  Neutron scattering is a unique and versatile characterization technique for
probing the magnetic structure and dynamics of materials. However, instruments
at neutron scattering facilities in the world is limited, and instruments at
such facilities are perennially oversubscribed. We demonstrate a significant
reduction in experimental time required for neutron diffraction experiments by
implementation of autonomous navigation of measurement parameter space through
machine learning. Prior scientific knowledge and Bayesian active learning are
used to dynamically steer the sequence of measurements. We developed the
autonomous neutron diffraction explorer (ANDiE) and used it to determine the
magnetic order of MnO and Fe1.09Te. ANDiE can determine the Neel temperature of
the materials with 5-fold enhancement in efficiency and correctly identify the
transition dynamics via physics-informed Bayesian inference. ANDiE's active
learning approach is broadly applicable to a variety of neutron-based
experiments and can open the door for neutron scattering as a tool of
accelerated materials discovery.
