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.

A Cr(III) coating impacts how well nanomaterials can dissolve in other substances, which can have both desired and undesired consequences.

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.

Researchers at Pacific Northwest National Laboratory (PNNL) are closer to understanding how iron may pave the way for sequestration of technetium-99 contaminants in the subsurface.

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.

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.

Like a toxic Trojan horse, microplastics can act as hot pockets of contaminant transport. But, can microplastics get into plant cells? Recent research shows that they can't.

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

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 devised a new method to probe the atomic structure of plutonium-containing microcrystals using laboratory-based equipment.

In recognition of Nuclear Science Week on Oct. 19-23, Pacific Northwest National Laboratory reflects on more than half a century of advancing nuclear science for the nation’s energy, environment, and security frontiers.

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.

Geophysical modeling and inversion software meets DOE and nuclear industry standards for use in decision-making for radiologically contaminated sites.

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.