Bridging Theory and Fusion Experiments through Physics-Informed Deep Learning
Neural networks guided by physics are creating new ways to observe the complexities of plasmas.
Neural networks guided by physics are creating new ways to observe the complexities of plasmas.
Quantum simulations reveal the presence of entanglement among the quarks produced in high energy collisions.
Scientists investigate neutrinoless double beta decay through neutrino mass and the nuclear structure of germanium-76.
Ligand design and electrochemical studies pave a new path toward stable high-valent mid-actinide complexes.
A newly discovered excited state in radioactive sodium-32 has an unusually long lifetime, and its shape dynamics could be the cause.
Measurements of the nuclear structure of cesium-136 open a new channel for measurements of astrophysical neutrinos and searches for dark matter.
Machine learning and artificial intelligence accelerate nanomaterials investigations.
A new microscopy technique measures atomic-level distortions, twist angles, and interlayer spacing in graphene.
Department of Energy user facility helps probe questions from changes in the structure of nuclei to nuclear reactions that shape the Universe.
A new system for detecting photons in laser-powered quantum computers brings these systems closer to reality.
Researchers examine the structure of the low-energy nuclear states of carbon-12 using nuclear lattice effective field theory.
Simulations of binary neutron star mergers suggest that future detectors will distinguish between different models of hot nuclear matter.