A breakthrough in electron microscopy based on deep learning can automatically visualize and identify areas of interest, helping to speed advances in materials science.

Beyond melting ice and higher temperatures: On Earth Day, scientists consider how climate change will affect national security.

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

The next-generation ShAPE machine has arrived at PNNL, where it will help prove the mettle of the ShAPE extrusion technique. ShAPE 2 is designed to allow researchers to produce larger, more complex extrusions.

Researchers from PNNL have been assessing installation and use of electric heat pumps in an Alaskan community that relies on fuel oil for heat. The resulting information could advance electrification in cold rural areas across the nation.

PNNL researchers are working to provide the technical assistance and expertise needed for communities to shape their clean energy future.

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.

Study finds regional priorities shape people’s interest in improving the energy efficiency of their homes.

PNNL’s software tool brings together artificial intelligence and satellite imagery to help responders assess damage and take action.

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

PNNL researchers are helping to better define the need for grid energy storage in future clean energy scenarios, as well as working to improve technologies for storing renewable energy so it's available when and where it's needed.

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 explore the feasibility of using low-flying drones to survey sites for low levels of radiation.

From energy-efficient buildings to new manufacturing techniques, researchers at PNNL strive for sustainability and a decarbonization.

Artificial intelligence may help humans and computers work together to monitor materials that could be used to make weapons.

PNNL researchers created rugged, adaptable, mass-manufacturable luminescent particle tracers for use in harsh environments.

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

Scientists at PNNL are working to better prepare authorities, emergency responders, communities and the grid in the face of increasingly extreme hurricanes.

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.