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.
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?
Battery energy storage systems are being proposed in municipalities across the U.S. PNNL researchers can help community planners guide safe siting and operations.
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 battery researcher Jie Xiao collaborates with academic and industry partners to address scientific challenges in manufacturing lithium-based batteries.
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.
Top scientists and officials from government, academia, Alaskan Native communities, and industry are heading to Alaska to focus on driving energy technologies for a more sustainable Arctic region.
PNNL scientists partnered with colleagues at the University of Akron to create a new molecule that could substantially improve the electrochemical stability of redox flow batteries.
The Energy Storage for Social Equity Initiative will help up to 15 disadvantaged communities consider energy storage technologies to meet local energy goals.
PNNL has developed seaweed-based inks and materials for 2-D and 3-D printing that can be used for a multitude of applications in the art, medical, STEM, and other fields.
