An approach combining multiple theories increases the accuracy of quantum-based simulations without increasing computational demands.

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.

Seaweed could be a new source of critical minerals for electronics, vehicles, and buildings.

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 developed the first coastal transport model for microplastics to understand how particles move in waterways and coastal currents.

Quantum computing promises leaps in chemistry and materials science, and PNNL is preparing to realize its potential with a collaborative approach.

This research helps predict how coastal forests will respond to future sea level changes which is crucial for protecting these ecosystems.

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

The Coastal Observations, Mechanisms, and Predictions Across Systems and Scales: Field, Measurements, and Experiments project established a network of observational field sites across Chesapeake Bay and western Lake Erie.

Due to their inherent variability and complexity over space and time, scientists are challenged to understand the complex interactions among soil, vegetation, and water along coastal terrestrial-aquatic interfaces.

This study characterized above- and below-ground properties to explore the spatial heterogeneity of the terrestrial aquatic interface ecosystem within the Chesapeake Bay area and evaluate the major drivers of soil respiration.

Neural networks techniques enable the efficient capture of electron correlation effects in reduced-dimensionality spaces.

In the search for rare physics events, extremely pure materials are essential. A partnership between PNNL and Ultramet has led to tungsten with low contamination from other elements.

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

Erik Lentz and Christian Boutan will research quantum information science-enabled discoveries in high energy physics with these awards.

Developing a new scheme to more efficiently solve quantum chemistry problems.

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