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.
@article{arxiv.2108.08918,
title = {On-the-fly Autonomous Control of Neutron Diffraction via Physics-Informed Bayesian Active Learning},
author = {Austin McDannald and Matthias Frontzek and Andrei T. Savici and Mathieu Doucet and Efrain E. Rodriguez and Kate Meuse and Jessica Opsahl-Ong and Daniel Samarov and Ichiro Takeuchi and A. Gilad Kusne and William Ratcliff},
journal= {arXiv preprint arXiv:2108.08918},
year = {2024}
}