PNNL shared recent developments in artificial intelligence at the inaugural Special Competitive Studies Project AI for National Competitiveness Expo.

Research at PNNL and the University of Texas at El Paso are addressing computational challenges of thinking beyond the list and developing bioagent-agnostic signatures to assess threats.

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.

Americans produce enough waste every year to replace up to a quarter of the country’s jet fuel with sustainable alternatives, new research shows.

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.

The SHASTA program is doing a deep dive on subsurface hydrogen storage in underground caverns, helping to lay the foundation for a robust hydrogen economy.

Researchers at PNNL have developed a new technique to get a high-resolution look at how—and why—corrosion happens.

In 2006, battery research was practically non-existent at PNNL. Today, the lab is lauded for its battery research. How did PNNL go from a new player to a leader in state-of-the-art storage for EVs and the grid?

Post-consumer recycled aluminum to be transformed into high strength building materials and consumer goods with patented ShAPE™ manufacturing process.

The convergence of artificial intelligence, cloud, and high-performance computing to accelerate scientific discovery is the focus of a multi-year collaboration between Microsoft and PNNL.

Now in its twentieth year, the Hydrogen Safety Panel is led by PNNL and includes more than two dozen experts. These experts developed a trusted resource for best practices for hydrogen energy.

An initiative from Washington State University and Snohomish County leaders is aiming to make Paine Field a nexus for testing and improving sustainable aviation fuels made from non-petroleum materials.

A newly developed, highly conductive copper wire could find applications in the electric grid, as well as in homes and businesses. The finding defies what's been thought about how metals conduct electricity.

PNNL researchers comment on the “Adversarial Machine Learning” white paper from the National Institute of Standards and Technology.

PNNL has created the Center for AI @PNNL to coordinate the pioneering research of hundreds of scientists working on a range of projects in artificial intelligence.

A seemingly simple shift in lithium-ion battery manufacturing could pay big dividends, improving electric vehicles’ ability to store more energy per charge and to withstand more charging cycles.

PNNL is one of the founding partners of the Trillion Parameter Consortium, which was formed to create reliable generative AI models.

Input from humans helps when deciding whether to trust the recommendations or decisions of a machine-learning system.

PNNL’s DataLife tool analyzes and identifies bottlenecks in scientific workflows.

The use of disciplines in pure mathematics can increase the reliability and explainability of machine learning models that “transcend human intuition,” according to PNNL scientists.