Physics Spotlight  
The image is an artist’s visualization of a nucleus as studied in numerical simulations, created using DeepArt neural network visualization software. Image courtesy of the Laboratory for Nuclear Science. The image is an artist’s visualization of a nucleus as studied in numerical simulations, created using DeepArt neural network visualization software.
Image courtesy of the Laboratory for Nuclear Science.

Project to elucidate the structure of atomic nuclei at the femtoscale

Laboratory for Nuclear Science project selected to explore machine learning for lattice quantum chromodynamics.

Scott Morley | Laboratory for Nuclear Science
July 6, 2018

The Argonne Leadership Computing Facility (ALCF), a U.S. Department of Energy (DOE) Office of Science User Facility, has selected 10 data science and machine learning projects for its Aurora Early Science Program (ESP). Set to be the nation’s first exascale system upon its expected 2021 arrival, Aurora will be capable of performing a quintillion calculations per second, making it 10 times more powerful than the fastest computer that currently exists.

The Aurora ESP, which commenced with 10 simulation-based projects in 2017, is designed to prepare key applications, libraries, and infrastructure for the architecture and scale of the exascale supercomputer. Researchers in the Laboratory for Nuclear Science’s Center for Theoretical Physics have been awarded funding for one of the projects under the ESP. Associate professor of physics William Detmold, assistant professor of physics Phiala Shanahan, and principal research scientist Andrew Pochinsky will use new techniques developed by the group, coupling novel machine learning approaches and state-of-the-art nuclear physics tools, to study the structure of nuclei.