Classification of crystallization outcomes using deep convolutional neural networks

Julie Carol Wilson, A Bruno, P Charbonneau, J Newman, E Snell, D So, V Vanhoucke, C Watkins, S Williams

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Article numbere0198883
Number of pages15
JournalPLOS one
Issue number6
Publication statusPublished - 20 Jun 2018


  • Algorithms
  • Crystallization
  • Crystallography, X-Ray
  • Datasets as Topic
  • Image Processing, Computer-Assisted
  • Neural Networks (Computer)

Cite this