PNNL cyber defenders are using the Laboratory’s own artificial intelligence research to recognize and address threats quickly.

David Donnelly, an international compliance analyst at PNNL, recently served on a team of experts that completed the International Atomic Energy Agency’s first International Physical Protection Advisory Service mission in Kenya.

Pacific Northwest National Laboratory scientists envision new research frontiers at the intersection of biology and AI

Scientists are tapping earthquake data to develop a new AI foundation model called SeisModal.

Emilie Purvine, a chief data scientist at PNNL, was elected as a council member at large of the American Mathematical Society.

An award-winning computing method called poly-streaming accelerates the analysis of massive datasets while using limited memory.

This achievement earned DIMPLES a Best Paper award at the ACM International Conference on Supercomputing 2025.

As Laboratory Director Steve Ashby pens his last monthly column, he highlights PNNL’s accomplishments with pride and gratitude.

Peter Heine, senior advisor in the Strategic Threat Analysis group at PNNL, recently travelled to Brussels, Belgium, to support the World Customs Organization's Operation Stingray.

A team of researchers at PNNL has received the 2025 National Nuclear Security Administration CIO Award for developing an innovative solution to enhance secure communications.

Three research teams led by PNNL organizers achieve journal publications following the Mathematics Research Communities program.

PNNL showcased its expertise in AI, machine learning, and related domains at the International Conference on Machine Learning.

Expanding the nation’s largest wind power plant time series database; helping grid planners navigate a resilient energy future with better data.

PNNL scientist James Stegen and an international team of collaborators recently published a comprehensive review of variably inundated ecosystems (VIEs).

PNNL researchers have published their paper, “Introducing Molecular Hypernetworks for Discovery in Multidimensional Metabolomics Data,” in the Journal of Proteome Research.

Researchers can now quantify uncertainty in trained AI models aimed at solving difficult chemistry, biology and materials science problems.

Researchers have created an algorithm that slashes the calculations necessary to prepare and customize data for quantum computing.

Researchers at PNNL are pursuing new approaches to understand, predict and control the phenome—the collection of biological traits within an organism shaped by its genes and interactions with the environment.

Scientists are exploring how web search engine technology might also keep the lights on, the water running and the trains moving.

For PNNL’s Jonathan Evarts, Hope Lackey, and Erik Reinhart, this partnership with WSU opened doors and provided opportunities for their scientific careers to flourish.