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Related Experiment Video

Updated: May 30, 2025

Neutron Radiography and Computed Tomography of Biological Systems at the Oak Ridge National Laboratory's High Flux Isotope Reactor

Neutron Radiography and Computed Tomography of Biological Systems at the Oak Ridge National Laboratory's High Flux Isotope Reactor

Published on: May 7, 2021

AutoRefl: active learning in neutron reflectometry for fast data acquisition.

David P Hoogerheide¹, Frank Heinrich^1,2

¹NIST Center for Neutron Research, National Institute of Standards and Technology, Gaithersburg, Maryland, USA.

Journal of Applied Crystallography

|January 28, 2025

Summary

This summary is machine-generated.

AutoRefl, an active learning algorithm, significantly speeds up neutron reflectometry (NR) measurements by intelligently selecting optimal measurement parameters. This enhances the efficiency of analyzing thin film structures using NR.

Keywords:

active learning forecasting information machine learning neutron reflectometry

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Last Updated: May 30, 2025

Neutron Radiography and Computed Tomography of Biological Systems at the Oak Ridge National Laboratory's High Flux Isotope Reactor

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Published on: May 7, 2021

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Published on: April 28, 2022

Neutron Spin Echo Spectroscopy as a Unique Probe for Lipid Membrane Dynamics and Membrane-Protein Interactions

Neutron Spin Echo Spectroscopy as a Unique Probe for Lipid Membrane Dynamics and Membrane-Protein Interactions

Published on: May 27, 2021

Area of Science:

Materials Science
Surface Science
Analytical Chemistry

Background:

Neutron reflectometry (NR) is crucial for characterizing thin film and interface structures.
Traditional NR measurements are time-consuming and limited by instrument availability, necessitating efficiency improvements.

Purpose of the Study:

To introduce AutoRefl, a model-based active learning (AL) algorithm designed to enhance neutron reflectometry measurement efficiency.
To optimize the selection of measurement positions and durations for faster data acquisition.

Main Methods:

Developed AutoRefl, an algorithm employing active learning to guide neutron reflectometry measurements.
AutoRefl selects subsequent measurement configurations based on information gained from existing data.
Incorporated forecasting for continuous measurement optimization and utilized signal-to-noise ratios for duration selection.

Main Results:

AutoRefl demonstrated significant improvements in neutron reflectometry measurement speeds across various scenarios.
Performance was validated against established best practices using digital twins of monochromatic and polychromatic reflectometers.
The algorithm effectively maximizes information acquisition rates for specific model parameters.

Conclusions:

AutoRefl offers a substantial advancement in accelerating neutron reflectometry experiments.
This active learning approach enhances measurement efficiency, making NR more accessible and productive.
The algorithm's ability to forecast future measurements supports practical, continuous experimental workflows.