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

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.

Directionally biased electrostatic interactions are the key to significantly tuning the reduction potential of iron/sulfur clusters.

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

This project sought to assure that research activities centered around different sampling and monitoring efforts in northwest Ohio would not disturb any historical cultural resources.

Pacific Northwest National Laboratory researchers developed a new theoretical method for studying highly correlated systems.

Nineteen PNNL-affiliated researchers have been named to Clarivate’s 2024 list of the world's most highly cited researchers.

In a recent publication in Nature Communications, a team of researchers presents a mathematical theory to address the challenge of barren plateaus in quantum machine learning.

Early life exposure to polycyclic aromatic hydrocarbons (PAHs), found in smoke, has been linked to developmental problems. To study the impacts of these pollutants, PAH metabolism in infants and adults were compared.

PMedIC, the PNNL and OHSU joint research collaboration, contributes to student success and impact in biomedicine.

A new approach validates quantum algorithms for low-energy nuclear physics applications

A new quantum algorithm speeds up simulations of coupled oscillator dynamics.

Researchers used a combination of sophisticated laboratory incubations and field measurements to determine the role of microbial production and consumption of methane in soils with different exposure to tidal inundation

In a new approach, quantum flow algorithms are used to simulate many-body systems on quantum computers and effectively describe chemical processes.

Researchers devised a quantitative and predictive understanding of the cloud chemistry of biomass-burning organic gases helping increase the understanding of wildfires.

Reproducible workflows for binning and visualizing nuclear magnetic resonance spectra from environmental samples.

PNNL’s Climate Network with MSIs and HBCUs aims to bring opportunities to diverse populations.

Spatial proteomics enables researchers to link protein measurements to features in the image of a tissue sample, which are lost using standard approaches.

A streamlined informatics workflow that automatically processes complex data and provides the locations of double bonds in lipids.