  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.
