  Machine learning promises to deliver powerful new approaches to neutron
scattering from magnetic materials. Large scale simulations provide the means
to realise this with approaches including spin-wave, Landau Lifshitz, and Monte
Carlo methods. These approaches are shown to be effective at simulating
magnetic structures and dynamics in a wide range of materials. Using large
numbers of simulations the effectiveness of machine learning approaches are
assessed. Principal component analysis and nonlinear autoencoders are
considered with the latter found to provide a high degree of compression and to
be highly suited to neutron scattering problems. Agglomerative heirarchical
clustering in the latent space is shown to be effective at extracting phase
diagrams of behavior and features in an automated way that aid understanding
and interpretation. The autoencoders are also well suited to optimizing model
parameters and were found to be highly advantageous over conventional fitting
approaches including being tolerant of artifacts in untreated data. The
potential of machine learning to automate complex data analysis tasks including
the inversion of neutron scattering data into models and the processing of
large volumes of multidimensional data is assessed. Directions for future
developments are considered and machine learning argued to have high potential
for impact on neutron science generally.
