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

TL;DR

This paper introduces ANDiE, an autonomous system that uses physics-informed Bayesian active learning to significantly reduce neutron diffraction experiment times and accurately identify magnetic transition properties.

Contribution

The paper presents a novel autonomous neutron diffraction explorer (ANDiE) that dynamically guides measurements, improving efficiency and accuracy in neutron scattering experiments.

Findings

Achieved 5-fold increase in efficiency for determining Neel temperature.

Successfully identified magnetic transition dynamics in MnO and Fe1.09Te.

Demonstrated broad applicability of active learning in neutron experiments.

Abstract

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.