662-4443 Integration of machine learning with neutron scattering for the Hamiltonian tuning of spin ice under pressure Samarakoon, Anjana Tennant, David Alan Ye, Feng Zhang, Qiang Grigera, Santiago Andrés 2022 2023-06-27T13:35:39Z en Ciencias Exactas Física Computational science Magnetic properties and materials 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. Instituto de Física de Líquidos y Sistemas Biológicos Articulo Articulo http://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International (CC BY 4.0) application/pdf |
| institution |
Universidad Nacional de La Plata |
| institution_str |
I-19 |
| repository_str |
R-120 |
| collection |
SEDICI (UNLP) |
| language |
Inglés |
| topic |
Ciencias Exactas Física Computational science Magnetic properties and materials |
| spellingShingle |
Ciencias Exactas Física Computational science Magnetic properties and materials Samarakoon, Anjana Tennant, David Alan Ye, Feng Zhang, Qiang Grigera, Santiago Andrés Integration of machine learning with neutron scattering for the Hamiltonian tuning of spin ice under pressure |
| topic_facet |
Ciencias Exactas Física Computational science Magnetic properties and materials |
| 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. |
| format |
Articulo Articulo |
| author |
Samarakoon, Anjana Tennant, David Alan Ye, Feng Zhang, Qiang Grigera, Santiago Andrés |
| author_facet |
Samarakoon, Anjana Tennant, David Alan Ye, Feng Zhang, Qiang Grigera, Santiago Andrés |
| author_sort |
Samarakoon, Anjana |
| title |
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_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 |
| publishDate |
2022 |
| url |
http://sedici.unlp.edu.ar/handle/10915/154709 |
| work_keys_str_mv |
AT samarakoonanjana integrationofmachinelearningwithneutronscatteringforthehamiltoniantuningofspiniceunderpressure AT tennantdavidalan integrationofmachinelearningwithneutronscatteringforthehamiltoniantuningofspiniceunderpressure AT yefeng integrationofmachinelearningwithneutronscatteringforthehamiltoniantuningofspiniceunderpressure AT zhangqiang integrationofmachinelearningwithneutronscatteringforthehamiltoniantuningofspiniceunderpressure AT grigerasantiagoandres integrationofmachinelearningwithneutronscatteringforthehamiltoniantuningofspiniceunderpressure |
| _version_ |
1770170846393925632 |