Sue Southard's one thousand dives as a PNNL staff member leave a ripple effect on efforts to keep our ocean healthy, our economy thriving, and our waters safe.

Parallel graph neural networks identify protein-ligand 3-D structural interactions to aid in protein function prediction and drug discovery.

Brian O’Neill joins a Roundtable on climate security organized by the National Academies of Sciences, Engineering, and Medicine.

An analysis of land use in watersheds that supply drinking water to over a hundred United States cities identified a wide range of exposure to potential contamination.

PNNL experts perform fundamental climate science work at multiple scales to understand impacts on energy, water, food, and infrastructure systems.

PNNL researchers have uncovered a plant-derived process that leads to the formation of aerosol particles over the Amazon rainforest and potentially other forested parts of the world. 

New work developed a methodology to generate building temperature setpoint schedules for use by modelers in large-scale building energy simulations.

COVID-19 changed when and where we use electricity. Researchers showed that commonly available total demand data masked the actual changes.

Storage Targets and Release Function Inference Tool uses data to infer the operating policies of large reservoirs on a national scale.

A multisectoral analysis that combines land use data with hydrological simulations reveals wide disparities in potential source water contamination.

Anika Halappanavar’s research into COVID-19 misinformation earned her recognition by the Washington State Academy of Sciences as one of the state’s top high school researchers.

PNNL scientist Brian O’Neill led the compilation of an IPCC chapter about climate-related risks.

COVID-19 had multiple impacts on the electric sector and the nature of those impacts varied on the scale analyzed.

To improve the study of human-Earth interactions, a 10-year vision report by the MultiSector Dynamics community of practice encourages the use of emerging human systems datasets, embedded intelligence in modeling, and workforce diversity.

Researchers have been investigating if machine learning techniques could be used to help create novel beers.

Using a novel settlement-related dataset produced more accurate population downscaling than using other, more traditional datasets.

Despite an increase in future electricity demands, virtual water trading in the U.S. electricity sector is expected to decline as renewable energy expands.

PNNL is highlighting scientific and technical experts in the national security domain who were recently promoted to scientist and engineer Level 5, one of PNNL’s most senior research roles.

PNNL data scientists are refining their artificial intelligence tools with COVID-19 data to advance therapeutics and treatments for a future pandemic.

Hypergraphs can more faithfully identify important genes in complex immune responses.