New funding spurs a new approach to researching the effective retrieval and processing of legacy radioactive waste. Four-year focus of the IDREAM EFRC will link attosecond timescales to decades-long chemical processes.

Researchers are planning for an electric grid that deploys machine learning to think ahead, plan for the worst, anticipate demand, and meet consumer needs safely and securely.

James Koch, a data scientist, developed a technique that enables high-fidelity remote sensing with limited data.

As new paradigms in advanced computing take shape, computational chemistry researchers are finding new ways to solve challenging chemistry problems.

A new AI model developed at PNNL can identify patterns in electron microscope images of materials without requiring human intervention, allowing for more accurate and consistent materials science.

PNNL chemical engineer Reid Peterson helped develop the process to pretreat Hanford Site tank waste by removing cesium-137.

DOE Basic Energy Sciences 2023 “Poetry of Science Art Contest” aims to educate and inspire; voting for the People’s Choice Award closes September 15.

The Rapid Assessment of Platform Technologies to Expedite Response project aims to prepare against future pandemics.

To overcome high-performance computing bottlenecks, a research team at PNNL proposed using graph theory, a mathematical field that explores relationships and connections between a number, or cluster, of points in a space.

PNNL researchers use machine learning and data analytics to assist with detection of nuclear proliferation and nuclear material trafficking.

Scientists are pioneering approaches in the branch of artificial intelligence known as machine learning to design and train computer software programs that guide the development of new manufacturing processes.

In honor of National Intern Day, PNNL and intern Jonathan Mills were named among the Top 100 in the nation by early-career recruiting firm WayUp.

As wildfires grow increasingly severe, PNNL researchers are working to slow their spread and mitigate their harmful effects.

TransMED analyzes patient data from similar diseases across multiple sources to understand COVID-19 patient outcome risk factors.

This PNNL-developed separation system quickly and successfully separates larger particles from smaller ones at various scales, in different solid-liquid mixtures and at different flow rates.

To support federal energy agencies in meeting renewed environmental policies, PNNL is identifying the mechanisms and practices that could enhance agencies’ existing environmental justice programs, policies, and activities.

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.

Focused research studies of advanced algorithms and materials are setting the stage for the next generation of quantum computers.

The newly created ICON Science Cooperative is a resource enabling an innovative approach to science to generate transferable knowledge and increase equity.