On-the-fly autonomous control of neutron diffraction via physics-informed Bayesian active learning
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 Fe 1.09 Te 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.
- Authors:
-
[1];
[2];
[2];
[2];
[6];
[7]
- National Inst. of Standards and Technology (NIST), Gaithersburg, MD (United States)
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Univ. of Maryland, College Park, MD (United States); Maryland Quantum Materials Center, College Park, MD (United States)
- Cornell Univ., Ithaca, NY (United States)
- Rice Univ., Houston, TX (United States)
- Univ. of Maryland, College Park, MD (United States); National Inst. of Standards and Technology (NIST), Gaithersburg, MD (United States)
- National Inst. of Standards and Technology (NIST), Gaithersburg, MD (United States); Univ. of Maryland, College Park, MD (United States)
- Publication Date:
- Research Org.:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); National Inst. of Standards and Technology (NIST), Gaithersburg, MD (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC); National Institute of Standards and Technology; National Science Foundation (NSF)
- OSTI Identifier:
- 1866666
- Alternate Identifier(s):
- OSTI ID: 1865963
- Grant/Contract Number:
- AC05-00OR22725; SC0016434; DMR-2010792; 70NANB17H301
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Applied Physics Reviews
- Additional Journal Information:
- Journal Volume: 9; Journal Issue: 2; Journal ID: ISSN 1931-9401
- Publisher:
- American Institute of Physics (AIP)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 36 MATERIALS SCIENCE; statistical analysis; statistical mechanics models; Bayesian inference; neutron scattering; machine learning; phase transitions; magnetic ordering
Citation Formats
McDannald, Austin, Frontzek, Matthias, Savici, Andrei T., Doucet, Mathieu, Rodriguez, Efrain E., Meuse, Kate, Opsahl-Ong, Jessica, Samarov, Daniel, Takeuchi, Ichiro, Ratcliff, William, and Kusne, A. Gilad. On-the-fly autonomous control of neutron diffraction via physics-informed Bayesian active learning. United States: N. p., 2022.
Web. doi:10.1063/5.0082956.
McDannald, Austin, Frontzek, Matthias, Savici, Andrei T., Doucet, Mathieu, Rodriguez, Efrain E., Meuse, Kate, Opsahl-Ong, Jessica, Samarov, Daniel, Takeuchi, Ichiro, Ratcliff, William, & Kusne, A. Gilad. On-the-fly autonomous control of neutron diffraction via physics-informed Bayesian active learning. United States. https://doi.org/10.1063/5.0082956
McDannald, Austin, Frontzek, Matthias, Savici, Andrei T., Doucet, Mathieu, Rodriguez, Efrain E., Meuse, Kate, Opsahl-Ong, Jessica, Samarov, Daniel, Takeuchi, Ichiro, Ratcliff, William, and Kusne, A. Gilad. Fri .
"On-the-fly autonomous control of neutron diffraction via physics-informed Bayesian active learning". United States. https://doi.org/10.1063/5.0082956. https://www.osti.gov/servlets/purl/1866666.
@article{osti_1866666,
title = {On-the-fly autonomous control of neutron diffraction via physics-informed Bayesian active learning},
author = {McDannald, Austin and Frontzek, Matthias and Savici, Andrei T. and Doucet, Mathieu and Rodriguez, Efrain E. and Meuse, Kate and Opsahl-Ong, Jessica and Samarov, Daniel and Takeuchi, Ichiro and Ratcliff, William and Kusne, A. Gilad},
abstractNote = {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 Fe 1.09 Te 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.},
doi = {10.1063/5.0082956},
journal = {Applied Physics Reviews},
number = 2,
volume = 9,
place = {United States},
year = {Fri Apr 29 00:00:00 EDT 2022},
month = {Fri Apr 29 00:00:00 EDT 2022}
}
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