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

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.

References in corpus (11)

• Scikit-learn: Machine Learning in Python
• Mantid - Data Analysis and Visualization Package for Neutron Scattering and $\mu SR$ Experiments
• On-the-fly Closed-loop Autonomous Materials Discovery via Bayesian Active Learning
• Optimization of Molecules via Deep Reinforcement Learning
• Autonomous discovery in the chemical sciences part I: Progress
• Magnetic-crystallographic phase diagram of superconducting parent compound Fe${}_{1+x}$Te
• Temperature-dependent properties of the magnetic order in single-crystal BiFeO3
• ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations
• The magnetic and structural properties near the Lifshitz point in Fe1+xTe
• Harnessing Interpretable and Unsupervised Machine Learning to Address Big Data from Modern X-ray Diffraction
• Exact and approximate analytical solutions of Weiss equation of ferromagnetism and their experimental relevance

Cited by in corpus (2)

• Bayesian optimization with experimental failure for high-throughput materials growth
• Active learning-assisted neutron spectroscopy with log-Gaussian processes