Three research teams led by PNNL organizers achieve journal publications following the Mathematics Research Communities program.

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

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

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

Researchers at PNNL advised elementary and middle school student teams with their problem-solving research for the FIRST® LEGO® League robotics competitions.

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.

Pacific Northwest National Laboratory launches the Training Outreach and Recruitment for Cybersecurity Hydropower program at the University of Texas at El Paso.

Researchers created a methodology to design custom network topologies that improve the execution time for scientific applications.

PNNL signs memorandum of understanding with Oregon State University to support marine science and technology advancements.

A new tool helps identify non-powered dams across the country that are top candidates for conversion to power-generating hydroelectric facilities.

The report explores how research in planning, data, policy, and technology can address some of the underlying challenges facing water power systems.

A team of researchers at PNNL has created a publicly available Hydropower eLibrary to improve access to information that could help streamline the FERC environmental review and licensing process.

Scientists screen for nanobodies that recognize wild type and mutant functional proteins to develop a framework to disrupt protein interactions that can cause disease.

PNNL convenes ocean leaders to tackle climate challenges and support the Ocean Climate Action Plan.

Researchers unveil user-friendly software to keep complex projects on budget and on schedule.

PNNL researchers present challenges and opportunities for fusing graph neural networks with deep reinforcement learning.

PNNL researchers outline open problems in different scientific areas in the Journal of Combinatorics.

Methods review suggests ways to use artificial intelligence and machine learning to handle missing data when integrating two or more omics approaches.

To thwart pathogens, researchers in the epidemiology field of infectious disease (ID) prediction are continuously trying to forecast when, where, and how an ID event will occur.