A decade-long evolution in making omics data easier to analyze earns widespread recognition.

Researchers showed that spiking neural networks can be accurate for regression problems, opening opportunities for efficient neuromorphic computing.

Tracking water age using a new precipitation tagging method within a hydrologic model generates key information to support hydropower energy and other water-dependent systems.

Following wildfire, the transformation and mobility of phosphorus-containing compounds depend on the type of vegetation burned and the severity of the fire.

Higher temperatures and land-use pressures were associated with weaker chemical separation between river water and sediment dissolved organic matter across 93 sites.

The American Meteorological Society will honor longtime ARM Director Jim Mather with its Cleveland Abbe Award for distinguished service to atmospheric science.

​​Tests show which low-cost 3D method best matches field measurements to minimize errors in mapping headwaters.​

More rain and less snow shorten how long water stays in watersheds.

Researchers build visibility for PNNL through involvement in national workshop reports while helping shape DOE’s future research priorities.

Award-winning research advances AI applications for environmental cleanup.

Genesis Mission awards to PNNL will advance scientific discovery, next-generation energy systems and advanced manufacturing, among others.

PNNL mathematician and AI researcher Henry Kvinge bridges math and AI, finding connections between the two fields and studying ways where they can foster mutual progress.

PNNL has named atmospheric scientist Larry Berg director of the U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) User Facility, where he will oversee a global network of atmospheric research observations.

PNNL researchers are studying ways to use seawater and seawater-generated chemicals to acquire magnesium, lithium, and other critical materials.

Computer scientist Antonino Tumeo is guest editing a special issue of IEEE’s Computer publication focused on democratizing microelectronics.

A research team from Pacific Northwest National Laboratory used machine reasoning to schedule scientific workflows while guaranteeing quality of service (QoS).

Researchers at PNNL have developed an interpretable, lightweight AI model that can easily predict weld microstructure features using only basic machine sensor inputs.

Critical minerals could be sourced from industrial waste at scale thanks to an AI-guided separation workflow developed by researchers at PNNL.

PNNL’s Robert Rallo and Nathan Hodas speak to students about the future of AI.

At the summit, PNNL engaged with partners and investors, highlighting seven projects that accelerate the deployment of new energy technologies.