July 28, 2021 - Scientists from the United States Department of Energy’s (DOE) Argonne National Laboratory are using deep learning and artificial intelligence strategies to upgrade the current Advanced Photon Source (APS) and visualize X-ray data in three dimensions.

Researchers have developed a new computational framework called 3D-CDI-NN. The framework has demonstrated it can create 3D visualizations from data collected at the APS significantly faster than traditional methods.

Coherent diffraction imaging (CDI) is an X-ray technique that bounces ultra-bright X-ray beams off samples. The beams of light then are collected by detectors as data and are turned into images. According to Mathew Cherukara, leader of the Computational X-ray Science group in Argonne’s X-ray Science Division (XSD), the current detectors only capture some of the beam’s information.

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Scientists rely on computers to fill in missing data. However, the process can take a significant amount of time. The solution, according to Cherukara, is to train artificial intelligence to recognize objects and the changes they undergo directly from raw data, without having to account for missing information.

The team trained the neural network with simulated X-ray data. The neural network is a series of algorithms designed to teach computers to predict outcomes based on the data it receives.

“We used computer simulations to create crystals of different shapes and sizes, and we converted them into images and diffraction patterns for the neural network to learn,” lead author Henry Chan said in a press release. “The ease of quickly generating many realistic crystals for training is the benefit of simulations.”

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