Michael Henry, a senior data scientist at PNNL, has accepted a joint appointment at the Texas A&M University RELLIS Center for Applied Research and Experiential Learning.
PNNL data scientists Henry Kvinge and Ted Fujimoto presented their research on few-shot learning and reinforcement learning, respectively, at workshops during the 2021 AAAI Conference on Artificial Intelligence.
PNNL catalysis experts Oliver Y. Gutierrez and Jamie Holladay, along with a colleague from The City College of New York, led a special issue of the Journal of Applied Electrochemistry.
The partnership to apply artificial intelligence to improve complex systems is part of a U.S. Department of Energy Office of Science $4.2 million, three-year grant.
PNNL scientists joined international leaders in artificial intelligence research to discuss the latest advances, opportunities, and challenges for neural information processing—the foundation for AI.
Red teaming for CPS, the process of challenging systems, involves a group of cybersecurity experts to emulate end-to-end cyberattacks following a set of realistic tactics, techniques, and procedures.
Through two U.S. Department of Energy funding calls awarded in 2020, PNNL is partnering with industry and academia to advance battery materials and processes.
PNNL computational biologists, structural biologists, and analytical chemists are using their expertise to safely accelerate the design step of the COVID-19 drug discovery process.
PNNL led a multi-institutional effort to design a highly active and more durable catalyst made from cobalt, which sets the foundation for fuel cells to power transportation, stationary and backup power, and more.
Scientists have created a single-crystal, nickel-rich cathode that is hardier and more efficient than before—important progress on the road to better lithium-ion batteries for electric vehicles.
Using public data from the entire 1,500-square-mile Los Angeles metropolitan area, PNNL researchers reduced the time needed to create a traffic congestion model by an order of magnitude, from hours to minutes.
PNNL researchers have shown an improved binarized neural network can deliver a low-cost and low-energy computation to help the performance of smart devices and the power grid.