Integration of Machine Learning with Neutron Scattering for the Hamiltonian Tuning of Spin Ice under Pressure

SAMARAKOON, ANJANA M.; D. A. TENNANT; YE, FENG; ZHANG, QIANG; GRIGERA, SANTIAGO A.

Communication materials

Nature Publishing Group

Año: 2022 vol. 3

2662-4443

Quantum materials research requires co-design of theory with experiments and involvesdemanding simulations and the analysis of vast quantities of data, usually including patternrecognition and clustering. Artificial intelligence is a natural route to optimise these processesand bring theory and experiments together. Here, we propose a scheme that integratesmachine learning with high-performance simulations and scattering measurements, coveringthe pipeline of typical neutron experiments. Our approach uses nonlinear autoencoderstrained on realistic simulations along with a fast surrogate for the calculation of scattering inthe form of a generative model. We demonstrate this approach in a highly frustrated magnet,Dy2Ti2O7, using machine learning predictions to guide the neutron scattering experimentunder hydrostatic pressure, extract material parameters and construct a phase diagram. Ourscheme provides a comprehensive set of capabilities that allows direct integration of theoryalong with automated data processing and provides on a rapid timescale direct insight into achallenging condensed matter system.