Embedding the known physics of the system to be controlled in the AI model of the system is data efficient and provides safety guarantees.

The National Nuclear Security Administration Graduate Fellowship Program has welcomed aboard the Class of 2020—53 graduate students placed (virtually for now) with offices across the nuclear security enterprise.

Scientists at PNNL have contributed much of the nuclear science that underlies an international monitoring system designed to detect nuclear explosions worldwide. The system detects radioxenon anywhere on the planet.

Like many graduates crossing the finish line in 2020, the National Nuclear Security Administration Graduate Fellowship Program class of 2019-2020 transitioned its closing ceremony to a virtual environment, joined by NNSA and PNNL leaders.

PNNL is managing the Data Archive and Portal, which provides the wind research community with secure, timely, easy, and open access to all data brought in from research under DOE’s Atmosphere to Electrons program.

PNNL quantum algorithm theorist and developer Nathan Wiebe is applying ideas from data science and gaming hacks to quantum computing

Artificial intelligence helps researchers identify metabolites, the small molecules that underlie life.

PNNL and the National Nuclear Security Administration are building future leaders for nuclear security through the NNSA Graduate Fellowship Program, a hands-on fellowship spanning the nuclear security enterprise.

A team of researchers is working to expand our uranium chemistry understanding using a surprising tool: lasers. This capability gives never-before-seen insight into uranium gas-phase oxidation during nuclear explosions.

PNNL researchers and professional staff led discussions ranging from biothreats and climate change to science careers at the 2020 annual meeting of the American Association for the Advancement of Science, held this year in Seattle.

First-of-its-kind network analysis on a supercomputer can speed real-time applications for cybersecurity, transportation, and infectious disease tracking

PNNL's Sensor Fish were deployed at Ice Harbor Dam to collect data from a new turbine. The data indicates the design changes are making travel through the dam less arduous for fish.

PNNL and the 13 other national laboratories of the Grid Modernization Laboratory Consortium (GMLC) will be sharing their R&D work and technologies for grid modernization at DistribuTECH International in San Antonio Jan. 28-30.

The world’s largest scientific society honored Sue B. Clark, a PNNL and WSU chemist, for contributions toward resolving our legacy of radioactive waste, advancing nuclear safeguards, and developing landmark nuclear research capabilities.

PNNL researchers demonstrated a nanoscale analysis tool to map isotopes to location in low-enriched uranium-molybdenum fuel plates for use in nuclear research reactors.

PNNL will lead three new grid modernization projects funded by the Department of Energy. The projects focus on scalability and usability, networked microgrids, and machine learning for a more resilient, flexible and secure power grid.

At a conference featuring the most advanced computing hardware and software, ML in its various guises was on full display and highlighted by Nathan Baker’s featured invited presentation.

Scientists at PNNL are bringing artificial intelligence into the quest to see whether computers can help humans sift through a sea of experimental data.

In today’s digital age, the rabbit hole of connected information can be not only a time sink, but downright overwhelming. Even for high-performance computers.

Twenty-four analysts from U.S. intelligence organizations met in August for a machine learning activity with PNNL researchers Nicole Nichols, Jeremiah Rounds, Lawrence Phillips, and Brian Kritzstein.