AI-empowered X-ray and neutron studies in material science

Research

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  Machine-learning-enhanced automatic spectral characterization of x-ray pulses from a free-electron laser

  A reliable characterization of x-ray pulses is critical to optimally exploit advanced photon sources, such as free-electron lasers. The authors present a method based on machine learning which improves the resolution and signal-to-noise ratio of the non-invasive spectral diagnostics available at European XFEL, and streamlines its operation.

  • Danilo Enoque Ferreira de Lima
  • Arman Davtyan
  • Luca Gelisio

  ArticleOpen Access18 Dec 2024 Communications Physics
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  AI-NERD: Elucidation of relaxation dynamics beyond equilibrium through AI-informed X-ray photon correlation spectroscopy

  Editorial summary: Understanding non-equilibrium dynamics in materials is hindered by the difficulty of collecting and analyzing experimental data. Here, authors develop an machine learning framework for categorizing and tracking dynamics using time-resolved XPCS.

  • James P. Horwath
  • Xiao-Min Lin
  • Mathew J. Cherukara

  ArticleOpen Access15 Jul 2024 Nature Communications
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  Powder diffraction crystal structure determination using generative models

  Crystal structure determination from powder X-ray diffraction is challenging but vital for materials research. Here, authors develop PXRDGen, an AI system that automatically solves crystal structures with 96% accuracy across thousands of compounds.

  • Qi Li
  • Rui Jiao
  • Xiaolong Chen

  ArticleOpen Access11 Aug 2025 Nature Communications