Connecting energy generation, electricity storage, and using sensors and control software to track load, including precommercial market marine energy generation technologies.

PNNL researchers Daniel Deng and Yehia Ibrahim join the 2025 class of National Academy of Inventors Fellows.

This achievement earned DIMPLES a Best Paper award at the ACM International Conference on Supercomputing 2025.

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

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.

The first tidal turbine deployed in the Pacific Northwest at PNNL-Sequim showcases the Lab’s growing role as a regional center for marine energy research.

The 2024 State of the Science Report on environmental effects of marine renewable energy research has been released.

A new mini-acoustic transmitter and tagging procedure is able to provide data on small, sensitive fish species like the American shad.

New methodological approach demonstrates how to assess the economic value, including non-traditional value streams, of converting non-powered dams to hydroelectric facilities.

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.

Developed with community input, the new Hydropower Vision Roadmap lays out a path to achieve a shared vison for modern hydropower by 2050.

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.

PNNL researchers teamed up with marine energy developers to test a new wave energy conversion device through the TEAMER program.

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.