Revolutionary AI Method Creates Precise Material “Fingerprints”

Researchers at the Argonne National Laboratory have developed a new technique using X-ray photon correlation spectroscopy and artificial intelligence to analyze materials.

This method generates detailed “fingerprints” of materials, which are interpreted by AI to reveal new information about material dynamics. The approach, known as AI-NERD, leverages unsupervised machine learning to recognize and cluster these fingerprints, enhancing understanding of material behavior under different conditions.

Like people, materials evolve over time. They also behave differently when they are stressed and relaxed. Scientists looking to measure the dynamics of how materials change have developed a new technique that leverages X-ray photon correlation spectroscopy (XPCS), artificial intelligence (AI), and machine learning.

Innovating Material Identification With AI
This technique creates ​“fingerprints” of different materials that can be read and analyzed by a neural network to yield new information that scientists previously could not access. A neural network is a computer model that makes decisions in a manner similar to the human brain.