PNNL recently joined the Department of Homeland Security for two technical meetings exploring national security research spanning the threat realm, from chemical and biological attacks to adversarial artificial intelligence.
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.
Three PNNL authored papers were accepted as posters to the ICLR 2023 Workshop on Physics for Machine Learning and Workshop on Mathematical and Empirical Understanding of Foundation Models.
A combined experimental and theoretical study identified multiple interactions that affect the performance of redox-active metal oxides for potential electrochemical separation and quantum computing applications.
