  Serial crystallography is the field of science that studies the structure and
properties of crystals via diffraction patterns. In this paper, we introduce a
new serial crystallography dataset comprised of real and synthetic images; the
synthetic images are generated through the use of a simulator that is both
scalable and accurate. The resulting dataset is called DiffraNet, and it is
composed of 25,457 512x512 grayscale labeled images. We explore several
computer vision approaches for classification on DiffraNet such as standard
feature extraction algorithms associated with Random Forests and Support Vector
Machines but also an end-to-end CNN topology dubbed DeepFreak tailored to work
on this new dataset. All implementations are publicly available and have been
fine-tuned using off-the-shelf AutoML optimization tools for a fair comparison.
Our best model achieves 98.5% accuracy on synthetic images and 94.51% accuracy
on real images. We believe that the DiffraNet dataset and its classification
methods will have in the long term a positive impact in accelerating
discoveries in many disciplines, including chemistry, geology, biology,
materials science, metallurgy, and physics.
