Classification of crystallization outcomes using deep convolutional neural networks
This work addresses crystal recognition for high-density screening in industrial and fundamental research applications, but it is incremental as it applies existing methods to new data.
The researchers tackled the problem of classifying crystallization outcomes from images using deep convolutional neural networks, achieving over 94% accuracy on test images across various experimental setups.
The Machine Recognition of Crystallization Outcomes (MARCO) initiative has assembled roughly half a million annotated images of macromolecular crystallization experiments from various sources and setups. Here, state-of-the-art machine learning algorithms are trained and tested on different parts of this data set. We find that more than 94% of the test images can be correctly labeled, irrespective of their experimental origin. Because crystal recognition is key to high-density screening and the systematic analysis of crystallization experiments, this approach opens the door to both industrial and fundamental research applications.