Putting together a jigsaw puzzle is a great activity for a rainy Sunday afternoon. But the somewhat more difficult process of quickly assembling 3D scientific jigsaw puzzles — atomic structures of different materials — has recently gotten a lot easier, thanks to new research that pairs high-powered X-ray beams with advanced computing methodologies.
Researchers at the U.S. Department of Energy’s (DOE) Argonne National Laboratory have developed a new technique that accelerates the solving of material structures from patterns uncovered in X-ray experiments. The technique allows researchers to study certain properties, such as corrosion or battery charging and discharging, in real time.
The technique, called AutoPhaseNN, is based on a method called machine learning, which trains an algorithm on certain experimental data and then uses it to choose the most likely outcome of the current experiment. The data used in this case are created by shining ultrabright X-ray beams from Argonne’s Advanced Photon Source (APS) on a material and capturing the light as they bounce off, a process called diffraction. The APS is a DOE Office of Science user facility at Argonne.