Artificial intelligence helps analyze images and reduce uncertainty for a key measure of watershed function.

Dynamic organic matter traits tied to reactions can surpass static descriptors in explaining decomposition rates and responses to environmental change.

Using an AI approach to combine outputs from microbial models and apply them as inputs to reactive-transport models reduces computational time by several orders of magnitude.

PNNL experts trained Armenian engineers in ultrasonic testing, advancing local expertise for nuclear power plant inspections.

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.

PNNL scientists were among the specialists keeping the crowds safe at the recent FIFA World Cup competition.

​​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.

New partnership agreement to focus on AI, robotics, and advanced energy systems.

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

A new report highlights a public workshop hosted by the National Academies Forum on Microbial Threats and features PNNL's Lauren Charles and the AI-Driven One Health Security program.

PNNL researchers highlighted advances in AI for scientific discovery at the AI+ Expo, showcasing generative AI, autonomous research workflows, and scalable AI systems supporting energy, security, and national competitiveness.

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

At a geospatial intelligence conference, senior data scientist Adam Attarian participated in a panel discussing how national laboratories are delivering science, technology, and partnerships to enhance modeling, analysis, and innovation.

David Wunschel, a chemist and Laboratory Fellow, participated on a panel focused on how emerging technologies are transforming CBRN defense, detection, and response across military and government operations.

PNNL has developed a next-generation electrical resistivity tomography system for DOE that uses E4D software and AI-enhanced modeling to produce real-time subsurface images that help guide environmental remediation decisions.

PNNL celebrates its sixth Handshake Early Talent Award, showcasing strong early‑career recruiting. Stories from data scientist Joshua Chong and engineer Alex Smith highlight journeys from different career paths to PNNL.