Artificial intelligence deciphers detector “clouds” to accelerate materials research

A machine learning algorithm automatically extracts information to speed up – and extend – the study of materials with X-ray pulse pairs.

X-rays can be used like a superfast, atomic-resolution camera, and if researchers shoot a pair of X-ray pulses just moments apart, they get atomic-resolution snapshots of a system at two points in time. Comparing these snapshots shows how a material fluctuates within a tiny fraction of a second, which could help scientists design future generations of super-fast computers, communications, and other technologies.

Resolving the information in these X-ray snapshots, however, is difficult and time intensive, so Joshua Turner, a lead scientist at the Department of Energy’s SLAC National Accelerator Center and Stanford University, and ten other researchers turned to artificial intelligence to automate the process. Their machine learning-aided method, published October 17 in Structural Dynamics, accelerates this X-ray probing technique, and extends it to previously inaccessible materials.

“The most exciting thing to me is that we can now access a different range of measurements, which we couldn’t before,” Turner said.

Handling the blob

When studying materials using this two-pulse technique, the X-rays scatter off a material and are usually detected one photon at a time. A detector measures these scattered photons, which are used to produce a speckle pattern – a blotchy image that represents the precise configuration of the sample at one instant in time. Researchers compare the speckle patterns from each pair of pulses to calculate fluctuations in the sample.

“However, every photon creates an explosion of electrical charge on the detector,” Turner said. “If there are too many photons, these charge clouds merge together to create an unrecognizable blob.” This cloud of noise means the researchers must collect tons of scattering data to yield a clear understanding of the speckle pattern.

“You need a lot of data to work out what’s happening in the system,” said Sathya Chitturi, a Ph.D. student at Stanford University who led this work. He is advised by Turner and coauthor Mike Dunne, director of the Linac Coherent Light Source (LCLS) X-ray laser at SLAC. 

Read more on the SLAC website

Image: A speckle pattern typical of the sort seen at LCLS’s detectors

Credit: Courtesy Joshua Turner