This summer, PNNL hosted the inaugural “As Conductive As Copper” (AC2.0) workshop, fostering a collaborative conversation on the future of the U.S. copper supply chain.
PNNL researchers have developed a new, physics-informed machine learning model that accurately predicts how heat accumulates and dissipates during friction stir processing.
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.