PNNL combines AI and cloud computing with damage assessment tool to predict path of wildfires and quickly evaluate the impact of natural disasters, giving first responders an upper hand.

Svitlana Volkova uses natural language processing and AI to reveal how misinformation and disinformation spread through social media.

Machine learning techniques are accelerating the development of stronger alloys for power plants, which will yield efficiency, cost, and decarbonization benefits.

Scientists have created a cybersecurity technology that stops hackers from doing damage by feeding them illusory tidbits of success.

National Nuclear Security Administration Graduate Fellow Marc Wonders has spent the past year working with researchers exploring artificial intelligence in the national security mission space.

A webapp developed by PNNL in collaboration with the University of Washington to help drive efficiencies for urban delivery drivers is now in the prototype stage and ready for testing.

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

For the second straight year, PNNL researchers are featured in a special edition of the Journal of Information Warfare. This issue explores the topic of macro cyber resiliency.

PNNL has published a cybersecurity guidance report for marine renewable energy devices to prepare the blue economy for harnessing ocean power from waves, tides, and currents.

One year ago, Verizon announced a partnership that made PNNL the U.S. Department of Energy’s first national laboratory with Verizon 5G ultra-wideband wireless technology.

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

Sentry-SECURE is a new communication and response platform developed by PNNL, VPI, and Microsoft Azure that rapidly and securely transfers radiological alarm data through the cloud.

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

New approach allows nonexperts to optimize control of a building's energy systems without adding computing power or proprietary software.

PNNL scientists joined international leaders in artificial intelligence research to discuss the latest advances, opportunities, and challenges for neural information processing—the foundation for AI.

PNNL computational biologists, structural biologists, and analytical chemists are using their expertise to safely accelerate the design step of the COVID-19 drug discovery process.

A special issue of the Marine Technology Society Journal, titled “Utilizing Offshore Resources for Renewable Energy Development,” focuses on research and development efforts including those at Pacific Northwest National Laboratory (PNNL).

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.

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