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

• Autores: Samarakoon, Anjana; Tennant, David Alan; Ye, Feng; Zhang, Qiang; Grigera, Santiago Andrés
• Año de publicación: 2022
• Idioma: inglés
• Tipo de recurso: artículo
• Materia: Ciencias Exactas; Física; Computational science; Magnetic properties and materials
• Institución: Universidad Nacional de La Plata

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