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

Researchers from PNNL and Parallel Works, Inc., applied machine learning methods to predict how much oxygen and nutrients are used by microorganisms in river sediments.

Lakes are important sentinels of climate change and contribute significantly to the emissions of the second most important greenhouse gas—methane.

The rate of conversion of cloud droplets to precipitation, known as the autoconversion rate, remains a major source of uncertainty in characterizing aerosol’s cloud lifetime effects and precipitation in global and regional models.

To assess the impact of observation period and gauge location, model parameters were learned on scenarios using different chunks of streamflow observations.

PNNL's E-COMP initiative is helping unleash American energy innovation with advanced theories, models, and software tools to better operate power systems that rely heavily on high-speed power electronic control.

This study presents an automated method to detect and classify open- and closed-cell mesoscale cellular convection (MCC) using long-term ground-based radar observations.

By combining computational modeling with experimental research, scientists identified a promising composition that reduces the need for a critical material in an alloy that can withstand extreme environments.

The Generator Scorecard, developed by PNNL in partnership with BPA, automates generator evaluations, reducing engineering workloads and improving grid reliability.

A new report details the potential for floating offshore wind to lower energy costs and enhance the resilience of the nation’s western grid.

PNNL’s year in review includes highlights ranging from advancing soil science to understanding Earth systems, expanding electricity transmission, detecting fentanyl, and applying artificial intelligence to aid scientific discovery.

PNNL researchers have developed a new, physics-informed machine learning model that accurately predicts how heat accumulates and dissipates during friction stir processing.

EGRASS wins in the software/services category and is awarded a gold medal as special recognition in corporate social responsibility.

The National Transmission Planning Study presents several transmission expansion scenarios that would reliably support the growing demand for energy across the nation.

New transmission lines and renewable energy projects would bring economic and climate change benefits to the Western United States’s electricity grid.

Three PNNL technologies have been named 2024 R&D 100 Award winners.

PNNL computing experts Robert Rallo and Court Corley contribute their knowledge to a recent DOE report on applications of AI to energy, materials, and the power grid.

Department of Energy presented six teams from PNNL with the Secretary's Honor Award.

Senior Advisor Isabella van Rooyen shares her journey from child to internationally recognized scientist in nuclear materials and manufacturing.

ChatGrid™ is a practical application of the Department of Energy’s exascale computing efforts and offers a new experience in easy, intuitive, and interactive data interaction.