(WSJ) A team at the US Department of Energy’s (DOE’s) Oak Ridge National Laboratory (ORNL) is using artificial intelligence (AI) to find patterns in neutron scattering data that can lead to an understanding of the physics inside quantum or complex magnetic materials. Led by Alan Tennant, Initiative Lead for Quantum Materials at ORNL, the team recently trained an artificial neural network (ANN) to successfully interpret data from a neutron scattering experiment performed at ORNL’s Spallation Neutron Source (SNS). The team trained the network by feeding it data from neutron scattering simulations performed on systems at the Oak Ridge Leadership Computing Facility (OLCF), including the center’s decommissioned Cray XK7 Titan. One of the most powerful machines of its time, Titan continues to supply the scientific community with new discoveries even after its retirement last fall.
“Before, when you would do an experiment, you weren’t entirely sure you had the right result,” Tennant said. “With this neural network, we can be confident in the answer due to the extensive training that the network had to go through. Of all the possible cases it encounters, it can find the optimal solution.”
The network can reveal new information about current neutron scattering experiments and even provide insight into which experiments would be most beneficial to run in the future.
ORNL Scientists Tap into AI to Find Patterns in Neutron Scattering Data to Better Understand Quantum Materials
