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This process is not only error-prone but also has the effect of slowing down the overall discovery process. However, no automated system currently exists to provide real-time analysis of the diffraction images produced, so scientists that have been specifically trained to understand these diffraction patterns have to screen the images manually. Recent technological advances have automated crystallography experiments, allowing researchers to generate diffraction images at unprecedented speeds. The diffraction image classification pipeline. Serial Crystallography, in turn, refers to a more recent crystallography technique for investigating properties from microcrystals using X-ray free-electron laser. This diffraction pattern is then used to analyze the crystal’s structure. In crystallography, a crystal is irradiated with an X-ray beam that strikes the crystal and produces an image with a diffraction pattern (Figure 1, see this video for more information on crystal diffraction). It has been a central tool in many disciplines, including chemistry, geology, biology, materials science, metallurgy, and physics, and has led to substantial advances in, for instance, drugs development for fighting diseases. Oliveira, Luigi Nardi Ī detailed description can be found in this paper.Ĭrystallography is the science that studies the properties of crystals. DeepFreak: Learning Crystallography Diffraction Patterns with Automated Machine Learning by Artur Souza, Leonardo B.