A team of scientists has leveraged artificial intelligence to train computers to keep up with the massive amounts of X-ray data taken at the Advanced Photon Source.

That’s the challenge a group of scientists from the U.S. Department of Energy’s (DOE) Argonne National Laboratory is working to overcome. Artificial intelligence has emerged as a versatile solution to the issues posed by big data processing. For scientists who use the Advanced Photon Source (APS), a DOE Office of Science User Facility at Argonne, to process 3D images, it may be the key to turning X-ray data into visible, understandable shapes at a much faster rate. A breakthrough in this area could have implications for astronomy, electron microscopy and other areas of science dependent on large amounts of 3D data.

The research team, which includes scientists from three Argonne divisions, has developed a new computational framework called 3D-CDI-NN, and has shown that it can create 3D visualizations from data collected at the APS hundreds of times faster than traditional methods can. The team’s research was published in Applied Physics Reviews, a publication of the American Institute of Physics.

CDI stands for coherent diffraction imaging, an X-ray technique that involves bouncing ultra-bright X-ray beams off of samples. Those beams of light will then be collected by detectors as data, and it takes some computational effort to turn that data into images. Part of the challenge, explains Mathew Cherukara, leader of the Computational X-ray Science group in Argonne’s X-ray Science Division (XSD), is that the detectors only capture some of the information from the beams.

But there is important information contained in the missing data, and scientists rely on computers to fill in that information. As Cherukara notes, while this takes some time to do in 2D, it takes even longer to do with 3D images. The solution, then, is to train an artificial intelligence to recognize objects and the microscopic changes they undergo directly from the raw data, without having to fill in the missing info.

To do this, the team started with simulated X-ray data to train the neural network. The NN in the framework’s title, a neural network is a series of algorithms that can teach a computer to predict outcomes based on data it receives. Henry Chan, the lead author on the paper and a postdoctoral researcher in the Center for Nanoscale Materials (CNM), a DOE Office of Science User Facility at Argonne, led this part of the work.

Story Source: Materials provided by DOE/Argonne National Laboratory. Original written by Andre Salles. Note: Content may be edited for style and length.