Study shows how materials change as they are stressed and relaxed.
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.
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.
In a new study by researchers in the Advanced Photon Source (APS) and Center for Nanoscale Materials (CNM) at the U.S. Department of Energy's (DOE) 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. The APS and CNM are DOE Office of Science user facilities.
"The goal of the AI is just to treat the scattering patterns as regular images or pictures and digest them to figure out what are the repeating patterns. The AI is a pattern recognition expert." — James (Jay) Horwath, Argonne National Laboratory
"The way we understand how materials move and change over time is by collecting X-ray scattering data," said Argonne postdoctoral researcher James (Jay) Horwath, the first author of the study.
These patterns are too complicated for scientists to detect without the aid of AI. "As we're shining the X-ray beam
