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On-the-Fly Autonomous Control of Neutron Diffraction via Physics-Informed Bayesian Active Learning

Published

Author(s)

Austin McDannald, Matthias Frontzek, Andrew Savici, Mathieu Doucet, Efrain Rodriguez, Kate Meuse, Jessica Opsahl-Ong, Daniel Samarov, Ichiro Takeuchi, William Ratcliff, A. Gilad Kusne

Abstract

We demonstrate the first live, autonomous control over neutron diffraction experiments by developing and deploying ANDiE: the autonomous neutron diffraction explorer. Neutron scattering is a unique and versatile characterization technique for probing the magnetic structure and behavior 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 show that ANDiE can experimentally determine the magnetic ordering transition of both MnO and Fe1.09Te all while providing a fivefold enhancement in measurement efficiency. Furthermore, in a hypothesis testing post-processing step, ANDiE can determine transition behavior from a set of possible physical models. 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.
Citation
Applied Physics Reviews
Volume
9
Issue
2

Keywords

neutron, AI, autonomous

Citation

McDannald, A. , Frontzek, M. , Savici, A. , Doucet, M. , Rodriguez, E. , Meuse, K. , Opsahl-Ong, J. , Samarov, D. , Takeuchi, I. , Ratcliff, W. and Kusne, A. (2022), On-the-Fly Autonomous Control of Neutron Diffraction via Physics-Informed Bayesian Active Learning, Applied Physics Reviews, [online], https://doi.org/10.1063/5.0082956, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=932357 (Accessed February 11, 2025)

Issues

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Created June 1, 2022, Updated January 31, 2025