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.

PermitAI’s newly released dataset, NEPATEC 2.0, captures and curates environmental review data for faster, better federal permitting.

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.

PNNL researchers continue to deliver high-quality, high-impact research on radioactive waste and nuclear materials management, earning “Papers of Note” and “Superior Paper” awards.

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.

New electees represent research in genomics, grid resilience, coastal ecology, and more.

PNNL researchers showcased subsurface sampling and remediation expertise at the 2025 Federal Remediation Technologies Roundtable.

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 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.

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.

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.