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.

PNNL data scientists Ján Drgoňa and Draguna Vrabie are working to reduce deployment costs of advanced energy control systems for buildings.

PNNL and Microsoft Quantum partner to link quantum circuits to powerful government supercomputers.

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.

The MIT-sponsored competition encourages community approaches to developing new solutions for analyzing graphs and sparse data; PNNL has placed a winner in each year.

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.

James A. Ang, a PNNL computing expert, was recently invited to moderate a panel in a virtual workshop focused on federally funded research and development on software for heterogeneous computing.

Today’s critical infrastructure systems are increasingly vulnerable to attacks that could cripple civil society, according to cybersecurity experts.

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.

Lauren Charles, a PNNL senior data scientist, has joined the editorial board of Pathogens. Pathogens is an international peer-reviewed open access journal of pathogens and pathogen-host interaction.

Predictive models indicate cells regulate enzyme activity to maintain liquid center.

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.

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.

The American Society for Quality (ASQ) has recognized Laboratory Fellow and Pacific Northwest National Laboratory (PNNL) Statistician Greg Piepel with the William G. Hunter Award.

PNNL team has developed and implemented a generalizable computational framework to study the resilience of the multilayered London Rail Network to the compound threat of intense flooding and a targeted cyberattack.