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Filtered by Catalysis, Distribution, Grid Analytics, Grid Energy Storage, Nuclear Nonproliferation, Transmission, Visual Analytics, and Weapons of Mass Effect
SEPTEMBER 17, 2020
Web Feature

Not Your Average Refinery

In a new review, PNNL researchers outline how to convert stranded biomass to sustainable fuel using electrochemical reduction reactions in mini-refineries powered by renewable energy.

Secretary of Energy Advisory Board (SEAB) Report Recognizes PNNL Contributions

ML and AI

Report features how PNNL’s computing capabilities are affecting the nation’s security, science, and energy missions

August 25, 2020
August 25, 2020
Highlight

Contributions from researchers across Pacific Northwest National Laboratory (PNNL) were recognized in the preliminary findings of a Secretary of Energy Advisory Board (SEAB) report from a working group dedicated to the U.S. Department of Energy’s (DOE’s) capabilities and future in artificial intelligence (AI) and machine learning. PNNL researchers’ expertise is prominent throughout DOE’s AI efforts, particularly in the areas of data sciences and national security.

Based largely on input from DOE sponsors, the report features how PNNL’s computing capabilities are affecting the nation’s security, science, and energy missions. Key highlights include:

  • Studying how AI affects the global landscape for securing nuclear materials, potentially using deep learning to enhance physical and digital protections against material concealment, delivery, theft, and sabotage.
  • Describing how the United States and its partners might employ deep learning to combat attack efforts for enhanced nuclear security.
  • Designing advanced deep learning models to characterize operations with buildings, using electrical signatures on power lines, enabling new designs for energy-efficient buildings in addition to enhanced security features for nuclear facilities.
  • Leading the nuclear explosive monitoring project with data scientists working to significantly lower detection thresholds of low-yield, evasive underground nuclear explosions without increasing time-to-detection or the amount of human analysis.
  • Co-design of advanced accelerator, memory and data movement concepts to support convergence of AI and machine learning methods with other forms of data analytics and traditional scientific high performance computing (HPC). 

The report highlights PNNL’s support to the National Nuclear Security Administration, featuring joint laboratory collaborations between PNNL and others, including the Y-12 National Security Complex, Sandia National Laboratories, Lawrence Livermore National Laboratory, Los Alamos National Laboratory, and Oak Ridge National Laboratory. Additionally, PNNL is working as part of DOE’s comparative advantages in AI, providing the Office of Energy Efficiency and Renewable Energy access to AI subject matter experts.

View full preliminary findings of the Secretary of Energy Advisory Board (SEAB) report.

For more information about PNNL’s research contributions, contact Aaron Luttman

JULY 21, 2020
Web Feature

A Remarkable Rate of Return with Catalytic Bias

A multi-institution research team found how the protein environment surrounding some enzymes can alter the direction of a cellular reaction, as well as its rate—up to six orders of magnitude—in a phenomenon referred to as catalytic bias.
JUNE 2, 2020
Web Feature

Virtual Ceremony Honors Nuclear Security Graduates

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.
MARCH 16, 2020
Web Feature

Carving Out Quantum Space

The race toward the first practical quantum computer is in full stride. Scientists at PNNL are bridging the gap between today’s fastest computers and tomorrow’s even faster quantum computers.