Classification of crystallization outcomes using deep convolutional neural networks.
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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.
SubjectScience & Technology
Science & Technology - Other Topics
Published Version (Please cite this version)10.1371/journal.pone.0198883
Publication InfoBruno, Andrew E; Charbonneau, Patrick; Newman, Janet; Snell, Edward H; So, David R; Vanhoucke, Vincent; ... Wilson, Julie (2018). Classification of crystallization outcomes using deep convolutional neural networks. PloS one, 13(6). pp. e0198883. 10.1371/journal.pone.0198883. Retrieved from https://hdl.handle.net/10161/17392.
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Professor of Chemistry
Professor Charbonneau studies soft matter. His work combines theory and simulation to understand the glass problem, protein crystallization, microphase formation, and colloidal assembly in external fields.