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On-the-fly autonomous control of neutron diffraction via physics-informed Bayesian active learning

Journal Article · · Applied Physics Reviews
DOI:https://doi.org/10.1063/5.0082956· OSTI ID:2564520
 [1];  [2];  [2];  [2];  [3];  [4];  [5];  [6];  [7];  [8];  [9]
  1. Material Measurement Laboratory, NIST, Gaithersburg, Maryland 20899, USA
  2. Neutron Sciences Directorate, ORNL, Oak Ridge, Tennessee 37831, USA
  3. Department of Chemistry and Biochemistry, University of Maryland, College Park, Maryland 20742, USA, Maryland Quantum Materials Center, College Park, Maryland 20742, USA
  4. Department of Computer Science, Cornell University, Ithaca, New York 14850, USA
  5. Department of Computer Science, Rice University, Houston, Texas 77005, USA
  6. Information Technology Laboratory, NIST, Gaithersburg, Maryland 20899, USA
  7. Maryland Quantum Materials Center, College Park, Maryland 20742, USA, Department of Materials Science and Engineering, University of Maryland, College Park, Maryland 20742, USA
  8. Department of Materials Science and Engineering, University of Maryland, College Park, Maryland 20742, USA, NIST Center for Neutron Research, NIST, Gaithersburg, Maryland 20899, USA
  9. Material Measurement Laboratory, NIST, Gaithersburg, Maryland 20899, USA, Department of Materials Science and Engineering, University of Maryland, College Park, Maryland 20742, USA
+ Show Author Affiliations

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.

Research Organization:
National Inst. of Standards and Technology (NIST), Gaithersburg, MD (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Organization:
National Institute of Standards and Technology; National Science Foundation (NSF); USDOE; USDOE Office of Science (SC)
Grant/Contract Number:
AC05-00OR22725; SC0016434
OSTI ID:
2564520
Alternate ID(s):
OSTI ID: 1865963
OSTI ID: 1866666
Journal Information:
Applied Physics Reviews, Journal Name: Applied Physics Reviews Journal Issue: 2 Vol. 9; ISSN 1931-9401
Publisher:
American Institute of PhysicsCopyright Statement
Country of Publication:
United States
Language:
English

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Related Subjects

36 MATERIALS SCIENCE
Bayesian inference
machine learning
magnetic ordering
neutron scattering
phase transitions
statistical analysis
statistical mechanics models