Pacific Northwest National Laboratory’s (PNNL) Generative AI (GenAI) investment aims to harness this transformative technology to drive innovation across the science, energy, and security research domains.

GeoBOSS is a software library that combines the data-handling capabilities of Spark and the user-friendliness of Python to simplify geospatial analytics and the transition between small-scale research and large-scale operational projects.

Scientific and technological advancements to safely and permanently store CO2

GODEEEP is an internal PNNL Agile investment inspired that addresses U.S. priorities related to clean energy and environmental and energy equity.

To facilitate the exploration of graphs with up to one million nodes and links, Pacific Northwest National Laboratory developed Green Hornet, a user-friendly graph visualization tool.

The Grid Storage Launchpad (GSL) is a national capability for energy storage research funded by the Department of Energy Office of Electricity and located on the Pacific Northwest National Laboratory (PNNL) campus in Richland, Washington

Pacific Northwest National Laboratory (PNNL) is committed to helping unleash U.S. global dominance in AI by improving the energy performance of the nation’s data centers.

A new set of resources from PNNL helps guide dam owners and operators through response and recovery actions in the wake of cybersecurity or unusual incidents.

The HydroPASSAGE project addresses hydropower challenges, which include efforts to improve environmental performance of hydropower.

HydroWIRES initiative helps water stakeholders reap the full benefit of hydropower’s superpower: versatility

The ICON Science Cooperative will revolutionize the way science is done to dramatically accelerate scientific progress while enhancing equity.

The Ion Dynamics in Radioactive Environments and Materials (IDREAM) Energy Frontier Research Center (EFRC) conducts fundamental science to support innovations in retrieving and processing high-level radioactive waste.

Researchers are bridging hydropower data gaps for unrivaled forecasting precision to support grid resilience.

By improving the Weather Research and Forecasting (WRF)-Solar model, this project aims to reduce forecast errors, improve sub-grid scale variability estimates, and more accurately estimate forecast uncertainty.