Scientists have developed a new artificial intelligence method to create unique "fingerprints" for materials, allowing them to better understand how they change over time when stressed and relaxed. The technique combines X-ray photon correlation spectroscopy (XPCS) with an unsupervised machine learning algorithm, which teaches itself to recognize patterns in the scattered X-rays. This approach enables researchers to identify trends and repeating patterns that were previously inaccessible.
The study, led by Argonne National Laboratory's James "Jay" Horwath, used a neural network to analyze X-ray scattering data and create fingerprints for different materials. These fingerprints can be thought of as a material's "genome," containing essential information about the sample. The researchers created a map of these fingerprints, clustering similar characteristics together to better understand how materials are structured and evolve over time.
The AI-NERD model, developed by Horwath and his team, has significant implications for understanding material dynamics, particularly with the upcoming upgrade to the Advanced Photon Source (APS) at Argonne National Laboratory. The improved facility will generate 500 times brighter X-ray beams, making AI essential for sorting through the resulting data.
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