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Machine-and-data intelligence for synchrotron science

Nature Reviews Physics volume 3, pages 766–768 (2021)Cite this article

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Integrated approaches with advanced machine learning techniques are becoming necessary to take full advantage of the advanced experimental capabilities of next-generation synchrotrons. Yijin Liu and colleagues discuss the emergence of synergistic machine-and-data intelligence in synchrotron technology, and how it may accelerate scientific discovery.

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Fig. 1: Overview of the application of machine learning in synchrotron technology and science.

References

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Acknowledgements

SLAC National Accelerator Laboratory is operated by Stanford University for the US Department of Energy’s (DOE) Office of Science. Stanford Synchrotron Radiation Lightsource is a DOE Office of Science user facility.

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Authors and Affiliations

  1. Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, Menlo Park, CA, USA

    Jizhou Li, Xiaobiao Huang, Piero Pianetta & Yijin Liu

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  1. Jizhou Li
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  2. Xiaobiao Huang
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  3. Piero Pianetta
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  4. Yijin Liu
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Correspondence to Yijin Liu.

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Li, J., Huang, X., Pianetta, P. et al. Machine-and-data intelligence for synchrotron science. Nat Rev Phys 3, 766–768 (2021). https://doi.org/10.1038/s42254-021-00397-0

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