From face recognition to phase recognition: Neural network captures atomic-scale rearrangements

If you want to understand how a material changes from one atomic-level configuration to another, it's not enough to capture snapshots of before-and-after structures. It'd be better to track details of the transition as it happens. Same goes for studying catalysts, materials that speed up chemical reactions by bringing key ingredients together; the crucial action is often triggered by subtle atomic-scale shifts at intermediate stages.

"To understand the structure of these transitional states, we need tools to both measure and identify what happens during the transition," said Anatoly Frenkel, a physicist with a joint appointment at the U.S. Department of Energy's Brookhaven National Laboratory and Stony Brook University.

Frenkel and his collaborators have now developed such a "phase-recognition" tool—or more precisely, a way to extract "hidden" signatures of an unknown structure from measurements made by existing tools. In a paper just published in Physical Review Letters, they describe how they trained a neural network to recognize features in a material's X-ray absorption spectrum that are sensitive to the arrangement of atoms at a very fine scale. The method helped reveal details of the atomic-scale rearrangements iron undergoes during an important but poorly understood phase change.

"This network training is similar to how machine learning is used in facial-recognition technology," Frenkel explained. In that technology, computers analyze thousands of images of faces and learn to recognize key features, or descriptors, and the differences that tell individuals apart. "There is a correlation between some features of the data," Frenkel explained. "In the language of our X-ray data, the correlations exist between the intensity of different regions of the spectra that also have direct relevance to the underlying structure and the corresponding phase."