Autonomous Neutron Diffraction Experiments with ANDiE
Category
Published on
Abstract
Active learning is a powerful tool that can be used to accelerate experimental research. In this tutorial we will cover how Bayesian Statistics can autonomously guide neutron diffraction measurements, accelerating the research by a factor of 5 compared to traditional methods. Neutron diffraction is one of the only measurement techniques that can directly study the magnetic order in a material. The magnetic transition temperatures and their transition dynamics are important material characteristics for use in many applications including: high-performance electric motors, high-frequency transformers, and solid-state refrigeration. However, determining the magnetic transition characteristics traditionally requires considerable neutron beamtime at already over-subscribed facilities. We developed ANDiE, the autonomous neutron diffraction explorer, that can analyze the diffraction measurements on-the-fly and autonomously decide on the best experiment to perform next. This tutorial will cover the working principles of ANDiE, how physics was encoded into the design, and demonstrate how ANDiE can be used to autonomously control neutron diffraction experiments.
The tool ANDiE can be ran on nanoHUB as Autonomous Neutron Diffraction Explorer.
Bio
Dr. Austin McDannald received his Ph.D in Materials Science and Engineering from the University of Connecticut in 2016. He worked as a Staff Scientist for II-VI M Cubed Technologies Inc. from 2016 to 2020. He joined the National Institute of Standards and Technology in 2020 as a Materials Research Engineer in the Materials Measurement Laboratory. His primary research interests are in encoding materials physics into machine learning and AI tools to enable autonomous experimental materials research.
Sponsored by
Cite this work
Researchers should cite this work as follows: