Advances in machine learning and computational imaging are transforming not only how scientists analyze data but how they conduct entire experiments. At U.S. Department of Energy (DOE) user facilities, researchers are integrating machine learning directly into experimental workflows—using it to identify what to measure, reconstruct images as data are collected and rapidly extract scientific information.

SYNAPS-I (Synergistic Neutron and Photon Science—Intelligence), a seed project under the ModCon initiative within DOE’s Genesis Mission, is bringing these capabilities together across X-ray light source and neutron facilities. Led by DOE’s Lawrence Berkeley National Laboratory’s Advanced Light Source, the multilaboratory collaboration includes DOE’s Argonne National Laboratory, DOE’s Oak Ridge National Laboratory, DOE’s SLAC National Accelerator Laboratory and DOE’s Brookhaven National Laboratory.

The project combines expertise across the laboratories with open-source computational intelligence models, industry partnerships and advanced computing resources to develop shared solutions for faster, more efficient imaging.