Using public data from the entire 1,500-square-mile Los Angeles metropolitan area, PNNL researchers reduced the time needed to create a traffic congestion model by an order of magnitude, from hours to minutes.
PNNL researchers have shown an improved binarized neural network can deliver a low-cost and low-energy computation to help the performance of smart devices and the power grid.
The project received an Innovative and Novel Computational Impact on Theory and Experiment (INCITE) award, a highly competitive U.S. Department of Energy Office of Science program.
Pacific Northwest National Laboratory researchers developed a graphical processing unit (GPU)-centered quantum computer simulator that can be 10 times faster than any other quantum computer simulator.
Researchers at PNNL have developed a bacteria testing system called OmniScreen that combines biological and synthetic chemistry with machine learning to hunt down pathogens before they strike.
PNNL’s new Smart Power Grid Simulator, or Smart-PGSim, combines high-performance computing and artificial intelligence to optimize power grid simulations without sacrificing accuracy.
In a new video series, PNNL is highlighting six scientific and technical experts in the national security domain throughout the fall. Each was promoted to scientist and engineer level 5 earlier this year.
PNNL researchers developed two web-based tools to assess and mitigate cyberthreats to utilities—inside and outside their firewalls. Both are low cost and can be used by control room operators who are not cybersecurity experts.
Infusing data science and artificial intelligence into electron microscopy could advance energy storage, quantum information science, and materials design.
The Facility Cybersecurity toolkit, developed by PNNL, is designed for federal facilities to help implement the presidential executive order on cybersecurity, but it is also available for commercial facilities without charge.
Tracking down nefarious users is just one example of work at PNNL’s Center for Advanced Technology Evaluation, a computing proving ground supported by DOE’s Advanced Scientific Computing Research program.
PNNL researchers established an Internet of Things Common Operating Environment (IoTCOE) laboratory to explore the risks associated with IoT connectivity to the internet, the energy grid and other critical infrastructures.
Pacific Northwest National Laboratory researchers used machine learning to explore the largest water clusters database, identifying—with the most accurate neural network—important information about this life-essential molecule.
A new agreement between Pacific Northwest National Laboratory and The University of Texas at El Paso will create research and internship opportunities.
PNNL researchers used machine learning to develop a tool for a nonprofit to identify orthopedic implants in X-ray images to improve surgical speed and accuracy.