Journal
JOURNAL OF SYNCHROTRON RADIATION
Volume 25, Issue -, Pages 655-670Publisher
INT UNION CRYSTALLOGRAPHY
DOI: 10.1107/S1600577518004873
Keywords
convolutional neural networks; deep learning; serial crystallography; X-ray free-electron laser; macromolecular structure
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Funding
- Office of Science, US Department of Energy (DOE) [DE-AC02-05CH11231]
- DOE, Office of Science, Office of Basic Energy Sciences [DE-AC02-76SF00515]
- DOE Office of Science [DE-AC02-05CH11231]
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A new tool is introduced for screening macromolecular X-ray crystallography diffraction images produced at an X-ray free-electron laser light source. Based on a data-driven deep learning approach, the proposed tool executes a convolutional neural network to detect Bragg spots. Automatic image processing algorithms described can enable the classification of large data sets, acquired under realistic conditions consisting of noisy data with experimental artifacts. Outcomes are compared for different data regimes, including samples from multiple instruments and differing amounts of training data for neural network optimization.
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