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.
Contribution:
	- Using a known parameters crystallography simulator (X-ray beam, structure being analyzed, environment (crystalline or not)) built a dataset (called DiffraNet) of 25,000 512x512 grayscale labeled images of resulting diffraction images of various materials/structures (crystalline or not) .
	- carried various classification approaches of the dataset (labelled) images in two steps:
		- Feature extraction (Scale Invariant Feature Transform with the Bag-of-Visual-Words approach as local feature extractor, and the Gray-level Co-occurrence Matrix and Local Binary Patterns as global feature extractor) then
		- Classification of the diffraction images is carried with three approaches. Two using images described by extracted features (from the previous feature extraction step) coupled with either random Forests or Support Vector Machines and a third consisting in a Convolution Neural Network (CNN) topology named DeepFreak.
		- The images are classified according to the diffraction patterns they encompass into one of 5 classes: blank, no-crystal, weak, good and strong. The last three describing presence of a crystalline structure.
		- A fine tuning step of the various algorithms was carried using AutoML optimization tools.
All algorithms were off the shelf publicly available implementations and have previously been used for such domain applications (crystallography patterns).
The approach and choices of classification algorithms is well articulated and results interesting.
A few questions though:
•	In what way the diffraction images are ‘synthetic’? Aren’t they actual diffraction images but in a controlled known and controlled setting: set of parameters (beam, structure to analyze)?
o	More like a library of diffraction pattern images for various materials/structures.
•	How many structures were analyzed (Were there 25000 for the 25000 pattern images), one image each?
o	This is to understand  the representability of the samples (structures) analyzed regarding the possible structures (Hundreds of thousands as per paper’s 2.1 ) .
•	What variations for each of the setting variabilities (X Ray beam(flux, beam size, divergence, and bandpass), crystal properties (unit cell, number of cells, and a structure factor table), and the experimental parameters (sources of background noise, detector point-spread, and shadows)) were used? 
o	This is to assess the size of the pattern space.
•	Were any real-life setting obtained pattern samples classified using DiffraNet dataset patterns’ fine-tuned classification algorithms?
o	This is to assess the generalization level of the DiffraNet dataset patterns’ fine-tuned classification algorithms to real-life obtained patterns (relates to the previously stated representability of the samples).
o	If not, your statement “ … we plan to add new images and new classes that are common place in serial crystallography” (in 6. Conclusions) would be an appreciated validation of general usability of your DiffraNet fine–tuned setting.
•	Were all the structures analyzed crystalline? 
o	It’s stated in Figure 2 and Table 6 that 2 classes are either blank or no-crystal but is that a known fact (purposely chosen) or no pattern images for crystalline structures due to inadequate experimental settings to uncover the crystalline nature of the analyzed structure?
•	Were the pattern images pre-processed in any manner before being classified?
Nota: In table 6, use no-crystal class as in Figure 2 for consistency.