Revolutionary AI Method Creates Precise Material “Fingerprints”

The AI-NERD model learns to produce a unique fingerprint for each sample of XPCS data. Mapping fingerprints from a large experimental dataset enables the identification of trends and repeating patterns which aids our understanding of how materials evolve. Credit: Argonne National LaboratoryThis method generates detailed “fingerprints” of materials, which are interpreted by AI to reveal new information about material dynamics.

In a new study by researchers in the Advanced Photon Source and Center for Nanoscale Materials at the U.S. Department of Energy’s Argonne National Laboratory, scientists have paired XPCS with an unsupervised machine learning algorithm, a form of neural network that requires no expert training. The algorithm teaches itself to recognize patterns hidden within arrangements of X-rays scattered by a colloid — a group of particles suspended in solution.

These patterns are too complicated for scientists to detect without the aid of AI. ​“As we’re shining the, the patterns are so diverse and so complicated that it becomes difficult even for experts to understand what any of them mean,” Horwath said. The goal of the researchers was to try to create a map of the material’s fingerprints, clustering together fingerprints with similar characteristics into neighborhoods. By looking holistically at the features of the various fingerprint neighborhoods on the map, the researchers were able to better understand how the materials were structured and how they evolved over time as they were stressed and relaxed.

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