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Bioinformatic Machine Learning for X-Ray Crystallography NASA Center: Langley Research Center (June 2017)

NASA Langley Research Center
Hampton, Virginia
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In collaboration with Jefferson Lab, we are pursuing a machine-learning project to post-process the data from the SLAC LCLS femtosecond x-ray laser. Data from this laser can produce x-ray diffraction images with single pulses, thereby observing chemical reactions in progress, protein folding, and material structures, to a degree not previously possible. However, the image data is of a moderate resolution (12nm), and the molecules are observed in random spatial orientations, such that reconstruction of 3D coordinates is difficult with standard crystallography techniques. Some recent progress has been made in this area through the expand-maximize-compress (EMC) algorithm and electron density map reduction. The student will apply methods of machine learning to recognize the electron density of known structures, in order to probabilistically determine the structures of more complex molecules and biological specimens. Possible methods may include neural networks, generative adversarial networks, and Markov Chain Monte Carlo searches through quantum annealing or simulated annealing.

Students will have the opportunity to engage with engineers, researchers, computer scientists, and technologists to identify and quantify areas where  x-ray crystallography will have applications to NASA materials science research. A final report or poster presentation will be required.

The deadline to register is May 31, 2017. Please apply directly here:

Please contact Stephen Casey at NASA for any questions:

Stephen Casey,