PNNL researchers are using explainable artificial intelligence to develop new ways to understand and interpret data for national security.

PNNL's Brenda Wiesner creates one-of-a-kind artwork using peeling paint and rusty scraps from decommissioned RPM shells.

Explainable artificial intelligence—understanding and explaining the reasoning behind AI decisions—is a necessity for national security.

Explainable AI helps scientists understand and untangle the threads of information that the system uses to make choices or recommendations.

Ultra-trace radiation detection technique sets new global standard for measuring the nearly immeasurable.

A recent edition of the Infrastructure Resilience Research Group Journal featured an article written by PNNL researchers Rob Siefken and Jake Burns about “Design Basis Threat and the Low Threat Environment.”

As a physicist at PNNL, Jon Burnett’s work is about developing instruments to detect ultra-trace radionuclide signatures, analyze samples from around the world to look for evidence of nuclear explosions, and then interpret that information.

Despite the challenges, the talented and dedicated staff at Pacific Northwest National Laboratory kept delivering for the nation.

In recognition of Nuclear Science Week on Oct. 19-23, Pacific Northwest National Laboratory reflects on more than half a century of advancing nuclear science for the nation’s energy, environment, and security frontiers.

PNNL recognizes researchers during National Postdoc Appreciation Week.

Radiation from natural sources in the environment can limit the performance of superconducting quantum bits, known as qubits. The discovery has implications for quantum computing and for the search for dark matter.

Quantum computing experts gather in the Pacific Northwest to discuss challenges and progress.

B3? E4? Remember the board game Battleship? One player suggests a set of coordinates to another, hoping to find the elusive location of an unseen vessel.That is a good place to start in assessing the search for dark matter.

Researchers apply numerical simulations to understand more about a sturdy material and how its basic structure responds to and resists radiation. The outcomes could help guide development of the resilient materials of the future.

Researchers at PNNL are applying deep learning techniques to learn more about neutrinos, part of a worldwide network of researchers trying to understand one of the universe’s most elusive particles.