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Found 5 papers, 1 papers with code
Augmenting x-ray single particle imaging reconstruction with self-supervised machine learning
The development of X-ray Free Electron Lasers (XFELs) has opened numerous opportunities to probe atomic structure and ultrafast dynamics of various materials.
Machine learning enabled experimental design and parameter estimation for ultrafast spin dynamics
Advanced experimental measurements are crucial for driving theoretical developments and unveiling novel phenomena in condensed matter and material physics, which often suffer from the scarcity of facility resources and increasing complexities.
PeakNet: An Autonomous Bragg Peak Finder with Deep Neural Networks
PeakNet is well-suited for expert-level real-time serial crystallography data analysis at high data rates.
SpeckleNN: A unified embedding for real-time speckle pattern classification in X-ray single-particle imaging with limited labeled examples
With X-ray free-electron lasers (XFELs), it is possible to determine the three-dimensional structure of noncrystalline nanoscale particles using X-ray single-particle imaging (SPI) techniques at room temperature.
Scaling and Acceleration of Three-dimensional Structure Determination for Single-Particle Imaging Experiments with SpiniFEL
We present SpiniFEL, an application used for structure determination of proteins from single-particle imaging (SPI) experiments.
