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.

Controlled synthesis and processing allow researchers to test how ions move through energy-relevant materials.

Magazine cover article—“Combating corrosion in the world’s nuclear reactors”—features PNNL research leaders Mark Nutt, Aaron Diaz, and Mychailo Toloczko.

PNNL’s Steven Spurgeon, a materials scientist, was recently elected leader of the Microscopy Society of America (MSA) aberration-corrected microscopy focused interest group.

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 structure-directing agent directly interacts with framework components during material formation.

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.

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.

Materials scientists at PNNL are demonstrating materials with improved properties using solid phase processing.

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, one of PNNL’s most senior research roles.

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.

A PNNL engineering team has designed personal protective equipment in support of COVID-19 aid.

Infusing data science and artificial intelligence into electron microscopy could advance energy storage, quantum information science, and materials design.

PNNL chemist Xin Zhang has rallied a multi-author team to produce a book the American Chemical Society published this fall.

PNNL has three small-scale spectroscopy devices that are speeding up the testing and analysis of candidate novel materials used in energy storage research and environmental remediation.

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.