Scientists at Argonne National Laboratory have developed a new AI method that creates unique “fingerprints” for materials using X-ray photon correlation spectroscopy (XPCS) and machine learning. This technique maps patterns from XPCS data, helping researchers understand how materials evolve when stressed or relaxed. The AI, specifically an autoencoder neural network, processes complex X-ray scattering patterns, identifying trends without expert training. This innovation, part of the AI-NERD project, enhances the study of material dynamics, with potential for significant impact when the upgraded Advanced Photon Source becomes operational.
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