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Integration of Machine Learning with Neutron Scattering: Hamiltonian Tuning in Spin Ice with Pressure
- Publication Year :
- 2021
-
Abstract
- 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, Dy$_2$Ti$_2$O$_7$, 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.<br />Comment: main text (12 pages, 6 figures) + Sup. Info. (4 pages 5 figures)
- Subjects :
- Condensed Matter - Other Condensed Matter
Physics - Applied Physics
Subjects
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2110.15817
- Document Type :
- Working Paper