Hybrid land use model incorporates key factors to understanding agriculture’s potential response to climate change.

PNNL data scientists Ján Drgoňa and Draguna Vrabie are working to reduce deployment costs of advanced energy control systems for buildings.

Differences in the rainfall intensity of mesoscale convective systems and other types of warm—season rainfall in the central United States lead to differences in their impacts over land.

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.

Atmospheric rivers that make landfall on the U.S. West Coast show broad increases in size and intensity with warmer local sea surfaces.

Floodplain inundation simulations shed light on flood generation mechanisms and the increasing role of extreme rainfall.

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.

Information extracted from a variety of atmospheric observations can correct the behavior of a model and improve simulated clouds.

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.

Aerosol treatments in E3SM affect clouds, heat reflected to space, and Earth’s snow-or-ice-covered surface.

Researchers created an open database for soil-atmosphere greenhouse gas flux data.

PNNL researchers used the Global Change Analysis Model (GCAM) to explore 15 different global scenarios that consisted of combinations of five different socioeconomic futures and four different climatic futures.

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

New-particle formation driven by natural biogenic emissions produces a large number of small particles in the Amazon free troposphere.

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.

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.

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.

A team of researchers led by scientists from PNNL simulated carbon cycling and community composition during 100 years of forest regrowth following disturbance.