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.

PNNL led a multi-institutional effort to design a highly active and more durable catalyst made from cobalt, which sets the foundation for fuel cells to power transportation, stationary and backup power, and more.

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.

Lu honored for "elucidating design principles of artificial metalloproteins to gain novel and deeper insights into the structure and function of natural systems."

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.

A new report outlines future research paths that are needed for airlines to reduce carbon emissions and notes that the only way to achieve emission reduction goals is with Sustainable Aviation Fuels.

A nanosized metal catalyst directly bonded to a metal-organic framework is active in converting carbon dioxide to methanol.

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.

PNNL bioenergy expert Dan Gaspar has been selected to assume leadership of DOE’s Co-Optimization of Fuels and Engines initiative.

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 scientists have developed a catalyst that converts ethanol into C5+ ketones that can serve as the building blocks for everything from solvents to jet fuel.

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.