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.

PNNL brings its expertise to the space domain by joining an organization that facilitates collaboration across the global space industry.

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.

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

Through an unprecedented collaboration with Idaho, Savannah River, and Argonne national laboratories, the Athena Project has built a network of nearly 150 scientists.

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

Researchers have begun a series of experiments that could bring about more energy for grid, thanks to a special delivery of 11 "high burnup" rods.

The Low-cost Earth-abundant Na-ion Storage consortium is a major effort to create superior, no-compromise batteries that replace lithium with inexpensive, domestically abundant sodium and use few—if any—critical materials.

Scientists have tapped AI and powerful computing to speed up how quickly officials are able to learn important details about nuclear events.

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

Patricia Paviet has led 100+ international webinars, building a vital global resource for next-generation nuclear energy education and collaboration.

PNNL researchers received an award from the Materials, Metals & Materials Society (TMS) for a publication on friction riveting.

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.

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.

By combining computational modeling with experimental research, scientists identified a promising composition that reduces the need for a critical material in an alloy that can withstand extreme environments.