Artículo
Integration of machine learning with neutron scattering for the Hamiltonian tuning of spin ice under pressure
Fecha de publicación:
11/2022
Editorial:
Springer
Revista:
Communications Materials
ISSN:
2662-4443
Idioma:
Inglés
Tipo de recurso:
Artículo publicado
Clasificación temática:
Resumen
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.
Palabras clave:
Machine learning
,
Frustrated systems
,
Neutron scattering
,
Magnetic materials
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Colecciones
Articulos(IFLYSIB)
Articulos de INST.FISICA DE LIQUIDOS Y SIST.BIOLOGICOS (I)
Articulos de INST.FISICA DE LIQUIDOS Y SIST.BIOLOGICOS (I)
Citación
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
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