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.

Red teaming for CPS, the process of challenging systems, involves a group of cybersecurity experts to emulate end-to-end cyberattacks following a set of realistic tactics, techniques, and procedures.

Data Scientist Sam Chatterjee to chair security and defense specialty group of the Society for Risk Analysis.

Connected moments math shortcut shaves time and cost of quantum calculations while maintaining accuracy.

Special issue highlights PNNL contributions to NWChem and CP2K, two prominent software packages for computational chemistry.

The Marine and Coastal Research Laboratory (MCRL), part of PNNL, in Sequim, Washington, is the U.S. Department of Energy’s only marine research facility. It has a rich history and expanding research scope.

The workshop brought together 184 scientists to explore research opportunities for nuclear physics in artificial intelligence.

Rigorous mathematical methods can help avoid unnecessary simplifications in numerical models of atmospheric clouds.

More accurate methods provide better results in numerical simulations of atmospheric clouds by avoiding accidental contradictions.

Despite the challenges, the talented and dedicated staff at Pacific Northwest National Laboratory kept delivering for the nation.

Urban landscapes and anthropogenic aerosols have the potential to make gusts stronger, hail larger and even steer storms toward cities.

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.

California and other areas of the U.S. Southwest may see less future winter precipitation than previously projected by climate models, according to new research that corrects for a long-standing model error: the double-ITCZ bias.

Long-term PNNL support has helped enable the continued success of the Chesapeake Large-Scale Analytics Conference.

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.

Researchers developed a new tool that combines artificial intelligence and microscopy data to rapidly test new materials for fusion reactor use.

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