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

Mystery of how important aluminum minerals form and dissolve solved with materials science and advanced imaging studies.

Nanosize platelets of an aluminum material slide and join in a staggered orientation to form larger crystals.

Early career researchers recognized with Team Science Award by the Department of Energy for presentation highlighting the collaborative science performed by IDREAM.

IDREAM researchers uncovered how dimeric complexes control how aluminum hydroxide minerals dissolve.

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.

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.

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.

From vehicles and airplanes to solid-phase processing of metals—how Curt Lavender and his team at PNNL solve industry problems with practical ingenuity.

The Department of Energy Art of Science contest entry focuses on the extraordinary universe of chemical bond making and breaking under irradiation.

Trace amounts of iron and chromium can incorporate into gibbsite, affecting how the mineral dissolves.

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

Experiments focus on catalysts for off-road vehicles that optimize the use of critical minerals while lasting longer and costing less.

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

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

Increasing concentrations of ions slows the rate of particle aggregation in boehmite suspensions.

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