Author: Aurelius, O.
Paper Title Page
Automated ML-based Sample Centering for Macromolecular X-Ray Crystallography with MXAimbot  
  • I. Lindhé, O. Aurelius, M. Eguiraun, A. Gonzalez, E. Jagudin, G. Lima, Z. Matej, J. Nan, J. Schurmann
    MAX IV Laboratory, Lund University, Lund, Sweden
  • J.W. Janneck
    Lund Institute of Technology (LTH), Lund University, Lund, Sweden
  MXAimbot is a neural network based tool, designed to automate the task of centering samples for macro-molecular X-ray crystallography experiments before exposing the sample to the beam. MXAimbot uses a convolutional neural network (CNN) trained on a few thousands images from an industrial vision camera pointed at the sample to predict suitable crystal centering for subsequent X-ray data collection. The motivation for this project is that the machine vision automated sample positioning allows X-ray laboratories and synchrotron beamlines to offer a more efficient alternative for the manual centering, which is time consuming and difficult to automate with conventional image analysis, and for the X-ray mesh scan centering, which can introduce radiation damage to the crystal. MXAimbot can be used to improve results of standard LUCID loop centering for fully automated data collection in fragment-screening campaigns. No need for sample rotation should be an additional advantage.  
slides icon Slides FRBR06 [12.433 MB]  
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