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Autonomous x-ray scattering.

Kevin G Yager1, Pawel W Majewski2, Marcus M Noack3

  • 1Center for Functional Nanomaterials, Brookhaven National Laboratory, Upton, New York NY-11973, United States of America.

Nanotechnology
|May 4, 2023
PubMed
Summary
This summary is machine-generated.

Autonomous experimentation (AE) automates scientific workflows, including decision-making, at synchrotron x-ray scattering beamlines. This approach enhances efficiency and accelerates the discovery of new materials.

Keywords:
AI/MLautonomous experimentationcharacterizationmachine learningmaterial discoveryx-ray scattering

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Area of Science:

  • Materials Science
  • Physics
  • Chemistry

Background:

  • Autonomous experimentation (AE) is an emerging paradigm.
  • AE automates the entire experimental workflow, including decision-making.
  • AE aims to free scientists for more complex challenges.

Purpose of the Study:

  • To describe progress in applying AE to synchrotron x-ray scattering beamlines.
  • To demonstrate the integration of measurement, data analysis, and decision-making into an autonomous loop.
  • To showcase AE's potential for enhancing efficiency and discovering new materials.

Main Methods:

  • Automating measurement instruments, data analysis, and decision-making.
  • Coupling these components into an autonomous experimental loop.
  • Utilizing Gaussian process modeling for surrogate models and uncertainty quantification.
  • Defining an objective function based on the surrogate model and uncertainty.

Main Results:

  • Successful application of AE to x-ray scattering experiments.
  • Demonstrated AE for sample imaging.
  • Showcased AE for exploring physical spaces via combinatorial methods.
  • Integrated AE with *in situ* processing platforms.

Conclusions:

  • AE significantly enhances efficiency in x-ray scattering experiments.
  • AE facilitates the discovery of novel materials.
  • AE empowers scientists to address more complex scientific problems.