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.

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annotated images crystal recognition highdensity screening machine recognition of crystallization outcomes machine learning algorithms macromolecular crystallization experiments

Andrew E Bruno,Patrick Charbonneau,Janet Newman,Edward H Snell,David R So,Vincent Vanhoucke,Christopher J Watkins,Shawn Williams,Julie Wilson,.Classification of crystallization outcomes using deep convolutional neural networks.. 13 (6),.

Andrew E Bruno,Patrick Charbonneau,Janet Newman,Edward H Snell,David R So,Vincent Vanhoucke,Christopher J Watkins,Shawn Williams,Julie Wilson,"Classification of crystallization outcomes using deep convolutional neural networks." 13

Andrew E Bruno,Patrick Charbonneau,Janet Newman,Edward H Snell,David R So,Vincent Vanhoucke,Christopher J Watkins,Shawn Williams,Julie Wilson,"Classification of crystallization outcomes using deep convolutional neural networks."