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

Abstract

Designing and understanding quantum materials requires continuous feedback between experimental observations and theoretical modelling. Here, a machine learning scheme integrates experiments with theory and modelling on experimental timescales for extracting material parameters and properties of Dy2Ti2O7 spin-ice under pressure.